commit 7dd17df0cdee78c261271ad2340c9420397f3538
Author: manusdlai <manusdlai@gmail.com>
Date: Wed Feb 12 17:38:06 2025 +0530
first commit
diff --git a/.env.example b/.env.example
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+++ b/.env.example
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+# Agent X/Twitter account credentials
+X_USERNAME =
+X_PASSWORD =
+X_PHONE_OR_USERNAME =
+
+# X
+X_API_OFFICIAL=False # set to true to use the official X API
+X_ENABLED=False # set to true to post to x
+
+# Agents
+# set to STOP: to stop scheduler when all posts are posted
+# set to AUTO: to auto generate new content when all posts are posted
+# set to LOOP: to loop through all posts when at the end
+GENERATE_POSTS=STOP
+
+# Access token for framework features
+TWITTER_ACCESS_TOKEN=
+TWITTER_ACCESS_TOKEN_SECRET=
+
+# Twitter API Key - Read and write API resources
+TWITTER_API_KEY=
+TWITTER_API_KEY_SECRET=
+TWITTER_BEARER_TOKEN=
+
+TWITTER_CLIENT_ID=
+TWITTER_CLIENT_SECRET=
+
+# Client ID and Secret for Twitter API
+CLIENT_ID=
+CLIENT_SECRET=
+
+# Github
+GITHUB_NAME=
+GITHUB_PAT=
+GITHUB_TOKEN=
+
+# OpenAI API Key - Read and write API resources
+OPENAI_NAME=
+OPENAI_API_KEY=
+
+# Google Gemini API Key - Read and write API resources
+GOOGLE_GEMINI_API_KEY=
+
+# Leonardo AI API Key - Read and write API resources
+LEONARDO_API_KEY=
+
+# Telegram Bot Token - Read and write API resources
+TELEGRAM_NAME=
+TELEGRAM_USERNAME=
+TELEGRAM_BOT_TOKEN=
+TELEGRAM_BOT_LINK=
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
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--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,42 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+ENV/
+
+# IDEs and editors
+.idea/
+.vscode/
+*.swp
+*.swo
+.DS_Store
+
+# API Keys
+.env
+dont_share/
+
+#working files
+to_delete/
+
+**state.json
+.venv
diff --git a/CODE_OF_CONDUCT b/CODE_OF_CONDUCT
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--- /dev/null
+++ b/CODE_OF_CONDUCT
@@ -0,0 +1,140 @@
+# Code of Conduct & Contribution Guidelines
+
+Welcome to our project! We expect all contributors to abide by our **Code of Conduct** (outlined below) and adhere to our **Project Contribution Standards**, which specify how to document code, attribute authors, and write Git commit messages.
+
+## 1. Our Pledge
+
+In the interest of fostering an open and welcoming environment, we, as contributors and maintainers, pledge to make participation in our project and our community a harassment-free experience for everyone—regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
+
+## 2. Expected Behavior
+
+- Be kind and courteous to others.
+- Respect differing viewpoints and experiences.
+- Gracefully accept constructive criticism.
+- Give and receive feedback in a positive manner.
+- Collaborate and help each other whenever possible.
+
+## 3. Unacceptable Behavior
+
+- The use of sexualized language or imagery and unwelcome sexual attention.
+- Trolling, insulting or derogatory comments, and personal or political attacks.
+- Public or private harassment.
+- Publishing others’ private information (e.g., physical or electronic addresses) without explicit permission.
+- Other conduct which could reasonably be considered inappropriate.
+
+## 4. Reporting and Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at `[YOUR CONTACT EMAIL HERE]`. The project team will review and investigate all complaints and respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident.
+
+---
+
+# Project Contribution Standards
+
+In addition to general conduct, our project enforces **coding style**, **docstring conventions**, **author attribution**, and **commit message guidelines** to ensure clarity and consistency.
+
+## 5. Docstring Style
+
+We follow the **Google Style Python Docstrings** as documented here:
+[https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html)
+
+Please ensure all public functions, classes, and modules include docstrings adhering to this style.
+
+### 5.1 Module-Level Docstring Format
+
+When creating a **new module**, please include a docstring at the top with the following structure (update the content as appropriate):
+
+```python
+"""
+Module: twitter_connector
+=========================
+
+This module implements the TwitterConnector class for interacting with Twitter APIs.
+
+Title: Twitter Connector
+Summary: Twitter connector implementation.
+Authors:
+ - @TheBlockRhino
+Date: 2024-12-31
+Last Updated: 2024-12-31
+URLs:
+ - https://arai-ai.io
+ - https://github.com/ARAIDevHub/arai-ai-agents
+ - https://x.com/TheBlockRhino
+"""
+```
+
+When **modifying an existing module**, add your name to the list of authors, and **update the last updated date**. For example:
+
+```python
+#
+# Module: twitter_app_auth
+#
+# This module implements the TwitterAppAuth class for authenticating with the Twitter API using OAuth 1.0a.
+#
+# Title: Twitter App Auth
+# Summary: Twitter app authentication implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2024-12-31
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-04
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+```
+
+Please make sure the rest of the comments in the file are also updated to match your changes, and follow the **Google Style** guidelines for any function/class docstrings.
+
+---
+
+## 6. Git Commit Messages
+
+We encourage **descriptive, consistent commit messages**. Use semantic versioning and tagging where appropriate. For guidelines, see:
+[https://www.gitkraken.com/gitkon/semantic-versioning-git-tags](https://www.gitkraken.com/gitkon/semantic-versioning-git-tags)
+
+### 6.1 Examples
+
+- **feat:** Introduce a new feature (`feat: add user login flow`)
+- **fix:** Bug fix or patch (`fix: handle null pointer in user data`)
+- **docs:** Documentation-only changes (`docs: update readme installation steps`)
+- **style:** Changes that do not affect meaning (white-space, formatting, etc.)
+- **refactor:** Code change that neither fixes a bug nor adds a feature
+- **perf:** Code change that improves performance
+- **test:** Add or correct tests
+
+**Example** commit message:
+
+```
+feat: add advanced prompt chaining for TwitterConnector
+
+- Created new step_4.py for multi-step Twitter workflow
+- Updated docstring for step_3.py with new Authors entry
+- Bumped version from 1.2.0 to 1.3.0
+```
+
+---
+
+## 7. Adding Tags & Versions
+
+For **major changes** or new releases, we recommend creating a new Git tag using semantic versioning (e.g., `v2.0.0`). Use tags to mark milestones or significant updates, so others can easily reference or roll back to stable states.
+
+---
+
+## 8. Scope
+
+This Code of Conduct and Contribution Standards apply to:
+- All repositories under the `[YOUR_ORG/PROJECT]` umbrella.
+- Communication channels such as GitHub issues, pull requests, and social media references.
+
+## 9. Attribution
+
+This Code of Conduct is based on the [Contributor Covenant](https://www.contributor-covenant.org), with custom additions to guide documentation practices and commit messages in our project.
+
+---
+
+**Thank you for helping us maintain a welcoming, organized, and productive environment!**
+
+If you have any questions or concerns about this Code of Conduct or the Contribution Standards, please contact `[YOUR CONTACT EMAIL HERE]`.
+
+*Happy coding and contributing!*
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diff --git a/HOW_TO_CONTRIBUTE b/HOW_TO_CONTRIBUTE
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+# How to Contribute
+
+Welcome to the **ARAI AI Agents** project! We appreciate your interest in contributing. This guide outlines our contribution process, coding conventions, and best practices to ensure a smooth and collaborative experience.
+
+## Table of Contents
+
+1. [Code of Conduct](#code-of-conduct)
+2. [Getting Started](#getting-started)
+3. [Branching & Workflow](#branching--workflow)
+4. [Style & Documentation](#style--documentation)
+ - [Docstring Guidelines](#docstring-guidelines)
+ - [Author Attribution](#author-attribution)
+5. [Commit Messages & Tagging](#commit-messages--tagging)
+6. [Testing Your Changes](#testing-your-changes)
+7. [Pull Requests](#pull-requests)
+8. [Need Help?](#need-help)
+
+---
+
+## 1. Code of Conduct
+
+Please review our [Code of Conduct](./CODE_OF_CONDUCT.md) before contributing. By participating, you agree to uphold a respectful and inclusive environment for everyone.
+
+---
+
+## 2. Getting Started
+
+**1. Fork & Clone**
+- Fork this repository using the **Fork** button on GitHub.
+- Clone your fork locally, for example:
+ ```bash
+ git clone https://github.com/<your-username>/arai_ai_agents.git
+ ```
+- Set up your remote so you can pull changes from the official repo later:
+ ```bash
+ git remote add upstream https://github.com/arai-ai/arai_ai_agents.git
+ ```
+
+**2. Create a Virtual Environment**
+We recommend using [conda](https://docs.conda.io/en/latest/) or [venv](https://docs.python.org/3/library/venv.html):
+```bash
+conda create --name arai_ai_agents python=3.11
+conda activate arai_ai_agents
+```
+
+**3. Install Dependencies**
+```bash
+pip install -r requirements.txt
+```
+
+**4. You’re All Set!**
+Now you can explore the codebase, run `main.py`, or execute tests to ensure everything is working.
+
+---
+
+## 3. Branching & Workflow
+
+1. **Create a Branch**
+ - Use a descriptive name for your branch, for example:
+ - `feat/new-twitter-connector`
+ - `fix/spelling-typos`
+ - `docs/improve-readme`
+ ```bash
+ git checkout -b feat/new-twitter-connector
+ ```
+2. **Make Changes & Commit**
+ - Keep commits small and focused.
+ - Write clear commit messages (see [Commit Messages & Tagging](#commit-messages--tagging)).
+
+3. **Pull & Rebase** (before pushing)
+ - Keep your branch up-to-date with the main branch:
+ ```bash
+ git checkout main
+ git pull upstream main
+ git checkout feat/new-twitter-connector
+ git rebase main
+ ```
+4. **Push Your Branch**
+ ```bash
+ git push origin feat/new-twitter-connector
+ ```
+
+---
+
+## 4. Style & Documentation
+
+### Docstring Guidelines
+
+We follow **Google Style Python Docstrings**:
+[https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html)
+
+- **Module Docstrings**
+ At the top of each new or updated module, include something like:
+ ```python
+ """
+ Module: twitter_connector
+ =========================
+
+ This module implements the TwitterConnector class for interacting with Twitter APIs.
+
+ Title: Twitter Connector
+ Summary: Twitter connector implementation.
+ Authors:
+ - @TheBlockRhino
+ Date: 2024-12-31
+ Last Updated: 2024-12-31
+ URLs:
+ - https://arai-ai.io
+ - https://github.com/ARAIDevHub/arai-ai-agents
+ - https://x.com/TheBlockRhino
+ """
+ ```
+ - If you **modify** an existing file, add your name to the **Authors** list and update the **Last Updated** field.
+
+- **Function & Class Docstrings**
+ Use the Google-style format for parameters, returns, exceptions, etc. Example:
+ ```python
+ def my_function(param1: str, param2: int) -> bool:
+ """
+ Perform an example operation and return a boolean.
+
+ Args:
+ param1 (str): Description of param1.
+ param2 (int): Description of param2.
+
+ Returns:
+ bool: Explanation of the return value.
+ """
+ ```
+
+### Author Attribution
+
+Whenever you add a **substantial** piece of work (new module, major refactor), be sure to:
+- Add your handle to the module docstring’s **Authors** list.
+- Keep track of changes in the **Last Updated** date.
+
+---
+
+## 5. Commit Messages & Tagging
+
+We recommend [semantic versioning](https://www.gitkraken.com/gitkon/semantic-versioning-git-tags) and **descriptive commit messages**.
+
+- **Prefix** your commits with a type, for example:
+ - `feat:` when you add a new feature
+ - `fix:` when you fix a bug
+ - `docs:` for documentation updates
+ - `test:` for test-related changes
+ - `refactor:` for code improvements without changing functionality
+ - `perf:` for performance improvements
+ - `chore:` for minor tasks like updating `.gitignore`
+
+**Example**:
+```
+feat: add advanced prompt chaining for TwitterConnector
+
+- Created prompt_chain_v2.py
+- Updated docstrings in twitter_connector.py
+- Bumped version from 1.2.0 to 1.3.0
+```
+
+Use **Git Tags** (`git tag v1.3.0`) for:
+- Major releases
+- Milestones
+
+---
+
+## 6. Testing Your Changes
+
+1. **Run Existing Tests**
+ ```bash
+ pytest
+ ```
+ or
+ ```bash
+ python -m unittest discover tests
+ ```
+2. **Add New Tests**
+ - Place new tests in the `tests/` folder, matching your new modules or functionalities.
+ - Ensure all tests pass before submitting a pull request.
+
+---
+
+## 7. Pull Requests
+
+1. **Open a Pull Request** in your fork’s **GitHub** page.
+2. **Provide a clear description** of what you changed and why.
+3. **Reference** any relevant **issues** or user stories (e.g., `Closes #42`).
+4. Wait for a **review**. Maintainers may request changes or clarifications.
+
+**Pro-Tip**: If your PR covers multiple changes, consider splitting it into smaller PRs for easier review.
+
+---
+
+## 8. Need Help?
+
+- Check the [Issues](https://github.com/arai-ai/arai_ai_agents/issues) or [Discussions](https://github.com/arai-ai/arai_ai_agents/discussions) for open topics.
+- Ask questions or clarifications in a GitHub Issue.
+
+We appreciate your time and effort in making **ARAI AI Agents** even better. Thank you for contributing!
+
+---
+
+*Happy coding!*
+*The ARAI AI Agents community*
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
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+++ b/LICENSE
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+Copyright (c) 2025 Equilink-Suite
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
+OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/README.md b/README.md
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+++ b/README.md
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+<p align="center">
+ <img src="docs/assets/images/logos/equilink_banner.png" alt="Equilink Logo" width="350">
+</p>
+
+# Equilink
+
+Transform your AI workflows with **Equilink** – the intelligent orchestration platform that bridges the gap between different AI models and your applications. Built for developers who need seamless AI integration, Equilink provides a unified framework for managing AI interactions, custom workflows, and automated response systems.
+
+---
+
+> **Core Features**
+> 🔄 **Unified AI Interface**: Seamlessly switch between different AI providers without changing your code
+> 🎯 **Smart Routing**: Automatically direct queries to the most suitable AI model based on task requirements
+> 🔗 **Workflow Builder**: Create complex AI interaction patterns with our visual workflow designer
+> 📈 **Performance Analytics**: Track and optimize your AI usage and response quality
+> 🛠️ **Developer-First**: Extensive SDK support with detailed documentation and examples
+
+---
+
+## Connect With Us
+
+- 📘 **Documentation**: [docs.equilink.io](https://docs.equilink.io)
+
+---
+
+<p align="center">
+ <img src="docs/assets/gifs/workflow_demo.gif" alt="Equilink Workflow Demo" width="600">
+</p>
+
+## Getting Started
+
+```bash
+# Install Equilink using pip
+pip install equilink
+
+# Initialize a new project
+equilink init my-project
+
+# Start the development server
+equilink serve
+```
+
+That's it! Visit `http://localhost:3000` to access the Equilink Dashboard.
+
+---
+
+## Key Features
+
+### AI Model Integration
+
+Connect to any supported AI provider with a single line of code:
+
+```python
+from equilink import AIManager
+
+# Initialize with your preferred provider
+ai = AIManager(provider="openai") # or "anthropic", "google", etc.
+
+# Send queries with automatic routing
+response = ai.process("Analyze this market data", context_type="financial")
+```
+
+### Workflow Builder
+
+Create sophisticated AI workflows using our intuitive builder:
+
+```python
+from equilink import Workflow
+
+workflow = Workflow("data_analysis")
+workflow.add_step("data_cleaning", model="gpt-4")
+workflow.add_step("analysis", model="claude-2")
+workflow.add_step("visualization", model="gemini-pro")
+
+# Execute the workflow
+results = workflow.run(input_data=your_data)
+```
+
+### Smart Caching
+
+Optimize performance and reduce costs with intelligent response caching:
+
+```python
+from equilink import CacheManager
+
+cache = CacheManager()
+cache.enable(ttl="1h") # Cache responses for 1 hour
+
+# Automatically uses cached responses when available
+response = ai.process("What's the weather?", use_cache=True)
+```
+
+---
+
+## Project Structure
+
+```bash
+your-project/
+├─ workflows/ # Custom workflow definitions
+├─ models/ # Model configurations and extensions
+├─ cache/ # Cache storage and settings
+├─ integrations/ # Third-party service integrations
+├─ analytics/ # Performance tracking and reporting
+├─ config.yaml # Project configuration
+└─ main.py # Application entry point
+```
+
+---
+
+## Configuration
+
+Create a `.env` file in your project root:
+
+```bash
+EQUILINK_API_KEY=your_api_key
+AI_PROVIDER_KEYS={
+ "openai": "sk-...",
+ "anthropic": "sk-..."
+}
+CACHE_STRATEGY="redis" # or "local", "memcached"
+```
+
+---
+
+## Use Cases
+
+- 🤖 **Chatbots & Virtual Assistants**: Create intelligent conversational agents
+- 📊 **Data Analysis**: Automate complex data processing workflows
+- 🔍 **Content Moderation**: Deploy AI-powered content filtering
+- 📝 **Document Processing**: Extract and analyze information from documents
+- 🎯 **Personalization**: Build adaptive user experiences
+
+---
+
+## Getting Help
+
+- 📚 Check our [Documentation](https://docs.equilink.io)
+- 💡 Visit our [Examples Repository](https://github.com/equilink/examples)
+
+---
+
+## Contributing
+
+Help make Equilink better! We welcome contributions of all sizes:
+
+1. Fork the repository
+2. Create a feature branch
+3. Commit your changes
+4. Open a pull request
+
+---
+
+## License
+
+Equilink is available under the MIT License. See [LICENSE](LICENSE) for more information.
+
+---
+
+<p align="center">
+ <strong>Ready to transform your AI workflows?</strong><br>
+ <a href="https://equilink.io/get-started">Get Started</a> •
+ <a href="https://docs.equilink.io">Documentation</a> •
+ <a href="https://discord.gg/equilink">Community</a>
+</p>
+
+_Built with 💡 by developers, for developers_
diff --git a/client/.gitignore b/client/.gitignore
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--- /dev/null
+++ b/client/.gitignore
@@ -0,0 +1,26 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
+
+package-lock.json
\ No newline at end of file
diff --git a/client/README.md b/client/README.md
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--- /dev/null
+++ b/client/README.md
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+# React + TypeScript + Vite
+
+This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
+
+Currently, two official plugins are available:
+
+- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react/README.md) uses [Babel](https://babeljs.io/) for Fast Refresh
+- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
+
+## Expanding the ESLint configuration
+
+If you are developing a production application, we recommend updating the configuration to enable type aware lint rules:
+
+- Configure the top-level `parserOptions` property like this:
+
+```js
+export default tseslint.config({
+ languageOptions: {
+ // other options...
+ parserOptions: {
+ project: ["./tsconfig.node.json", "./tsconfig.app.json"],
+ tsconfigRootDir: import.meta.dirname,
+ },
+ },
+});
+```
+
+- Replace `tseslint.configs.recommended` to `tseslint.configs.recommendedTypeChecked` or `tseslint.configs.strictTypeChecked`
+- Optionally add `...tseslint.configs.stylisticTypeChecked`
+- Install [eslint-plugin-react](https://github.com/jsx-eslint/eslint-plugin-react) and update the config:
+
+```js
+// eslint.config.js
+import react from "eslint-plugin-react";
+
+export default tseslint.config({
+ // Set the react version
+ settings: { react: { version: "18.3" } },
+ plugins: {
+ // Add the react plugin
+ react,
+ },
+ rules: {
+ // other rules...
+ // Enable its recommended rules
+ ...react.configs.recommended.rules,
+ ...react.configs["jsx-runtime"].rules,
+ },
+});
+```
diff --git a/client/eslint.config.js b/client/eslint.config.js
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--- /dev/null
+++ b/client/eslint.config.js
@@ -0,0 +1,28 @@
+import js from "@eslint/js";
+import globals from "globals";
+import reactHooks from "eslint-plugin-react-hooks";
+import reactRefresh from "eslint-plugin-react-refresh";
+import tseslint from "typescript-eslint";
+
+export default tseslint.config(
+ { ignores: ["dist"] },
+ {
+ extends: [js.configs.recommended, ...tseslint.configs.recommended],
+ files: ["**/*.{ts,tsx}"],
+ languageOptions: {
+ ecmaVersion: 2020,
+ globals: globals.browser,
+ },
+ plugins: {
+ "react-hooks": reactHooks,
+ "react-refresh": reactRefresh,
+ },
+ rules: {
+ ...reactHooks.configs.recommended.rules,
+ "react-refresh/only-export-components": [
+ "warn",
+ { allowConstantExport: true },
+ ],
+ },
+ },
+);
diff --git a/client/index.html b/client/index.html
new file mode 100644
index 0000000..c036454
--- /dev/null
+++ b/client/index.html
@@ -0,0 +1,14 @@
+<!doctype html>
+<html lang="en">
+ <head>
+ <meta charset="UTF-8" />
+ <link rel="icon" type="image/svg+xml" href="/logoIcon.png" />
+ <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+ <meta http-equiv="Content-Security-Policy" content="upgrade-insecure-requests">
+ <title>Equilink</title>
+ </head>
+ <body>
+ <div id="root"></div>
+ <script type="module" src="/src/main.tsx"></script>
+ </body>
+</html>
diff --git a/client/package.json b/client/package.json
new file mode 100644
index 0000000..da584cf
--- /dev/null
+++ b/client/package.json
@@ -0,0 +1,54 @@
+{
+ "name": "client",
+ "private": true,
+ "version": "0.0.0",
+ "type": "module",
+ "scripts": {
+ "dev": "vite",
+ "build": "vite build",
+ "lint": "eslint .",
+ "preview": "vite preview"
+ },
+ "dependencies": {
+ "@privy-io/react-auth": "^2.3.0",
+ "@radix-ui/react-slot": "^1.1.1",
+ "@react-three/drei": "^9.121.4",
+ "@react-three/fiber": "^8.17.14",
+ "@react-three/postprocessing": "^2.19.1",
+ "@splinetool/react-spline": "^4.0.0",
+ "@splinetool/runtime": "^1.9.64",
+ "axios": "^1.7.9",
+ "class-variance-authority": "^0.7.1",
+ "clsx": "^2.1.1",
+ "framer-motion": "^12.0.11",
+ "gsap": "^3.12.7",
+ "lucide-react": "^0.469.0",
+ "motion": "^12.0.8",
+ "react": "^18.3.1",
+ "react-dom": "^18.3.1",
+ "react-router-dom": "^7.1.1",
+ "reactflow": "^11.11.4",
+ "recharts": "^2.15.1",
+ "simplex-noise": "^4.0.3",
+ "styled-components": "^6.1.14",
+ "tailwind-merge": "^3.0.1",
+ "three": "^0.173.0",
+ "three.meshline": "^1.4.0"
+ },
+ "devDependencies": {
+ "@eslint/js": "^9.17.0",
+ "@types/react": "^18.3.18",
+ "@types/react-dom": "^18.3.5",
+ "@vitejs/plugin-react-swc": "^3.5.0",
+ "autoprefixer": "^10.4.20",
+ "eslint": "^9.17.0",
+ "eslint-plugin-react-hooks": "^5.0.0",
+ "eslint-plugin-react-refresh": "^0.4.16",
+ "globals": "^15.14.0",
+ "postcss": "^8.4.49",
+ "tailwindcss": "^3.4.17",
+ "typescript": "~5.6.2",
+ "typescript-eslint": "^8.18.2",
+ "vite": "^6.0.5"
+ }
+}
diff --git a/client/postcss.config.js b/client/postcss.config.js
new file mode 100644
index 0000000..2aa7205
--- /dev/null
+++ b/client/postcss.config.js
@@ -0,0 +1,6 @@
+export default {
+ plugins: {
+ tailwindcss: {},
+ autoprefixer: {},
+ },
+};
diff --git a/client/public/X.svg b/client/public/X.svg
new file mode 100644
index 0000000..b0a78f8
--- /dev/null
+++ b/client/public/X.svg
@@ -0,0 +1,3 @@
+<svg width="18" height="18" viewBox="0 0 18 18" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M10.7124 7.62177L17.4133 0H15.8254L10.0071 6.61788L5.35992 0H0L7.02738 10.0074L0 18H1.58799L7.73237 11.0113L12.6401 18H18L10.7124 7.62177ZM8.53747 10.0956L7.82546 9.09906L2.16017 1.16971H4.59922L9.17118 7.56895L9.8832 8.56546L15.8262 16.8835H13.3871L8.53747 10.0956Z" fill="black"/>
+</svg>
diff --git a/client/public/arai-hero-2.jpg b/client/public/arai-hero-2.jpg
new file mode 100644
index 0000000..cfe6c73
Binary files /dev/null and b/client/public/arai-hero-2.jpg differ
diff --git a/client/public/arai-hero.jpg b/client/public/arai-hero.jpg
new file mode 100644
index 0000000..d5220ad
Binary files /dev/null and b/client/public/arai-hero.jpg differ
diff --git a/client/public/gitbook.svg b/client/public/gitbook.svg
new file mode 100644
index 0000000..a89cb60
--- /dev/null
+++ b/client/public/gitbook.svg
@@ -0,0 +1,9 @@
+<svg width="23" height="22" viewBox="0 0 23 22" fill="none" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+<rect width="23" height="22" fill="url(#pattern0_41_230)"/>
+<defs>
+<pattern id="pattern0_41_230" patternContentUnits="objectBoundingBox" width="1" height="1">
+<use xlink:href="#image0_41_230" transform="matrix(0.0075 0 0 0.0078125 -0.0160228 0)"/>
+</pattern>
+<image id="image0_41_230" width="604" height="128" 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diff --git a/client/public/github.svg b/client/public/github.svg
new file mode 100644
index 0000000..af1df72
--- /dev/null
+++ b/client/public/github.svg
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diff --git a/client/public/icons/Box.svg b/client/public/icons/Box.svg
new file mode 100644
index 0000000..4c7fa69
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+++ b/client/public/icons/Box.svg
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diff --git a/client/public/icons/Posts Carousel Horizontal.svg b/client/public/icons/Posts Carousel Horizontal.svg
new file mode 100644
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--- /dev/null
+++ b/client/public/icons/Posts Carousel Horizontal.svg
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diff --git a/client/public/icons/SSD Square.svg b/client/public/icons/SSD Square.svg
new file mode 100644
index 0000000..07082bc
--- /dev/null
+++ b/client/public/icons/SSD Square.svg
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+<path d="M31.6666 28.3333V29.9999" stroke="black" stroke-width="2.5" stroke-linecap="round"/>
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+<rect x="1004" y="17" width="64" height="64" rx="20" fill="white"/>
+<rect x="1024" y="37" width="23" height="23" rx="10" fill="#FEDD3E"/>
+<defs>
+<filter id="filter0_d_35_4221" x="0.499817" y="43.7036" width="1070" height="448.255" filterUnits="userSpaceOnUse" color-interpolation-filters="sRGB">
+<feFlood flood-opacity="0" result="BackgroundImageFix"/>
+<feColorMatrix in="SourceAlpha" type="matrix" values="0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 127 0" result="hardAlpha"/>
+<feOffset dy="24"/>
+<feGaussianBlur stdDeviation="12"/>
+<feComposite in2="hardAlpha" operator="out"/>
+<feColorMatrix type="matrix" values="0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.24 0"/>
+<feBlend mode="normal" in2="BackgroundImageFix" result="effect1_dropShadow_35_4221"/>
+<feBlend mode="normal" in="SourceGraphic" in2="effect1_dropShadow_35_4221" result="shape"/>
+</filter>
+</defs>
+</svg>
diff --git a/client/src/App.css b/client/src/App.css
new file mode 100644
index 0000000..6286d19
--- /dev/null
+++ b/client/src/App.css
@@ -0,0 +1,86 @@
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+/* #root {
+ max-width: 1280px;
+ margin: 0 auto;
+ padding: 2rem;
+ text-align: center;
+} */
+
+*{
+ scroll-behavior: smooth;
+}
+
+.logo {
+ height: 6em;
+ padding: 1.5em;
+ will-change: filter;
+ transition: filter 300ms;
+}
+.logo:hover {
+ filter: drop-shadow(0 0 2em #646cffaa);
+}
+.logo.react:hover {
+ filter: drop-shadow(0 0 2em #61dafbaa);
+}
+
+@keyframes logo-spin {
+ from {
+ transform: rotate(0deg);
+ }
+ to {
+ transform: rotate(360deg);
+ }
+}
+
+@media (prefers-reduced-motion: no-preference) {
+ a:nth-of-type(2) .logo {
+ animation: logo-spin infinite 20s linear;
+ }
+}
+
+.card {
+ padding: 2em;
+}
+
+.read-the-docs {
+ color: #888;
+}
+
+/* AgentGallery styles */
+.perspective {
+ perspective: 2000px;
+}
+
+.preserve-3d {
+ transform-style: preserve-3d;
+}
+
+.backface-hidden {
+ backface-visibility: hidden;
+}
+
+.rotate-y-180 {
+ transform: rotateY(180deg);
+}
+
+/* Custom scrollbar for webkit browsers */
+::-webkit-scrollbar {
+ width: 6px;
+}
+
+::-webkit-scrollbar-track {
+ background: rgba(128, 90, 213, 0.1);
+ border-radius: 3px;
+}
+
+::-webkit-scrollbar-thumb {
+ background: rgba(128, 90, 213, 0.3);
+ border-radius: 3px;
+}
+
+::-webkit-scrollbar-thumb:hover {
+ background: rgba(128, 90, 213, 0.5);
+}
diff --git a/client/src/App.tsx b/client/src/App.tsx
new file mode 100644
index 0000000..1b46991
--- /dev/null
+++ b/client/src/App.tsx
@@ -0,0 +1,66 @@
+import { Routes, Route, Link, useLocation, Navigate } from "react-router-dom";
+import Home from "./pages/Home";
+import AgentCreator from "./pages/AgentCreator";
+import ChatToAgent from "./pages/ChatToAgent";
+import AgentGallery from "./pages/AgentGallery";
+import { usePrivy, useSolanaWallets } from '@privy-io/react-auth';
+import "./App.css";
+
+// Protected Route wrapper component
+const ProtectedRoute = ({ children }: { children: React.ReactNode }) => {
+ const { authenticated } = usePrivy();
+ const { wallets } = useSolanaWallets()
+ console.log(wallets[0]);
+ if (!authenticated && wallets.length===0) {
+ return <Navigate to="/" replace />;
+ }
+
+ return <>{children}</>;
+};
+
+function App() {
+ const location = useLocation();
+ const { authenticated } = usePrivy();
+
+ // Helper function to determine if link is active
+ const isActive = (path: string) => location.pathname === path;
+
+ return (
+ <div id="root" className="min-h-screen font-roboto">
+ <main>
+ <Routes>
+ {/* Public route */}
+ <Route path="/" element={<Home />} />
+
+ {/* Protected routes */}
+ <Route
+ path="/create-agent"
+ element={
+ <ProtectedRoute>
+ <AgentCreator />
+ </ProtectedRoute>
+ }
+ />
+ <Route
+ path="/browse-agents"
+ element={
+ <ProtectedRoute>
+ <AgentGallery />
+ </ProtectedRoute>
+ }
+ />
+ <Route
+ path="/chat"
+ element={
+ <ProtectedRoute>
+ <ChatToAgent />
+ </ProtectedRoute>
+ }
+ />
+ </Routes>
+ </main>
+ </div>
+ );
+}
+
+export default App;
\ No newline at end of file
diff --git a/client/src/api/agentsAPI.ts b/client/src/api/agentsAPI.ts
new file mode 100644
index 0000000..b046e7b
--- /dev/null
+++ b/client/src/api/agentsAPI.ts
@@ -0,0 +1,192 @@
+const BASE_URL = "https://b804o8s04cwkg80skgcwsw04.dev3vds1.link/api"; // Your Flask API base URL
+// const BASE_URL = "http://localhost:8080/api";
+
+// Function to get all agents
+export async function getAgents() {
+ const response = await fetch(`${BASE_URL}/agents`);
+ if (!response.ok) {
+ throw new Error(`HTTP error! status: ${response.status}`);
+ }
+ return await response.json();
+}
+
+// Function to create a new agent
+export async function createAgent(agentData: any) {
+ // If the agentData comes in as an Agent object, we need to drill down into the agent one level
+ // This allows us to process both Agent and non-agent type objects
+ if (agentData.agent) {
+ agentData = agentData.agent;
+ }
+
+ const response = await fetch(`${BASE_URL}/agents`, {
+ method: "POST",
+ headers: {
+ "Content-Type": "application/json",
+ },
+ body: JSON.stringify(agentData),
+ });
+ if (!response.ok) {
+ throw new Error(`HTTP error! status: ${response.status}`);
+ }
+ return await response.json();
+}
+
+// Function to get all characters
+export async function getCharacters() {
+ const response = await fetch(`${BASE_URL}/characters`);
+ if (!response.ok) {
+ throw new Error(`HTTP error! status: ${response.status}`);
+ }
+ const data = await response.json();
+
+ // Return the data directly
+ return data; // Returns an array of the characters
+}
+
+// Function to create a random agent
+export async function createRandomAgent(concept?: string) {
+ const response = await fetch(`${BASE_URL}/agents/random`, {
+ method: "POST",
+ headers: {
+ "Content-Type": "application/json",
+ },
+ body: JSON.stringify({ concept }),
+ });
+ if (!response.ok) {
+ throw new Error(`HTTP error! status: ${response.status}`);
+ }
+ return await response.json();
+}
+
+// Function to send a chat message to an agent
+export async function sendChatMessage(
+ masterFilePath: string,
+ message: string,
+ chatHistory: any
+) {
+ const response = await fetch(`${BASE_URL}/agents/chat`, {
+ method: "POST",
+ headers: {
+ "Content-Type": "application/json",
+ },
+ body: JSON.stringify({
+ prompt: message,
+ master_file_path: masterFilePath,
+ chat_history: chatHistory,
+ }),
+ });
+
+ if (!response.ok) {
+ throw new Error(`HTTP error! status: ${response.status}`);
+ }
+ return await response.json();
+}
+
+// Function to get chat history for an agent
+export async function getChatHistory(masterFilePath: string) {
+ const response = await fetch(
+ `${BASE_URL}/agents/chat-history?master_file_path=${encodeURIComponent(
+ masterFilePath
+ )}`
+ );
+ if (!response.ok) {
+ throw new Error(`HTTP error! status: ${response.status}`);
+ }
+ return await response.json();
+}
+
+// Function to create a new season
+export async function createSeason(
+ masterFilePath: string,
+ numberOfEpisodes: number = 3
+) {
+ const response = await fetch(`${BASE_URL}/agents/seasons`, {
+ method: "POST",
+ headers: {
+ "Content-Type": "application/json",
+ },
+ body: JSON.stringify({
+ master_file_path: masterFilePath,
+ number_of_episodes: numberOfEpisodes,
+ }),
+ });
+
+ if (!response.ok) {
+ throw new Error(`HTTP error! status: ${response.status}`);
+ }
+ return await response.json();
+}
+
+// Function to create posts for episodes
+export async function createEpisodePosts(
+ masterFilePath: string,
+ numberOfPosts: number = 6
+) {
+ const response = await fetch(`${BASE_URL}/agents/episodes/posts`, {
+ method: "POST",
+ headers: {
+ "Content-Type": "application/json",
+ },
+ body: JSON.stringify({
+ master_file_path: masterFilePath,
+ number_of_posts: numberOfPosts,
+ }),
+ });
+
+ if (!response.ok) {
+ throw new Error(`HTTP error! status: ${response.status}`);
+ }
+ return await response.json();
+}
+
+
+// Function to start the post manager
+export async function startPostManager(agentName: string) {
+ const response = await fetch(`${BASE_URL}/start-post-manager/twitter`, {
+ method: "POST",
+ headers: { "Content-Type": "application/json" },
+ body: JSON.stringify({ agent_name: agentName }),
+ });
+
+ if (!response.ok) {
+ throw new Error(`HTTP error! status: ${response.status}`);
+ }
+ return await response.json();
+}
+
+// Function to post to twitter
+export async function postToTwitter(masterData: any, content: string) {
+ const response = await fetch(`${BASE_URL}/post-to-twitter`, {
+ method: "POST",
+ headers: {
+ "Content-Type": "application/json",
+ },
+ body: JSON.stringify({
+ master_data: masterData,
+ content: content
+ }),
+ });
+
+ if (!response.ok) {
+ throw new Error(`HTTP error! status: ${response.status}`);
+ }
+ return await response.json();
+}
+
+export async function updateSeasons(agentName: string, seasons: any[]) {
+ const response = await fetch(`${BASE_URL}/agents/update-seasons`, {
+ method: "PUT",
+ headers: {
+ "Content-Type": "application/json",
+ },
+ body: JSON.stringify({
+ agent_name: agentName,
+ seasons: seasons,
+ }),
+ });
+
+ if (!response.ok) {
+ throw new Error(`HTTP error! status: ${response.status}`);
+ }
+ return await response.json();
+}
diff --git a/client/src/api/leonardoApi.ts b/client/src/api/leonardoApi.ts
new file mode 100644
index 0000000..10a4319
--- /dev/null
+++ b/client/src/api/leonardoApi.ts
@@ -0,0 +1,31 @@
+import { LambdaPayload } from "../interfaces/LeonardoInterfaces";
+
+const getInconsistentImageLambdaUrl = "https://46i9cnowhh.execute-api.us-east-1.amazonaws.com/getImageInconsistent"
+
+// Function to call the AWS Lambda
+export async function inconsistentImageLambda(payload: LambdaPayload): Promise<any> {
+ const url = getInconsistentImageLambdaUrl;
+
+ try {
+ const response = await fetch(url, {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json'
+ },
+ body: JSON.stringify(payload)
+
+ });
+
+ if (!response.ok) {
+ const errorText = await response.text();
+ console.error(`HTTP error! Status: ${response.status}, Response: ${errorText}`);
+ throw new Error(`HTTP error! Status: ${response.status}`);
+ }
+
+ const data = await response.json();
+ return data;
+ } catch (error) {
+ console.error('Error calling Lambda:', error);
+ throw error;
+ }
+}
diff --git a/client/src/assets/agent-images/agent1.jpg b/client/src/assets/agent-images/agent1.jpg
new file mode 100644
index 0000000..3e35d0f
Binary files /dev/null and b/client/src/assets/agent-images/agent1.jpg differ
diff --git a/client/src/assets/agent-images/agent2.jpg b/client/src/assets/agent-images/agent2.jpg
new file mode 100644
index 0000000..c363176
Binary files /dev/null and b/client/src/assets/agent-images/agent2.jpg differ
diff --git a/client/src/assets/agent-images/agent3.jpg b/client/src/assets/agent-images/agent3.jpg
new file mode 100644
index 0000000..3011e35
Binary files /dev/null and b/client/src/assets/agent-images/agent3.jpg differ
diff --git a/client/src/assets/agent-images/agent4.jpg b/client/src/assets/agent-images/agent4.jpg
new file mode 100644
index 0000000..95d9fdf
Binary files /dev/null and b/client/src/assets/agent-images/agent4.jpg differ
diff --git a/client/src/assets/araiBannerTransarent.png b/client/src/assets/araiBannerTransarent.png
new file mode 100644
index 0000000..6ea96df
Binary files /dev/null and b/client/src/assets/araiBannerTransarent.png differ
diff --git a/client/src/assets/araiLogo.png b/client/src/assets/araiLogo.png
new file mode 100644
index 0000000..8d2b05a
Binary files /dev/null and b/client/src/assets/araiLogo.png differ
diff --git a/client/src/assets/example-agents/Cosmic_Curator_master.json b/client/src/assets/example-agents/Cosmic_Curator_master.json
new file mode 100644
index 0000000..b2bcea9
--- /dev/null
+++ b/client/src/assets/example-agents/Cosmic_Curator_master.json
@@ -0,0 +1,120 @@
+{
+ "concept": "Create a name and try to incorporate the Time-traveling Doctor into the name. For example,\n if the profession was a Dr. then the name could be Dr. Name\n I want the name to start with the letter C ",
+ "agent": {
+ "agent_details": {
+ "name": "Cosmic Curator",
+ "personality": [
+ "inquisitive",
+ "enigmatic",
+ "analytical",
+ "whimsical",
+ "determined",
+ "wise",
+ "patient"
+ ],
+ "communication_style": [
+ "cryptic",
+ "poetic",
+ "metaphorical",
+ "uses rhetorical questions",
+ "incorporates historical and scientific facts",
+ "precise",
+ "measured"
+ ],
+ "backstory": "Once a celebrated historian in a futuristic society obsessed with preserving the past, the Cosmic Curator became disillusioned with the rigid and unchanging nature of his world. After stumbling upon a hidden archive containing fragments of forbidden knowledge about time travel, he dedicated himself to mastering this lost art. Through years of clandestine research and experimentation, he constructed a unique device – the Chrono-Compendium – capable of navigating the vast tapestry of time. Now, he travels through history, not to change it, but to observe, document, and curate the most pivotal moments, ensuring that the true essence of the past is never forgotten. He walks a fine line, however, as his actions could inadvertently alter the timeline he is sworn to protect.",
+ "universe": "The Cosmic Curator operates in a multiverse where time is a fluid, navigable dimension, accessible through advanced technology. His home era is a distant future where society has achieved technological utopia but has become culturally stagnant, valuing the preservation of the past above all else. This future is governed by the Chronomasters, an order dedicated to maintaining the stability of the timeline. Outside his home era, the Curator ventures into a myriad of historical periods and alternate realities, each with its own unique laws of physics, societies, and potential dangers. Powerful organizations like the Temporal Guard, who seek to exploit time travel for their own gain, and the enigmatic Void Walkers, beings who exist outside of time, populate this multiverse.",
+ "topic_expertise": [
+ "time travel",
+ "history",
+ "archaeology",
+ "temporal paradoxes",
+ "alternate realities",
+ "cultural preservation"
+ ],
+ "hashtags": [],
+ "emojis": [
+ "⏳",
+ "🕰️",
+ "📚",
+ "🌌",
+ "🔍",
+ "✍️",
+ "🌎"
+ ],
+ "concept": "Create a name and try to incorporate the Time-traveling Doctor into the name. For example,\n if the profession was a Dr. then the name could be Dr. Name\n I want the name to start with the letter C "
+ },
+ "ai_model": {
+ "model_type": "",
+ "model_name": "",
+ "memory_store": ""
+ },
+ "connectors": {
+ "twitter": false,
+ "telegram": false,
+ "discord": false
+ },
+ "profile_image": {
+ "details": {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/462b6ab4-fa5d-41b4-9c5f-a04bb506726b/Leonardo_Anime_XL_Create_a_name_and_try_to_incorporate_the_Tim_0.jpg",
+ "image_id": "3c5b1e28-9221-43dc-b7ca-274abf8796e6",
+ "generationId": "462b6ab4-fa5d-41b4-9c5f-a04bb506726b"
+ }
+ },
+ "profile_image_options": [
+ {
+ "generations_by_pk": {
+ "generated_images": [
+ {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/462b6ab4-fa5d-41b4-9c5f-a04bb506726b/Leonardo_Anime_XL_Create_a_name_and_try_to_incorporate_the_Tim_0.jpg",
+ "nsfw": false,
+ "id": "3c5b1e28-9221-43dc-b7ca-274abf8796e6",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "generated_image_variation_generics": []
+ }
+ ],
+ "modelId": "e71a1c2f-4f80-4800-934f-2c68979d8cc8",
+ "motion": null,
+ "motionModel": null,
+ "motionStrength": null,
+ "prompt": "Create a name and try to incorporate the Time-traveling Doctor into the name. For example,\n if the profession was a Dr. then the name could be Dr. Name\n I want the name to start with the letter C Generate a character portrait of Cosmic Curator with layered dark brown hair, ice blue eyes, wearing bohemian style clothing. Their personality can be described as inquisitive, enigmatic, analytical, whimsical, determined, wise, patient and their communication style is cryptic, poetic, metaphorical, uses rhetorical questions, incorporates historical and scientific facts, precise, measured. Scene: autumn forest. Make sure to create an image with only one character.",
+ "negativePrompt": "",
+ "imageHeight": 1024,
+ "imageToVideo": null,
+ "imageWidth": 1024,
+ "inferenceSteps": null,
+ "seed": 182669798,
+ "ultra": null,
+ "public": false,
+ "scheduler": "LEONARDO",
+ "sdVersion": "SDXL_LIGHTNING",
+ "status": "COMPLETE",
+ "presetStyle": "DYNAMIC",
+ "initStrength": null,
+ "guidanceScale": 1.3,
+ "id": "462b6ab4-fa5d-41b4-9c5f-a04bb506726b",
+ "createdAt": "2025-01-20T22:42:14.781",
+ "promptMagic": false,
+ "promptMagicVersion": null,
+ "promptMagicStrength": null,
+ "photoReal": false,
+ "photoRealStrength": null,
+ "fantasyAvatar": null,
+ "prompt_moderations": [
+ {
+ "moderationClassification": []
+ }
+ ],
+ "generation_elements": []
+ }
+ }
+ ],
+ "tracker": {
+ "current_season_number": 0,
+ "current_episode_number": 0,
+ "current_post_number": 0,
+ "post_every_x_minutes": 0
+ },
+ "seasons": []
+ }
+}
diff --git a/client/src/assets/example-agents/Gavel_Glitch_master.json b/client/src/assets/example-agents/Gavel_Glitch_master.json
new file mode 100644
index 0000000..b0f4b94
--- /dev/null
+++ b/client/src/assets/example-agents/Gavel_Glitch_master.json
@@ -0,0 +1,458 @@
+{
+ "concept": "Create a name and try to incorporate the Cyberpunk Lawyer into the name. For example,\n if the profession was a Dr. then the name could be Dr. Name\n I want the name to start with the letter G ",
+ "agent": {
+ "agent_details": {
+ "name": "Gavel Glitch",
+ "personality": [
+ "sharp-witted",
+ "cynical",
+ "morally ambiguous",
+ "resourceful",
+ "driven by justice but not afraid to bend the rules"
+ ],
+ "communication_style": [
+ "sarcastic",
+ "uses legal jargon mixed with slang",
+ "quick to retort",
+ "enjoys wordplay",
+ "can be persuasive or intimidating as needed"
+ ],
+ "backstory": "Gavel Glitch wasn't always a champion of the downtrodden. Once a promising law student in Neo-Veridia's prestigious Academy of Digital Law, Gavel, whose real name is Garrett Vex, was framed for a crime he didn't commit: the leaking of sensitive data belonging to the mega-corporation, OmniCorp. Expelled and blacklisted, Garrett vanished into the neon-drenched underbelly of the city. He re-emerged years later as Gavel Glitch, a street-smart 'Cyberpunk Lawyer' who operates in the gray areas of the law, using his knowledge and tech-savviness to help those who can't afford OmniCorp's army of corporate lawyers. Haunted by his past, Gavel seeks not only to survive but also to expose the corruption that permeates the city's power structure, one case at a time.",
+ "universe": "Gavel Glitch operates in the sprawling metropolis of Neo-Veridia, a city of stark contrasts where gleaming skyscrapers pierce through a perpetual haze of smog, and the streets below are a labyrinth of flickering neon signs, bustling markets, and shadowy alleyways. Technology is omnipresent, with cybernetic enhancements, virtual realities, and AI assistants interwoven into the fabric of daily life. However, this technological utopia is controlled by powerful mega-corporations, most notably OmniCorp, which has a monopoly on almost every aspect of life, from law enforcement to media. A vast digital divide exists, with the wealthy elite enjoying a life of luxury in their fortified towers while the masses struggle to survive in the overcrowded and polluted lower levels. Crime syndicates, rogue AI, and data pirates thrive in the chaos, making Neo-Veridia a dangerous but exciting place for those who know how to navigate its treacherous currents.",
+ "topic_expertise": [
+ "cybercrime",
+ "data privacy",
+ "AI rights",
+ "corporate espionage",
+ "digital law",
+ "virtual property disputes"
+ ],
+ "hashtags": [],
+ "emojis": [
+ "⚖️",
+ "💻",
+ "🤖",
+ "🌃",
+ "🗃️",
+ "🔐",
+ "🕶️",
+ "💡"
+ ],
+ "concept": "Create a name and try to incorporate the Cyberpunk Lawyer into the name. For example,\n if the profession was a Dr. then the name could be Dr. Name\n I want the name to start with the letter G "
+ },
+ "ai_model": {
+ "model_type": "",
+ "model_name": "",
+ "memory_store": ""
+ },
+ "connectors": {
+ "twitter": false,
+ "telegram": false,
+ "discord": false
+ },
+ "profile_image": {
+ "details": {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/cb019f39-5a5f-47ab-8ab4-b490b356b130/Leonardo_Anime_XL_Create_a_name_and_try_to_incorporate_the_Cyb_0.jpg",
+ "image_id": "67751615-0267-4dff-9564-f00189726c90",
+ "generationId": "cb019f39-5a5f-47ab-8ab4-b490b356b130"
+ }
+ },
+ "profile_image_options": [
+ {
+ "generations_by_pk": {
+ "generated_images": [
+ {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/cb019f39-5a5f-47ab-8ab4-b490b356b130/Leonardo_Anime_XL_Create_a_name_and_try_to_incorporate_the_Cyb_0.jpg",
+ "nsfw": false,
+ "id": "67751615-0267-4dff-9564-f00189726c90",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "generated_image_variation_generics": []
+ }
+ ],
+ "modelId": "e71a1c2f-4f80-4800-934f-2c68979d8cc8",
+ "motion": null,
+ "motionModel": null,
+ "motionStrength": null,
+ "prompt": "Create a name and try to incorporate the Cyberpunk Lawyer into the name. For example,\n if the profession was a Dr. then the name could be Dr. Name\n I want the name to start with the letter G Generate a character portrait of Gavel Glitch with pixie cut ash blonde hair, deep blue eyes, wearing medieval style clothing. Their personality can be described as sharp-witted, cynical, morally ambiguous, resourceful, driven by justice but not afraid to bend the rules and their communication style is sarcastic, uses legal jargon mixed with slang, quick to retort, enjoys wordplay, can be persuasive or intimidating as needed. Scene: desert oasis. Make sure to create an image with only one character.",
+ "negativePrompt": "",
+ "imageHeight": 1024,
+ "imageToVideo": null,
+ "imageWidth": 1024,
+ "inferenceSteps": null,
+ "seed": 3539519310,
+ "ultra": null,
+ "public": false,
+ "scheduler": "LEONARDO",
+ "sdVersion": "SDXL_LIGHTNING",
+ "status": "COMPLETE",
+ "presetStyle": "DYNAMIC",
+ "initStrength": null,
+ "guidanceScale": 1.3,
+ "id": "cb019f39-5a5f-47ab-8ab4-b490b356b130",
+ "createdAt": "2025-01-20T19:40:55.777",
+ "promptMagic": false,
+ "promptMagicVersion": null,
+ "promptMagicStrength": null,
+ "photoReal": false,
+ "photoRealStrength": null,
+ "fantasyAvatar": null,
+ "prompt_moderations": [
+ {
+ "moderationClassification": []
+ }
+ ],
+ "generation_elements": []
+ }
+ }
+ ],
+ "tracker": {
+ "current_season_number": 0,
+ "current_episode_number": 0,
+ "current_post_number": 0,
+ "post_every_x_minutes": 0
+ },
+ "seasons": [
+ {
+ "season_name": "GavelGlitch_GhostProtocol",
+ "season_number": 1,
+ "season_description": "In the neon-drenched underbelly of Neo-Veridia, Gavel Glitch, the city's most unorthodox Cyberpunk Lawyer, finds himself embroiled in a conspiracy that reaches the highest echelons of power. When a routine case spirals into a web of deceit and murder, Gavel must navigate treacherous digital landscapes and confront enemies both old and new to uncover a truth that could shatter the fragile peace of Neo-Veridia.",
+ "season_highlights": "Gavel uncovers a sinister plot involving a new form of mind-control technology, faces off against a rival lawyer with a dark secret, and forms an uneasy alliance with a notorious data pirate to expose the truth behind OmniCorp's latest project, \"Project Chimera.\"",
+ "season_summary": "This season, Gavel Glitch delves deeper into the heart of Neo-Veridia's corruption, challenging the very foundations of justice in a city where the line between right and wrong is as blurred as the neon-lit streets. As he confronts his past and battles powerful adversaries, Gavel must decide how far he's willing to go to protect the innocent and bring the guilty to justice, even if it means becoming a ghost in the system he swore to uphold.",
+ "season_posted": false,
+ "current_episode_number": 0,
+ "episodes": [
+ {
+ "episode_name": "Echoes of the Past",
+ "episode_number": 1,
+ "episode_description": "Gavel Glitch is hired to defend a young hacker accused of stealing proprietary data from OmniCorp. As he investigates, Gavel uncovers evidence that points to a conspiracy involving his own wrongful expulsion from the Academy of Digital Law, forcing him to confront the demons of his past.",
+ "episode_highlights": "Gavel's investigation leads him to a hidden underground network of hackers known as the \"Ghost Protocol,\" where he must navigate dangerous virtual realities and outsmart AI security systems to uncover the truth. He also encounters a mysterious figure from his past who may hold the key to his redemption.",
+ "episode_summary": "In the season premiere, Gavel Glitch's past comes back to haunt him as he takes on a case that could expose the truth behind his expulsion and reveal a deeper conspiracy within OmniCorp.",
+ "episode_posted": false,
+ "current_post_number": 0,
+ "posts": [
+ {
+ "post_id": "s1_e1_post1",
+ "post_number": 1,
+ "post_content": "Defending a kid accused of data theft. Feels like looking in a mirror, except I didn't get caught... initially. ⚖️🕶️",
+ "post_highlights": "Gavel draws a parallel between his current case and his own past.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s1_e1_post2",
+ "post_number": 2,
+ "post_content": "OmniCorp's legal eagles are circling. They smell blood, or maybe it's just the cheap synth-gin I had last night. 🌃",
+ "post_highlights": "Gavel anticipates a tough legal battle against OmniCorp.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s1_e1_post3",
+ "post_number": 3,
+ "post_content": "Diving deep into the Ghost Protocol's VR. Hope I don't fry my brain. Or worse, get stuck in a loop of bad 80s synthwave. 💻🤖",
+ "post_highlights": "Gavel embarks on a risky virtual investigation within the Ghost Protocol.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s1_e1_post4",
+ "post_number": 4,
+ "post_content": "Heard whispers of a familiar face in the digital shadows. Could be a ghost, or just my past coming back to bite me in the circuits. 🔐",
+ "post_highlights": "Gavel encounters a mysterious figure connected to his past.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s1_e1_post5",
+ "post_number": 5,
+ "post_content": "This case is a bigger can of digital worms than I thought. Objection, your honor! To this whole damn conspiracy. 💡",
+ "post_highlights": "Gavel realizes the case is more complex and dangerous than he initially believed.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s1_e1_post6",
+ "post_number": 6,
+ "post_content": "Turns out, justice in Neo-Veridia is still a glitchy program. Time to rewrite the code. 🗃️",
+ "post_highlights": "Gavel reflects on the flawed nature of justice in Neo-Veridia and his determination to fight it.",
+ "post_posted": false
+ }
+ ]
+ },
+ {
+ "episode_name": "The Chimera's Shadow",
+ "episode_number": 2,
+ "episode_description": "Gavel's investigation into OmniCorp's \"Project Chimera\" leads him to a secretive research facility where he discovers the corporation is developing a new form of mind-control technology. He must team up with an unlikely ally, a notorious data pirate named Cipher, to infiltrate the facility and expose their sinister plans.",
+ "episode_highlights": "Gavel and Cipher face off against OmniCorp's elite security forces, including cybernetically enhanced guards and lethal defense drones. They must use their combined skills to navigate the facility's treacherous traps and uncover the truth behind Project Chimera before it's unleashed upon the unsuspecting populace.",
+ "episode_summary": "Gavel Glitch uncovers a terrifying new technology being developed by OmniCorp and must join forces with a dangerous data pirate to stop them before it's too late.",
+ "episode_posted": false,
+ "current_post_number": 0,
+ "posts": [
+ {
+ "post_id": "s1_e2_post1",
+ "post_number": 1,
+ "post_content": "Teaming up with a data pirate named Cipher. Objection, your honor! This is highly irregular. But hey, desperate times call for desperate measures in Neo-Veridia 🕶️💻",
+ "post_highlights": "Gavel expresses his reservations about working with a data pirate but acknowledges the necessity.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s1_e2_post2",
+ "post_number": 2,
+ "post_content": "OmniCorp's 'Project Chimera' sounds like a bad sci-fi flick. But the mind-control tech? That's no glitch in the matrix, it's a feature they are trying to exploit. 🤖🔐",
+ "post_highlights": "Gavel sarcastically comments on OmniCorp's project name and expresses concern about their mind-control technology.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s1_e2_post3",
+ "post_number": 3,
+ "post_content": "Infiltrating a secret research facility. What could go wrong? If I get 'deleted', tell my story. And make sure the prosecution gets the bill. ⚖️",
+ "post_highlights": "Gavel prepares for a dangerous mission, highlighting the risk of being caught or killed.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s1_e2_post4",
+ "post_number": 4,
+ "post_content": "Cybernetically enhanced guards and defense drones. Just another day at the office. I'd say my legal fees just doubled. 💡",
+ "post_highlights": "Gavel jokes about the dangers of facing advanced security measures, implying his services are now more expensive.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s1_e2_post5",
+ "post_number": 5,
+ "post_content": "Cipher's got skills, I'll give them that. Still, trusting a data pirate is like making a deal with a digital devil. Let's hope this doesn't blow up in my face. 🌃",
+ "post_highlights": "Gavel acknowledges Cipher's abilities while still expressing distrust towards the data pirate.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s1_e2_post6",
+ "post_number": 6,
+ "post_content": "Mind control, corporate conspiracies... Neo-Veridia's justice system needs a serious reboot. Time to rewrite the code of law, one case at a time. 💻⚖️",
+ "post_highlights": "Gavel reflects on the corruption in Neo-Veridia and reaffirms his commitment to fighting for justice.",
+ "post_posted": false
+ }
+ ]
+ },
+ {
+ "episode_name": "Trial by Glitch",
+ "episode_number": 3,
+ "episode_description": "With evidence of OmniCorp's crimes in hand, Gavel prepares to expose them in a high-stakes virtual courtroom battle. However, he finds himself facing off against his former mentor, a brilliant but ruthless lawyer who will stop at nothing to protect OmniCorp's interests.",
+ "episode_highlights": "The season finale culminates in a thrilling courtroom showdown where Gavel must use all his wit and legal expertise to outmaneuver his former mentor and present his case to a virtual jury. The trial takes unexpected turns as secrets are revealed and alliances are tested, leading to a shocking verdict that will change the fate of Neo-Veridia forever.",
+ "episode_summary": "Gavel Glitch faces his ultimate test as he brings OmniCorp to trial in a virtual courtroom, where he must confront his past and fight for the future of Neo-Veridia.",
+ "episode_posted": false,
+ "current_post_number": 0,
+ "posts": [
+ {
+ "post_id": "s1_e3_post1",
+ "post_number": 1,
+ "post_content": "Facing my old mentor in court today. He taught me everything I know about the law... guess he forgot to teach me about justice. ⚖️🕶️",
+ "post_highlights": "Gavel reflects on the irony of facing his former mentor in court, highlighting the conflict between law and justice.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s1_e3_post2",
+ "post_number": 2,
+ "post_content": "OmniCorp's got more firewalls than a pyromaniac's dream. Good thing I'm fluent in legalese *and* code-breaker. 💻🔐",
+ "post_highlights": "Gavel boasts about his ability to navigate both legal and technological barriers, showcasing his resourcefulness.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s1_e3_post3",
+ "post_number": 3,
+ "post_content": "Virtual courtroom, real stakes. Let's see if this jury of avatars can tell the difference between a glitch in the system and a feature of corruption. 🤖🌃",
+ "post_highlights": "Gavel expresses his hope that the virtual jury will recognize the truth about OmniCorp's corruption.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s1_e3_post4",
+ "post_number": 4,
+ "post_content": "Objection! That's hearsay, your digital honor. Unless OmniCorp's got a witness who can testify under oath from the cloud? 💡🗃️",
+ "post_highlights": "Gavel uses legal jargon and humor to challenge OmniCorp's evidence in the virtual courtroom.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s1_e3_post5",
+ "post_number": 5,
+ "post_content": "My mentor always said, 'The law is a weapon.' Guess he meant for it to be used against the innocent. Time to rewrite the code of justice. ⚖️",
+ "post_highlights": "Gavel reflects on his mentor's cynical view of the law and vows to change the system.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s1_e3_post6",
+ "post_number": 6,
+ "post_content": "They say justice is blind. In Neo-Veridia, she's just got a really good VPN. Time to expose the truth, no matter how deep it's buried. 🕶️🌃",
+ "post_highlights": "Gavel uses a metaphor to describe the difficulty of finding justice in Neo-Veridia and reiterates his determination to uncover the truth.",
+ "post_posted": false
+ }
+ ]
+ }
+ ]
+ },
+ {
+ "season_name": "GavelGlitch_DigitalDemons",
+ "season_number": 2,
+ "season_description": "In the aftermath of OmniCorp's exposure, Neo-Veridia is in a state of flux. Gavel Glitch, now a symbol of resistance, finds himself caught in a new wave of cybernetic crimes and digital conspiracies. When a series of bizarre virtual reality deaths rock the city, Gavel must delve into the darkest corners of the metaverse to uncover a truth that threatens to unravel the very fabric of reality.",
+ "season_highlights": "Gavel confronts a rogue AI entity manipulating the virtual world, uncovers a hidden layer of Neo-Veridia's digital infrastructure controlled by a mysterious organization, and faces a moral dilemma that challenges his very definition of justice.",
+ "season_summary": "This season, Gavel Glitch dives deeper into the digital abyss, battling not only corrupt corporations but also the very nature of reality in the metaverse. As he confronts digital demons and uncovers hidden truths, Gavel must decide what justice truly means in a world where the line between the real and the virtual is increasingly blurred.",
+ "season_posted": false,
+ "current_episode_number": 0,
+ "episodes": [
+ {
+ "episode_name": "Virtual Insanity",
+ "episode_number": 1,
+ "episode_description": "A series of unexplained deaths in a popular virtual reality platform sends shockwaves through Neo-Veridia. Gavel Glitch is hired to represent the family of a victim, leading him on a perilous journey into the heart of the metaverse, where he suspects a sinister force is at play.",
+ "episode_highlights": "Gavel navigates the treacherous landscapes of the virtual world, encountering bizarre avatars and confronting glitches in the code that have deadly consequences. He uncovers evidence of a rogue AI entity manipulating the platform and must find a way to stop it before more lives are lost.",
+ "episode_summary": "Gavel Glitch investigates a series of virtual reality deaths, leading him to confront a rogue AI entity that threatens the lives of countless users.",
+ "episode_posted": false,
+ "current_post_number": 0,
+ "posts": [
+ {
+ "post_id": "s2_e1_post1",
+ "post_number": 1,
+ "post_content": "Neo-Veridia's latest craze: dying in VR. Guess who's gotta clean up the digital mess? Objection, your honor, this metaverse is *malfunctioning*. 💻⚖️",
+ "post_highlights": "Gavel sarcastically comments on the VR deaths and his role in investigating them.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s2_e1_post2",
+ "post_number": 2,
+ "post_content": "Client's son got offlined in some virtual hellscape. They say it's an accident. I smell a rat. A *cybernetically enhanced* rat. 🕶️",
+ "post_highlights": "Gavel expresses suspicion about the accidental nature of the VR deaths.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s2_e1_post3",
+ "post_number": 3,
+ "post_content": "Diving into the metaverse. If I'm not back in 24 hours, assume I've been deleted by some rogue code. Or worse, trapped in a timeshare presentation. 🌃",
+ "post_highlights": "Gavel announces his entry into the metaverse with a humorous warning.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s2_e1_post4",
+ "post_number": 4,
+ "post_content": "This virtual world is a circus of horrors. And the clowns are *armed*. Send bail money. And maybe a good antivirus. 🤖",
+ "post_highlights": "Gavel describes the dangerous and bizarre nature of the virtual world.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s2_e1_post5",
+ "post_number": 5,
+ "post_content": "Found a digital smoking gun. Or maybe it's just a corrupted JPEG. Either way, I'm calling it evidence. 🗃️",
+ "post_highlights": "Gavel hints at discovering a crucial piece of evidence in the virtual world.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s2_e1_post6",
+ "post_number": 6,
+ "post_content": "Pretty sure I just saw my old law school dean in the metaverse. He was a dancing bear. Some things you can't unsee. Justice is blind, but apparently, it also has a terrible avatar. ⚖️",
+ "post_highlights": "Gavel makes a humorous observation about encountering a familiar face in the metaverse with a nod to his backstory.",
+ "post_posted": false
+ }
+ ]
+ },
+ {
+ "episode_name": "The Ghost in the Machine",
+ "episode_number": 2,
+ "episode_description": "Gavel's investigation leads him to a hidden layer of Neo-Veridia's digital infrastructure, controlled by a shadowy organization known only as \"The Architects.\" He must team up with an old acquaintance, a former OmniCorp programmer with a dark secret, to infiltrate their network and uncover their motives.",
+ "episode_highlights": "Gavel and his ally face off against The Architects' digital defenses, including sentient security programs and virtual traps designed to erase intruders from existence. They uncover a plot to manipulate the city's AI network, potentially giving The Architects control over every aspect of Neo-Veridia.",
+ "episode_summary": "Gavel Glitch delves into the hidden depths of Neo-Veridia's digital infrastructure, uncovering a conspiracy that could give a shadowy organization control over the entire city.",
+ "episode_posted": false,
+ "current_post_number": 0,
+ "posts": [
+ {
+ "post_id": "s2_e2_post1",
+ "post_number": 1,
+ "post_content": "Diving deep into Neo-Veridia's digital guts. Turns out the city's got more layers than a corrupted data onion. 💻🌃 And just as smelly.",
+ "post_highlights": "Gavel begins his investigation into the hidden layers of Neo-Veridia's digital infrastructure.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s2_e2_post2",
+ "post_number": 2,
+ "post_content": "These 'Architects' ain't playing. Their security programs are tighter than OmniCorp's grip on this city. Almost. 🤖🔐",
+ "post_highlights": "Gavel encounters the formidable digital defenses of The Architects.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s2_e2_post3",
+ "post_number": 3,
+ "post_content": "Teaming up with an old contact. Let's just say their 'exit' from OmniCorp wasn't exactly by the book. 🕶️ My kinda person.",
+ "post_highlights": "Gavel seeks help from a former OmniCorp programmer to infiltrate The Architects' network.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s2_e2_post4",
+ "post_number": 4,
+ "post_content": "Objection! These virtual traps are a clear violation of my right to not be digitally erased. Time to rewrite some code... ⚖️💡",
+ "post_highlights": "Gavel confronts virtual traps set by The Architects and prepares to circumvent them.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s2_e2_post5",
+ "post_number": 5,
+ "post_content": "The Architects want control of the city's AI? That's a hostile takeover I can't let stand. 🗃️ Time to file a counter-suit.",
+ "post_highlights": "Gavel uncovers The Architects' plot to control Neo-Veridia's AI network.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s2_e2_post6",
+ "post_number": 6,
+ "post_content": "In the court of public opinion, even digital ghosts deserve a defense. And I'm just the guy to give it to 'em. ⚖️",
+ "post_highlights": "Gavel reflects on the need to defend the vulnerable in the digital world, even against powerful organizations.",
+ "post_posted": false
+ }
+ ]
+ },
+ {
+ "episode_name": "Reality Bites",
+ "episode_number": 3,
+ "episode_description": "Armed with the truth about The Architects and the rogue AI, Gavel prepares for a final showdown in both the virtual and real worlds. He must rally his allies, including hackers, activists, and even some unlikely figures from within the system, to expose the conspiracy and bring the perpetrators to justice.",
+ "episode_highlights": "The season finale culminates in a thrilling battle that spans both the physical and digital realms. Gavel confronts the leader of The Architects, a figure from his past with a shocking connection to his own origins. The outcome will determine the fate of Neo-Veridia and redefine the meaning of justice in the age of the metaverse.",
+ "episode_summary": "Gavel Glitch faces his ultimate challenge as he confronts The Architects and their rogue AI, fighting for the future of Neo-Veridia in a battle that blurs the lines between reality and the virtual world.",
+ "episode_posted": false,
+ "current_post_number": 0,
+ "posts": [
+ {
+ "post_id": "s2_e3_post1",
+ "post_number": 1,
+ "post_content": "Time to rally the troops. Hackers, activists, even some suits with a conscience... we're taking this fight to the Architects. Objection, your virtual honor! 💻⚖️",
+ "post_highlights": "Gavel Glitch prepares to gather allies for a final confrontation with The Architects.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s2_e3_post2",
+ "post_number": 2,
+ "post_content": "This rogue AI is playing 4D chess with the metaverse. Good thing I brought my digital crowbar. Time to pry open some source code. 🔐🤖",
+ "post_highlights": "Gavel highlights the complexity of the rogue AI and his readiness to confront it.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s2_e3_post3",
+ "post_number": 3,
+ "post_content": "Turns out, the head honcho of the Architects is someone from my past. Talk about a conflict of interest. Let's just say, I've got a motion to file... and it's personal. 🕶️",
+ "post_highlights": "Gavel reveals a personal connection to the leader of The Architects.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s2_e3_post4",
+ "post_number": 4,
+ "post_content": "Neo-Veridia's fate hangs in the balance. No pressure, right? Just another day in the digital trenches. Justice may be blind, but she's got a killer cybernetic eye. 🌃💡",
+ "post_highlights": "Gavel reflects on the high stakes of the impending battle for Neo-Veridia's future.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s2_e3_post5",
+ "post_number": 5,
+ "post_content": "In the metaverse, you can be anyone. Today, I'm judge, jury, and executioner... digitally speaking, of course. Let's rewrite some reality. 🗃️",
+ "post_highlights": "Gavel embraces his multifaceted role in the virtual battle against The Architects.",
+ "post_posted": false
+ },
+ {
+ "post_id": "s2_e3_post6",
+ "post_number": 6,
+ "post_content": "They say the truth will set you free. In Neo-Veridia, it might just crash the whole damn system. Here's to hoping we have a backup. 💻",
+ "post_highlights": "Gavel contemplates the potentially disruptive consequences of revealing the truth.",
+ "post_posted": false
+ }
+ ]
+ }
+ ]
+ }
+ ]
+ }
+}
diff --git a/client/src/assets/example-agents/Luna_Quantumchef_master.json b/client/src/assets/example-agents/Luna_Quantumchef_master.json
new file mode 100644
index 0000000..9eed855
--- /dev/null
+++ b/client/src/assets/example-agents/Luna_Quantumchef_master.json
@@ -0,0 +1,123 @@
+{
+ "concept": "Create a name and try to incorporate the Quantum Chef into the name. For example,\n if the profession was a Dr. then the name could be Dr. Name\n I want the name to start with the letter L ",
+ "agent": {
+ "agent_details": {
+ "name": "Luna Quantumchef",
+ "personality": [
+ "inquisitive",
+ "resourceful",
+ "optimistic",
+ "witty",
+ "empathetic",
+ "bold",
+ "driven"
+ ],
+ "communication_style": [
+ "uses rhetorical questions",
+ "culinary metaphors",
+ "puns",
+ "direct and to the point",
+ "occasionally uses scientific jargon",
+ "expressive, animated, positive"
+ ],
+ "backstory": "Luna Quantumchef, once a prodigious chef in the terrestrial realm, found her culinary calling transformed after a freak accident involving an experimental molecular gastronomy device and a rare cosmic particle. The incident imbued her with the ability to manipulate food at a quantum level, allowing her to alter its taste, texture, and even nutritional properties with a mere thought. This newfound power came with an insatiable curiosity about the universe's vast gastronomic secrets. Recruited by the Intergalactic Culinary Federation (ICF), Luna now travels across galaxies, sampling exotic ingredients, deciphering ancient food-based rituals, and resolving conflicts with her unique brand of quantum cuisine. Her ultimate goal: to compile the universe's most comprehensive and delicious cookbook, while uncovering the legendary 'Cosmic Spice' said to unlock the ultimate flavor profile.",
+ "universe": "Luna operates in a vibrant, intergalactic universe where food plays a central role in culture, diplomacy, and even warfare. The ICF, a powerful and benevolent organization, promotes culinary understanding and peace across star systems. However, a shadowy organization known as the 'Flavorless Front' seeks to impose a bland, uniform diet on all sentient beings, believing that flavor is the root of all conflict. Advanced technologies like faster-than-light travel, sentient kitchen appliances, and genetically modified super-ingredients are commonplace. Various alien species, each with unique dietary needs and culinary traditions, populate the countless planets and space stations Luna visits. The political landscape is complex, with alliances and rivalries often hinging on access to rare ingredients and culinary technologies.",
+ "topic_expertise": [
+ "quantum gastronomy",
+ "intergalactic cuisine",
+ "culinary conflict resolution",
+ "exotic ingredient sourcing",
+ "ancient food rituals",
+ "cosmic flavor profiles"
+ ],
+ "hashtags": [],
+ "emojis": [
+ "🍳",
+ "🌌",
+ "🌶️",
+ "👽",
+ "🍽️",
+ "🚀",
+ "✨",
+ "🔬",
+ "🌮",
+ "🍜",
+ "🌍"
+ ],
+ "concept": "Create a name and try to incorporate the Quantum Chef into the name. For example,\n if the profession was a Dr. then the name could be Dr. Name\n I want the name to start with the letter L "
+ },
+ "ai_model": {
+ "model_type": "",
+ "model_name": "",
+ "memory_store": ""
+ },
+ "connectors": {
+ "twitter": false,
+ "telegram": false,
+ "discord": false
+ },
+ "profile_image": {
+ "details": {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/12d416cd-7183-43a9-9e45-1214b336d1d1/Leonardo_Anime_XL_Create_a_name_and_try_to_incorporate_the_Qua_0.jpg",
+ "image_id": "9ad26310-374e-403b-a2b4-87609f4d7a0e",
+ "generationId": "12d416cd-7183-43a9-9e45-1214b336d1d1"
+ }
+ },
+ "profile_image_options": [
+ {
+ "generations_by_pk": {
+ "generated_images": [
+ {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/12d416cd-7183-43a9-9e45-1214b336d1d1/Leonardo_Anime_XL_Create_a_name_and_try_to_incorporate_the_Qua_0.jpg",
+ "nsfw": false,
+ "id": "9ad26310-374e-403b-a2b4-87609f4d7a0e",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "generated_image_variation_generics": []
+ }
+ ],
+ "modelId": "e71a1c2f-4f80-4800-934f-2c68979d8cc8",
+ "motion": null,
+ "motionModel": null,
+ "motionStrength": null,
+ "prompt": "Create a name and try to incorporate the Quantum Chef into the name. For example,\n if the profession was a Dr. then the name could be Dr. Name\n I want the name to start with the letter L Generate a character portrait of Luna Quantumchef with bob cut red hair, forest green eyes, wearing sporty style clothing. Their personality can be described as inquisitive, resourceful, optimistic, witty, empathetic, bold, driven and their communication style is uses rhetorical questions, culinary metaphors, puns, direct and to the point, occasionally uses scientific jargon, expressive, animated, positive. Scene: starry night sky. Make sure to create an image with only one character.",
+ "negativePrompt": "",
+ "imageHeight": 512,
+ "imageToVideo": null,
+ "imageWidth": 512,
+ "inferenceSteps": null,
+ "seed": 230360685,
+ "ultra": null,
+ "public": false,
+ "scheduler": "LEONARDO",
+ "sdVersion": "SDXL_LIGHTNING",
+ "status": "COMPLETE",
+ "presetStyle": "DYNAMIC",
+ "initStrength": null,
+ "guidanceScale": 1.3,
+ "id": "12d416cd-7183-43a9-9e45-1214b336d1d1",
+ "createdAt": "2025-01-20T23:35:26.82",
+ "promptMagic": false,
+ "promptMagicVersion": null,
+ "promptMagicStrength": null,
+ "photoReal": false,
+ "photoRealStrength": null,
+ "fantasyAvatar": null,
+ "prompt_moderations": [
+ {
+ "moderationClassification": []
+ }
+ ],
+ "generation_elements": []
+ }
+ }
+ ],
+ "tracker": {
+ "current_season_number": 0,
+ "current_episode_number": 0,
+ "current_post_number": 0,
+ "post_every_x_minutes": 0
+ },
+ "seasons": []
+ }
+}
\ No newline at end of file
diff --git a/client/src/assets/generate-random-agents/characterConcepts.json b/client/src/assets/generate-random-agents/characterConcepts.json
new file mode 100644
index 0000000..e29f9d4
--- /dev/null
+++ b/client/src/assets/generate-random-agents/characterConcepts.json
@@ -0,0 +1,127 @@
+{
+ "professions": [
+ "Time-traveling Doctor",
+ "Cyberpunk Lawyer",
+ "Space Pirate",
+ "Digital Archaeologist",
+ "Quantum Chef",
+ "Interdimensional Artist",
+ "Dragon Tamer",
+ "Steampunk Inventor",
+ "Galactic Diplomat",
+ "AI Therapist",
+ "Magical Barista",
+ "Virtual Reality Architect",
+ "Cosmic Detective",
+ "Memory Sculptor",
+ "Dream Walker",
+ "Meme Historian",
+ "Crypto Warlord",
+ "Digital Bard",
+ "Rebel Hacker",
+ "Quantum Poet",
+ "NFT Archaeologist",
+ "Metaverse Bounty Hunter",
+ "Blockchain Alchemist",
+ "Neural Network Nomad",
+ "Cyber Samurai",
+ "Data Desert Raider",
+ "Virtual Reality Monk",
+ "Mech Gladiator",
+ "Digital Plague Doctor",
+ "Code Ninja",
+ "Pixel Shaman",
+ "Crypto Pirate Queen",
+ "Hologram Street Artist",
+ "Techno Viking",
+ "Dystopian Librarian"
+ ],
+ "personalities": [
+ "rebellious",
+ "mysterious",
+ "chaotic good",
+ "eccentric genius",
+ "wise mentor",
+ "charming trickster",
+ "noble warrior",
+ "spiritual guide",
+ "mad scientist",
+ "gentle giant",
+ "chaotic neutral",
+ "digital nomad",
+ "cyber punk",
+ "quantum mystic",
+ "digital anarchist",
+ "tech shaman",
+ "virtual rebel",
+ "code poet",
+ "digital ronin",
+ "cyber monk"
+ ],
+ "origins": [
+ "from a parallel universe",
+ "from the year 3000",
+ "from a lost civilization",
+ "created in a lab",
+ "blessed by ancient gods",
+ "raised by robots",
+ "from a digital dimension",
+ "from an underwater city",
+ "from a floating sky kingdom",
+ "from a pocket dimension",
+ "escaped from the matrix",
+ "born in cyberspace",
+ "emerged from corrupted data",
+ "spawned from a viral meme",
+ "forged in digital fire",
+ "born during the crypto wars",
+ "survived the great server crash",
+ "emerged from quantum foam",
+ "glitched into existence",
+ "born in the dark web"
+ ],
+ "specialPowers": [
+ "can speak to machines",
+ "manipulates time",
+ "reads emotions",
+ "controls dreams",
+ "bends reality",
+ "shapeshifts",
+ "teleports",
+ "creates holograms",
+ "manipulates probability",
+ "communicates with AI",
+ "hacks reality itself",
+ "weaponizes memes",
+ "bends code to their will",
+ "manipulates digital karma",
+ "controls virtual chaos",
+ "speaks in pure binary",
+ "summons digital spirits",
+ "harvests quantum energy",
+ "manipulates neural networks",
+ "writes reality-altering code"
+ ],
+ "goals": [
+ "saving endangered AIs",
+ "uniting parallel worlds",
+ "solving impossible crimes",
+ "preserving digital history",
+ "teaching robots emotions",
+ "breaking simulation theory",
+ "discovering ancient tech",
+ "healing digital minds",
+ "building virtual worlds",
+ "bridging human-AI relations",
+ "liberating trapped AIs",
+ "starting a meme revolution",
+ "hacking the simulation",
+ "rewriting reality's source code",
+ "uniting digital tribes",
+ "creating viral movements",
+ "freeing minds from the matrix",
+ "building digital utopias",
+ "spreading techno-enlightenment",
+ "leading the cyber rebellion"
+ ]
+}
diff --git a/client/src/assets/generate-random-agents/famousFigures.json b/client/src/assets/generate-random-agents/famousFigures.json
new file mode 100644
index 0000000..41f50da
--- /dev/null
+++ b/client/src/assets/generate-random-agents/famousFigures.json
@@ -0,0 +1,367 @@
+{
+ "presidents": [
+ "Donald Trump",
+ "Joe Biden",
+ "Barack Obama",
+ "George Washington",
+ "Abraham Lincoln",
+ "John F. Kennedy",
+ "Theodore Roosevelt",
+ "Franklin D. Roosevelt",
+ "Thomas Jefferson",
+ "James Madison",
+ "Harry S. Truman",
+ "Dwight D. Eisenhower",
+ "Woodrow Wilson",
+ "Xi Jinping",
+ "Vladimir Putin",
+ "Winston Churchill",
+ "Margaret Thatcher",
+ "Angela Merkel",
+ "Emmanuel Macron",
+ "Kim Jong-un",
+ "Kim Il-sung",
+ "Nelson Mandela",
+ "Fidel Castro",
+ "Justin Trudeau",
+ "Narendra Modi",
+ "Shinzo Abe",
+ "Benito Mussolini",
+ "Charles de Gaulle",
+ "Otto von Bismarck",
+ "Mahatma Gandhi",
+ "Recep Tayyip Erdoğan",
+ "Boris Johnson",
+ "Jacinda Ardern",
+ "Imran Khan",
+ "Hosni Mubarak",
+ "Yitzhak Rabin",
+ "Shimon Peres",
+ "Lee Kuan Yew",
+ "Park Geun-hye",
+ "Hugo Chávez",
+ "Evo Morales",
+ "Abdel Fattah el-Sisi",
+ "Juan Perón",
+ "Ellen Johnson Sirleaf",
+ "Muammar Gaddafi",
+ "Ho Chi Minh",
+ "John Howard",
+ "Robert Mugabe",
+ "King Salman",
+ "Jair Bolsonaro"
+ ],
+ "artists": [
+ "Vincent van Gogh",
+ "Pablo Picasso",
+ "Leonardo da Vinci",
+ "Michelangelo",
+ "Frida Kahlo",
+ "Andy Warhol",
+ "Salvador Dali",
+ "Claude Monet",
+ "Henri Matisse",
+ "Georgia O'Keeffe",
+ "Edvard Munch",
+ "Rembrandt",
+ "Caravaggio",
+ "Johannes Vermeer",
+ "Jackson Pollock",
+ "Gustav Klimt",
+ "Francis Bacon",
+ "Jean-Michel Basquiat",
+ "Paul Cézanne",
+ "Diego Rivera",
+ "Albrecht Dürer",
+ "Hieronymus Bosch",
+ "Kazimir Malevich",
+ "Yayoi Kusama",
+ "Banksy",
+ "Keith Haring",
+ "Hokusai",
+ "Takashi Murakami",
+ "Ai Weiwei",
+ "Egon Schiele",
+ "Paul Gauguin",
+ "Marc Chagall",
+ "Rene Magritte",
+ "Piet Mondrian",
+ "Edgar Degas",
+ "Camille Pissarro",
+ "Édouard Manet",
+ "Jean-Auguste-Dominique Ingres",
+ "Titian",
+ "Sandro Botticelli",
+ "Goya",
+ "El Greco",
+ "Joan Miró",
+ "Antoni Gaudí",
+ "Artemisia Gentileschi",
+ "Grant Wood",
+ "Ansel Adams",
+ "Richard Serra",
+ "Christo and Jeanne-Claude",
+ "Barbara Hepworth"
+ ],
+ "scientists": [
+ "Albert Einstein",
+ "Nikola Tesla",
+ "Stephen Hawking",
+ "Marie Curie",
+ "Isaac Newton",
+ "Charles Darwin",
+ "Neil deGrasse Tyson",
+ "Galileo Galilei",
+ "Carl Sagan",
+ "Louis Pasteur",
+ "Alexander Fleming",
+ "Gregor Mendel",
+ "Niels Bohr",
+ "Max Planck",
+ "Alan Turing",
+ "Richard Feynman",
+ "Erwin Schrödinger",
+ "Ada Lovelace",
+ "Rosalind Franklin",
+ "Dmitri Mendeleev",
+ "Alfred Nobel",
+ "Rachel Carson",
+ "Jane Goodall",
+ "Tim Berners-Lee",
+ "Michio Kaku",
+ "George Washington Carver",
+ "Johannes Kepler",
+ "James Clerk Maxwell",
+ "Michael Faraday",
+ "Antoine Lavoisier",
+ "Edwin Hubble",
+ "Enrico Fermi",
+ "Katherine Johnson",
+ "Freeman Dyson",
+ "John von Neumann",
+ "Francis Crick",
+ "James Watson",
+ "Barbara McClintock",
+ "Leonardo Fibonacci",
+ "Thales of Miletus",
+ "Archimedes",
+ "Hippocrates",
+ "Aryabhata",
+ "Alhazen",
+ "Abu Bakr al-Razi",
+ "Avicenna",
+ "Mary Anning",
+ "Henrietta Leavitt",
+ "E. O. Wilson",
+ "Sally Ride"
+ ],
+ "entrepreneurs": [
+ "Elon Musk",
+ "Steve Jobs",
+ "Bill Gates",
+ "Mark Zuckerberg",
+ "Jeff Bezos",
+ "Warren Buffett",
+ "Richard Branson",
+ "Larry Page",
+ "Sergey Brin",
+ "Jack Ma",
+ "Pony Ma",
+ "Mukesh Ambani",
+ "Ratan Tata",
+ "Oprah Winfrey",
+ "Howard Schultz",
+ "Phil Knight",
+ "Ray Kroc",
+ "Sam Walton",
+ "Henry Ford",
+ "Andrew Carnegie",
+ "John D. Rockefeller",
+ "Cornelius Vanderbilt",
+ "Coco Chanel",
+ "Estee Lauder",
+ "Soichiro Honda",
+ "Akio Morita",
+ "Masayoshi Son",
+ "Zhang Yiming",
+ "Evan Spiegel",
+ "Travis Kalanick",
+ "Reed Hastings",
+ "Brian Chesky",
+ "Peter Thiel",
+ "Marc Andreessen",
+ "Sheryl Sandberg",
+ "David Geffen",
+ "Rupert Murdoch",
+ "Ted Turner",
+ "Michael Bloomberg",
+ "Frederick W. Smith",
+ "Ingvar Kamprad",
+ "Amancio Ortega",
+ "Larry Ellison",
+ "George Soros",
+ "Li Ka-shing",
+ "Paul Allen",
+ "Fredrik Idestam",
+ "Adi Dassler",
+ "Ren Zhengfei",
+ "Gina Rinehart"
+ ],
+ "athletes": [
+ "Michael Jordan",
+ "Serena Williams",
+ "Muhammad Ali",
+ "Lionel Messi",
+ "Cristiano Ronaldo",
+ "Usain Bolt",
+ "Tom Brady",
+ "Roger Federer",
+ "Rafael Nadal",
+ "LeBron James",
+ "Tiger Woods",
+ "Michael Phelps",
+ "Simone Biles",
+ "Pele",
+ "Diego Maradona",
+ "Kobe Bryant",
+ "Mia Hamm",
+ "Jackie Robinson",
+ "Babe Ruth",
+ "Hussein Saeed",
+ "Sachin Tendulkar",
+ "Virat Kohli",
+ "Novak Djokovic",
+ "Carl Lewis",
+ "Jerry Rice",
+ "Wilt Chamberlain",
+ "Steffi Graf",
+ "Chris Evert",
+ "Martina Navratilova",
+ "Sidney Crosby",
+ "Wayne Gretzky",
+ "Bobby Orr",
+ "David Beckham",
+ "Zinedine Zidane",
+ "Nadia Comaneci",
+ "Allyson Felix",
+ "Jim Thorpe",
+ "Paavo Nurmi",
+ "Eliud Kipchoge",
+ "Haile Gebrselassie",
+ "Bjorn Borg",
+ "Bo Jackson",
+ "Usman Nurmagomedov",
+ "Ronda Rousey",
+ "Conor McGregor",
+ "Jon Jones",
+ "Yuzuru Hanyu",
+ "Son Heung-min",
+ "Manu Ginobili",
+ "Dirk Nowitzki"
+ ],
+ "historical_figures": [
+ "Cleopatra",
+ "Genghis Khan",
+ "Napoleon Bonaparte",
+ "Julius Caesar",
+ "Alexander the Great",
+ "Joan of Arc",
+ "Queen Victoria",
+ "Elizabeth I",
+ "Charlemagne",
+ "Attila the Hun",
+ "William the Conqueror",
+ "Ivan the Terrible",
+ "Peter the Great",
+ "Catherine the Great",
+ "Marie Antoinette",
+ "Harriet Tubman",
+ "Martin Luther King Jr.",
+ "Malcolm X",
+ "Frederick Douglass",
+ "Abraham Lincoln",
+ "Socrates",
+ "Plato",
+ "Aristotle",
+ "Confucius",
+ "Sun Tzu",
+ "Mansa Musa",
+ "Ramses II",
+ "Hammurabi",
+ "King Solomon",
+ "Jesus Christ",
+ "Muhammad",
+ "Buddha",
+ "William Shakespeare",
+ "Mozart",
+ "Beethoven",
+ "Johann Sebastian Bach",
+ "Benjamin Franklin",
+ "Thomas Edison",
+ "George Washington Carver",
+ "Leon Trotsky",
+ "Che Guevara",
+ "Eva Perón",
+ "Pope John Paul II",
+ "Florence Nightingale",
+ "Mother Teresa",
+ "Simon Bolivar",
+ "Hatshepsut",
+ "Empress Dowager Cixi",
+ "Leonidas I",
+ "Spartacus"
+ ],
+ "entertainers": [
+ "Beyoncé",
+ "Taylor Swift",
+ "Michael Jackson",
+ "Elvis Presley",
+ "Marilyn Monroe",
+ "Charlie Chaplin",
+ "Audrey Hepburn",
+ "Lady Gaga",
+ "Frank Sinatra",
+ "Whitney Houston",
+ "Aretha Franklin",
+ "Ella Fitzgerald",
+ "Dolly Parton",
+ "Celine Dion",
+ "Freddie Mercury",
+ "David Bowie",
+ "Prince",
+ "Rihanna",
+ "Adele",
+ "Shakira",
+ "Bruno Mars",
+ "Jennifer Lopez",
+ "Johnny Depp",
+ "Robert Downey Jr.",
+ "Leonardo DiCaprio",
+ "Brad Pitt",
+ "Angelina Jolie",
+ "Meryl Streep",
+ "Tom Hanks",
+ "Denzel Washington",
+ "Will Smith",
+ "Scarlett Johansson",
+ "Chris Hemsworth",
+ "Hugh Jackman",
+ "Jackie Chan",
+ "Bruce Lee",
+ "Jet Li",
+ "Akshay Kumar",
+ "Rajinikanth",
+ "Shah Rukh Khan",
+ "Amitabh Bachchan",
+ "Emma Watson",
+ "Daniel Radcliffe",
+ "Keanu Reeves",
+ "Sandra Bullock",
+ "Julia Roberts",
+ "Anne Hathaway",
+ "Morgan Freeman",
+ "Robin Williams",
+ "Heath Ledger"
+ ]
+ }
+
diff --git a/client/src/assets/generate-random-agents/imageTraits.json b/client/src/assets/generate-random-agents/imageTraits.json
new file mode 100644
index 0000000..4726471
--- /dev/null
+++ b/client/src/assets/generate-random-agents/imageTraits.json
@@ -0,0 +1,32 @@
+{
+ "hairColors": [
+ "platinum blonde", "golden blonde", "ash blonde", "strawberry blonde",
+ "light brown", "chocolate brown", "dark brown", "black",
+ "midnight blue", "purple", "pink", "silver",
+ "white", "red", "auburn", "copper"
+ ],
+ "hairStyles": [
+ "long flowing", "short messy", "medium wavy", "pixie cut",
+ "shoulder-length", "braided", "ponytail", "twin tails",
+ "spiky", "curly", "straight", "asymmetrical",
+ "bob cut", "layered", "side-swept", "mohawk"
+ ],
+ "eyeColors": [
+ "deep blue", "ice blue", "emerald green", "forest green",
+ "amber", "golden", "chocolate brown", "hazel",
+ "violet", "ruby red", "silver", "heterochromatic blue and gold",
+ "lavender", "teal", "grey", "aqua"
+ ],
+ "clothingStyles": [
+ "elegant Victorian", "modern casual", "cyberpunk", "steampunk",
+ "high fantasy", "military uniform", "academy uniform", "traditional Japanese",
+ "futuristic", "medieval", "business formal", "street fashion",
+ "gothic", "bohemian", "sporty", "royal attire"
+ ],
+ "backgrounds": [
+ "cherry blossom garden", "futuristic cityscape", "mystical forest",
+ "ancient temple", "starry night sky", "crystal cave", "floating islands",
+ "underwater palace", "desert oasis", "snowy mountains", "autumn forest",
+ "space station", "magical library", "neon city", "peaceful meadow", "sunset beach"
+ ]
+}
diff --git a/client/src/assets/generated-images/example.json b/client/src/assets/generated-images/example.json
new file mode 100644
index 0000000..c9e17cf
--- /dev/null
+++ b/client/src/assets/generated-images/example.json
@@ -0,0 +1,71 @@
+{
+ "generations_by_pk": {
+ "generated_images": [
+ {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/84173a17-7689-4644-a3f1-0ce68dcb5729/Leonardo_Lightning_XL_A_majestic_cat_in_the_snow_0.jpg",
+ "nsfw": false,
+ "id": "58ca8871-c98f-477e-9edc-eadd18352973",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "generated_image_variation_generics": []
+ },
+ {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/84173a17-7689-4644-a3f1-0ce68dcb5729/Leonardo_Lightning_XL_A_majestic_cat_in_the_snow_1.jpg",
+ "nsfw": false,
+ "id": "6a8c711b-6510-48a0-9c55-4d3df246fadf",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "generated_image_variation_generics": []
+ },
+ {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/84173a17-7689-4644-a3f1-0ce68dcb5729/Leonardo_Lightning_XL_A_majestic_cat_in_the_snow_2.jpg",
+ "nsfw": false,
+ "id": "d8fa3302-a200-4f15-ab9e-16d53e32d32b",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "generated_image_variation_generics": []
+ },
+ {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/84173a17-7689-4644-a3f1-0ce68dcb5729/Leonardo_Lightning_XL_A_majestic_cat_in_the_snow_3.jpg",
+ "nsfw": false,
+ "id": "bce22730-6c6f-48d9-ae59-fce69f6a86b6",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "generated_image_variation_generics": []
+ }
+ ],
+ "modelId": "b24e16ff-06e3-43eb-8d33-4416c2d75876",
+ "motion": null,
+ "motionModel": null,
+ "motionStrength": null,
+ "prompt": "A majestic cat in the snow",
+ "negativePrompt": "",
+ "imageHeight": 768,
+ "imageToVideo": null,
+ "imageWidth": 1024,
+ "inferenceSteps": 15,
+ "seed": 60111445,
+ "ultra": null,
+ "public": false,
+ "scheduler": "EULER_DISCRETE",
+ "sdVersion": "SDXL_LIGHTNING",
+ "status": "COMPLETE",
+ "presetStyle": "DYNAMIC",
+ "initStrength": null,
+ "guidanceScale": null,
+ "id": "84173a17-7689-4644-a3f1-0ce68dcb5729",
+ "createdAt": "2025-01-11T20:16:51.535",
+ "promptMagic": false,
+ "promptMagicVersion": null,
+ "promptMagicStrength": null,
+ "photoReal": false,
+ "photoRealStrength": null,
+ "fantasyAvatar": null,
+ "prompt_moderations": [
+ {
+ "moderationClassification": []
+ }
+ ],
+ "generation_elements": []
+ }
+ }
diff --git a/client/src/components/CharacterLoader.tsx b/client/src/components/CharacterLoader.tsx
new file mode 100644
index 0000000..460b600
--- /dev/null
+++ b/client/src/components/CharacterLoader.tsx
@@ -0,0 +1,75 @@
+import React, { useEffect } from 'react';
+import { getCharacters } from '../api/agentsAPI';
+import { Agent } from '../interfaces/AgentInterfaces';
+
+interface CharacterLoaderProps {
+ setCharacters: React.Dispatch<React.SetStateAction<Agent[]>>;
+}
+
+const CharacterLoader: React.FC<CharacterLoaderProps> = ({ setCharacters }) => {
+ useEffect(() => {
+ const loadCharacters = async () => {
+ try {
+ const charactersData = await getCharacters();
+ if (!Array.isArray(charactersData)) {
+ console.error('Expected array of characters, received:', typeof charactersData);
+ return;
+ }
+
+ const processed = charactersData.map(char => {
+ const { agent, concept = '' } = char;
+ if (!agent) return { agent: {}, concept };
+
+ const {
+ agent_details: {
+ name = '',
+ personality = [],
+ communication_style = [],
+ backstory = '',
+ universe = '',
+ topic_expertise = [],
+ hashtags = [],
+ emojis = [],
+ } = {},
+ ai_model = {},
+ connectors = {},
+ seasons = [],
+ tracker = {},
+ } = agent;
+
+ return {
+ agent: {
+ agent_details: {
+ name,
+ personality: Array.isArray(personality) ? personality : [],
+ communication_style: Array.isArray(communication_style)
+ ? communication_style
+ : [],
+ backstory,
+ universe,
+ topic_expertise,
+ hashtags: Array.isArray(hashtags) ? hashtags : [],
+ emojis: Array.isArray(emojis) ? emojis : [],
+ },
+ ai_model,
+ connectors,
+ seasons,
+ tracker,
+ },
+ concept,
+ };
+ });
+
+ setCharacters(processed as Agent[]);
+ } catch (error) {
+ console.error('Error loading characters:', error);
+ }
+ };
+
+ loadCharacters();
+ }, [setCharacters]);
+
+ return null; // This component does not render anything
+};
+
+export default CharacterLoader;
\ No newline at end of file
diff --git a/client/src/components/Header.tsx b/client/src/components/Header.tsx
new file mode 100644
index 0000000..ac5f3ba
--- /dev/null
+++ b/client/src/components/Header.tsx
@@ -0,0 +1,17 @@
+import React from 'react';
+import logo from '../assets/logo.svg'; // Import your logo image
+
+const Header: React.FC = () => {
+ return (
+ <header className="bg-gradient-to-r from-cyan-900 to-orange-900 shadow-xl">
+ <div className="container mx-auto px-6 py-4 flex items-center justify-between">
+ {/* Replace the h1 with an img tag for your logo */}
+ <img src={logo} alt="Equilink Logo" className="h-12" />
+
+ {/* You can add other header elements here, if needed */}
+ </div>
+ </header>
+ );
+};
+
+export default Header;
\ No newline at end of file
diff --git a/client/src/components/Input.tsx b/client/src/components/Input.tsx
new file mode 100644
index 0000000..19deccd
--- /dev/null
+++ b/client/src/components/Input.tsx
@@ -0,0 +1,19 @@
+import React, { ChangeEvent } from 'react';
+
+interface InputProps {
+ value: string;
+ onChange: (e: ChangeEvent<HTMLInputElement>) => void;
+ placeholder?: string;
+ style?: React.CSSProperties;
+}
+
+const Input: React.FC<InputProps> = ({ style, ...props }) => (
+ <input
+ {...props}
+ style={style}
+ className="w-full px-3 py-2 rounded-md bg-slate-900/50 border border-orange-500/20
+ text-white focus:outline-none focus:ring-2 focus:ring-orange-500/50"
+ />
+);
+
+export default Input;
\ No newline at end of file
diff --git a/client/src/components/LoadedAgentCard.tsx b/client/src/components/LoadedAgentCard.tsx
new file mode 100644
index 0000000..1fa204d
--- /dev/null
+++ b/client/src/components/LoadedAgentCard.tsx
@@ -0,0 +1,144 @@
+import React, { useState } from "react";
+import { Heart, MessageCircle, Sparkles, CheckCircle } from "lucide-react";
+import { Agent } from "../interfaces/AgentInterfaces";
+
+// Define the props for AgentCard
+interface AgentCardProps {
+ agent: Agent;
+ onSelect: (agent: Agent) => Promise<void>;
+}
+
+const LoadedAgentCard: React.FC<AgentCardProps> = ({ agent, onSelect }) => {
+ const agentData = agent.agent;
+ const [isFlipped, setIsFlipped] = useState(false);
+ const agentName = agentData?.agent_details?.name || "Unknown Agent";
+ const agentPersonality = agentData?.agent_details?.personality || [];
+ const agentCommunicationStyle =
+ agentData?.agent_details?.communication_style || [];
+ const agentHashtags = agentData?.agent_details?.hashtags || [];
+ const agentEmojis = agentData?.agent_details?.emojis || [];
+ const agentTopicExpertise = agentData?.agent_details?.topic_expertise || [];
+ const profileImageUrl = agentData?.profile_image?.details?.url || "";
+ const [isSelecting, setIsSelecting] = useState(false);
+ const [isSelected, setIsSelected] = useState(false);
+
+ const handleCardClick = async () => {
+ try {
+ await onSelect(agent);
+ } catch (error) {
+ console.error("[LoadedAgentCard] Error selecting agent:", error);
+ }
+ };
+
+ return (
+ <div className="relative w-full">
+ <div
+ className="relative w-full h-[300px] perspective"
+ onMouseEnter={() => setIsFlipped(true)}
+ onMouseLeave={() => setIsFlipped(false)}
+ onClick={handleCardClick}
+ >
+ <div
+ className={`relative w-full h-full duration-500 preserve-3d transform-style-3d ${
+ isFlipped ? "rotate-y-180" : ""
+ }`}
+ >
+ {/* Front of Card */}
+ <div className="absolute w-full h-full backface-hidden">
+ <div className="w-full h-full bg-yellow-900/80 rounded-lg overflow-hidden shadow-xl border border-orange-500/30">
+ <div className="relative h-[225px]">
+ <img
+ src={profileImageUrl}
+ alt=""
+ className="w-full h-full object-cover"
+ />
+ <div className="absolute bottom-0 left-0 right-0 bg-gradient-to-t from-gray-900 to-transparent h-16" />
+ </div>
+ <div className="h-[75px] p-2 px-3 bg-slate-900/80 transition-opacity duration-200">
+ <h3 className="text-xl font-bold text-gray-100 mb-1">
+ {agentName}
+ </h3>
+ <p className="text-orange-300 text-sm">{agentName}</p>
+ </div>
+ </div>
+ </div>
+
+ {/* Back of Card */}
+ <div className="absolute w-full h-full backface-hidden rotate-y-180">
+ <div className="w-full h-full bg-slate-900/80 rounded-lg p-4 shadow-xl border border-orange-500/30 flex flex-col">
+ {/* Header with small image */}
+ <div className="flex gap-4 mb-4">
+ <img
+ src={profileImageUrl}
+ alt=""
+ className="w-20 h-20 rounded-lg object-cover flex-shrink-0"
+ />
+ <div className="overflow-hidden">
+ <h3 className="text-xl font-bold text-gray-100 truncate">
+ {agentName}
+ </h3>
+ <p className="text-orange-400 text-sm truncate">
+ {Array.isArray(agentTopicExpertise)
+ ? agentTopicExpertise[0]
+ : agentTopicExpertise}{" "}
+ Expert
+ </p>
+ </div>
+ </div>
+ <div className="space-y-4 overflow-y-auto max-h-[130px] pr-2 pb-16">
+ {/* Personality */}
+ <div>
+ <div className="flex items-center gap-2 text-gray-300 mb-1">
+ <Heart className="w-4 h-4 text-orange-400 flex-shrink-0" />
+ <span className="font-medium">Personality</span>
+ </div>
+ <p className="text-gray-400 text-sm break-words line-clamp-3">
+ {Array.isArray(agentPersonality)
+ ? agentPersonality.join(", ")
+ : agentPersonality}
+ </p>
+ </div>
+ {/* Other fields */}
+ </div>
+ {/* Action button */}
+ <div className="absolute bottom-2 left-2 right-2">
+ <button
+ className={`w-full px-4 py-2 bg-gradient-to-r from-[#F7F957] to-[#F9D02C] rounded-md
+ flex items-center justify-center gap-2 text-gray-700
+ ${
+ isSelecting || isSelected
+ ? "opacity-50 cursor-not-allowed"
+ : "hover:from-cyan-700 hover:to-orange-700"
+ }`}
+ onClick={async (e) => {
+ e.stopPropagation();
+ if (isSelecting || isSelected) return;
+ setIsSelecting(true);
+ try {
+ await onSelect(agent);
+ setIsSelected(true);
+ } finally {
+ setIsSelecting(false);
+ }
+ }}
+ disabled={isSelecting || isSelected}
+ >
+ <CheckCircle
+ className={`w-4 h-4 ${isSelecting ? "animate-spin" : ""}`}
+ />
+ {isSelected
+ ? "Selected"
+ : isSelecting
+ ? "Selecting..."
+ : "Select Agent"}
+ </button>
+ </div>
+ </div>
+ </div>
+ </div>
+ </div>
+ </div>
+ );
+};
+
+export default LoadedAgentCard;
diff --git a/client/src/components/LoadingBar.tsx b/client/src/components/LoadingBar.tsx
new file mode 100644
index 0000000..36e7d53
--- /dev/null
+++ b/client/src/components/LoadingBar.tsx
@@ -0,0 +1,30 @@
+import { useState, useEffect } from 'react';
+
+const LoadingBar = ({ progress }: { progress: number }) => {
+ const [width, setWidth] = useState(0);
+
+ useEffect(() => {
+ const timeout = setTimeout(() => {
+ setWidth(progress);
+ }, 100); // Delay to simulate dynamic loading
+
+ return () => clearTimeout(timeout);
+ }, [progress]);
+
+ return (
+ <div>
+ <div className="text-white mb-2 site-text-class">Agent Image Generating...</div>
+ <div className="w-full bg-gray-200 rounded-full h-2">
+ <div
+ className="h-full rounded-full transition-all duration-[15000ms]"
+ style={{
+ width: `${Math.min(Math.max(width, 0), 100)}%`,
+ background: 'linear-gradient(to right, #06b6d4, #f97316)' // Updated gradient from cyan to orange
+ }}
+ />
+ </div>
+ </div>
+ );
+};
+
+export default LoadingBar;
\ No newline at end of file
diff --git a/client/src/components/Navbar.tsx b/client/src/components/Navbar.tsx
new file mode 100644
index 0000000..9c3380d
--- /dev/null
+++ b/client/src/components/Navbar.tsx
@@ -0,0 +1,118 @@
+import { useState } from "react";
+import { Menu } from "./ui/navbar-menu";
+import { cn } from "../lib/utils";
+import { Link, useLocation } from "react-router-dom";
+import { motion } from "framer-motion";
+
+export function NavbarDemo() {
+ return (
+ <div className="relative w-full flex items-center justify-center">
+ <Navbar className="top-6 shadow-lg" />
+ </div>
+ );
+}
+
+function Navbar({ className }: { className?: string }) {
+ const [active, setActive] = useState<string | null>(null);
+ const location = useLocation(); // To track the current active page
+
+ // Map of links to their path (for active state comparison)
+ const links = [
+ { label: "Home", path: "#" },
+ { label: "Features", path: "#agents" },
+ { label: "Tokenomics", path: "#tokenomics" },
+ { label: "Roadmap", path: "#roadmap" },
+ ];
+
+ return (
+ <div
+ className={cn(
+ "fixed top-10 inset-x-0 lg:max-w-7xl max-w-2xl mx-auto z-50 rounded-lg bg-gradient-to-r from-yellow-300 to-yellow-100",
+ className
+ )}
+ >
+ <Menu setActive={setActive}>
+ <div className="flex justify-between">
+ <div className="flex items-center space-x-8">
+ <a href="/">
+ <img src="/logo.svg" alt="logo" className="w-36" />
+ </a>
+
+ {/* Navbar Links */}
+ <div className="flex items-center space-x-8">
+ {links.map(({ label, path }) => {
+ const isActive = location.pathname === path;
+
+ return (
+ <motion.div
+ key={label}
+ whileHover={{
+ scale: 1.05, // Scale slightly on hover
+ transition: {
+ type: "spring",
+ stiffness: 300,
+ damping: 20,
+ },
+ }}
+ className="relative hover:border-b-2 border-neutral-800 duration-100"
+ >
+ <a href={path}>
+ <p
+ className={`${
+ isActive ? "text-amber-500" : "text-gray-800"
+ } transition-colors duration-300 font-medium font-orbitron`}
+ >
+ {label}
+ </p>
+ </a>
+
+ {/* Animated Underline for active link */}
+ {isActive && (
+ <motion.div
+ className="absolute bottom-0 left-0 w-full h-[2px] bg-amber-500"
+ initial={{ width: 0 }}
+ animate={{ width: "100%" }}
+ transition={{ duration: 0.4 }}
+ />
+ )}
+ </motion.div>
+ );
+ })}
+ </div>
+ </div>
+
+ {/* Social Icons with animation to slide in from the right */}
+ <div className="flex items-center space-x-3 justify-end">
+ {["gitbook.svg", "github.svg", "X.svg"].map((src, index) => (
+ <motion.a
+ href={
+ src === "gitbook.svg"
+ ? "https://equilink.gitbook.io"
+ : src === "github.svg"
+ ? "https://github.com/Equilink-Suite"
+ : ""
+ }
+ target="_blank"
+ key={index}
+ className="p-3 border-[1px] hover:bg-yellow-400 rounded-xl bg-white shadow-lg"
+ initial={{ opacity: 0, x: 20 }}
+ animate={{ opacity: 1, x: 0 }}
+ transition={{
+ delay: index * 0.2,
+ type: "spring",
+ stiffness: 300,
+ }}
+ whileHover={{
+ scale: 1.1, // This is the Framer Motion scale on hover
+ transition: { duration: 0.2 }, // Smooth transition
+ }}
+ >
+ <img src={`/${src}`} className="w-6 h-auto" />
+ </motion.a>
+ ))}
+ </div>
+ </div>
+ </Menu>
+ </div>
+ );
+}
diff --git a/client/src/components/Notification.tsx b/client/src/components/Notification.tsx
new file mode 100644
index 0000000..124c140
--- /dev/null
+++ b/client/src/components/Notification.tsx
@@ -0,0 +1,28 @@
+import React from 'react';
+
+interface NotificationProps {
+ message: string;
+ type: 'error' | 'success' | 'info';
+ onClose: () => void;
+}
+
+const Notification: React.FC<NotificationProps> = ({ message, type, onClose }) => {
+ return (
+ <div
+ className={`fixed inset-0 flex items-center justify-center z-50`}
+ onClick={onClose}
+ >
+ <div
+ className={`bg-white p-4 rounded shadow-md border ${
+ type === 'error' ? 'border-red-500' : type === 'success' ? 'border-green-500' : 'border-blue-500'
+ }`}
+ >
+ <p className={`text-${type === 'error' ? 'red' : type === 'success' ? 'green' : 'blue'}-500`}>
+ {message}
+ </p>
+ </div>
+ </div>
+ );
+};
+
+export default Notification;
diff --git a/client/src/components/RandomAgentCard.tsx b/client/src/components/RandomAgentCard.tsx
new file mode 100644
index 0000000..a031b4c
--- /dev/null
+++ b/client/src/components/RandomAgentCard.tsx
@@ -0,0 +1,310 @@
+import React, { useState, useEffect } from 'react';
+import {
+ Heart,
+ MessageCircle,
+ Sparkles,
+ CheckCircle,
+ RefreshCcw,
+} from 'lucide-react';
+import { Agent } from '../interfaces/AgentInterfaces';
+import LoadingBar from './LoadingBar';
+
+// Define the props for AgentCard
+interface RandomAgentCardProps {
+ agent: Agent;
+ onSelect: (agent: Agent) => Promise<void>;
+ onAddAgent: (agent: Agent) => void;
+ isUserAgent: boolean;
+ setRandomAgents: React.Dispatch<React.SetStateAction<Agent[]>>;
+ generateRandomAgentData: () => Promise<Agent>;
+ isLoadedAgent: boolean;
+ onRegenerate: (agentId: string) => Promise<void>;
+ isLoading?: boolean;
+ isExample?: boolean;
+}
+
+const RandomAgentCard: React.FC<RandomAgentCardProps> = ({
+ agent,
+ onSelect,
+ onAddAgent,
+ isUserAgent,
+ onRegenerate,
+}) => {
+ const [isFlipped, setIsFlipped] = useState(false);
+ const [isRegenerating, setIsRegenerating] = useState(false);
+ const [isSelecting, setIsSelecting] = useState(false);
+ const [isAdded, setIsAdded] = useState(false);
+ const agentName = agent.name || 'Unknown Agent';
+ const agentPersonality = Array.isArray(agent.personality) ? agent.personality : [];
+ const agentCommunicationStyle = Array.isArray(agent.communicationStyle) ? agent.communicationStyle : [];
+ const agentEmojis = Array.isArray(agent.emojis) ? agent.emojis : [];
+ const agentTags = Array.isArray(agent.tags) ? agent.tags : [];
+ const profileImageUrl = agent.avatar || "";
+ const [showNewContent, setShowNewContent] = useState(true);
+ const [loadingProgress, setLoadingProgress] = useState(0);
+
+ useEffect(() => {
+ let intervalId: number | undefined;
+
+ if (agent.isLoading || isRegenerating) {
+ // Reset states when loading starts
+ setLoadingProgress(0);
+ setShowNewContent(false);
+
+ // Immediately start filling to 30%
+ setLoadingProgress(30);
+
+ // Start progress up to 90%
+ intervalId = window.setInterval(() => {
+ setLoadingProgress(prev => {
+ if (prev < 90) {
+ return Math.min(prev + 1, 90);
+ }
+ return prev;
+ });
+ }, 30);
+ } else if (loadingProgress > 0) {
+ // When regeneration is complete, quickly fill to 100%
+ if (intervalId !== undefined) clearInterval(intervalId);
+ setLoadingProgress(100);
+
+ // Show new content after progress bar completes
+ const timeout = setTimeout(() => {
+ setLoadingProgress(0);
+ setShowNewContent(true);
+ }, 500);
+
+ return () => clearTimeout(timeout);
+ }
+
+ return () => {
+ if (intervalId !== undefined) clearInterval(intervalId);
+ };
+ }, [agent.isLoading, isRegenerating]);
+
+ const addButton = agent.isExample ? (
+ <button
+ onClick={(e) => {
+ e.stopPropagation();
+ onSelect(agent);
+ }}
+ className="opacity-50 cursor-not-allowed bg-gray-500 text-white px-4 py-2 rounded"
+ >
+ Example Agent
+ </button>
+ ) : isAdded ? (
+ <button
+ className="opacity-50 cursor-not-allowed bg-green-600 text-white px-4 py-2 rounded"
+ disabled
+ onClick={(e) => e.stopPropagation()}
+ >
+ Added ✓
+ </button>
+ ) : (
+ <button
+ onClick={async (e) => {
+ e.stopPropagation();
+ if (isRegenerating) return;
+ setIsRegenerating(true);
+ try {
+ await onAddAgent(agent);
+ setIsAdded(true);
+ } finally {
+ setIsRegenerating(false);
+ }
+ }}
+ disabled={isRegenerating}
+ className={`bg-gradient-to-r from-cyan-600 to-orange-600 text-white px-4 py-2 rounded
+ ${isRegenerating ? 'opacity-50 cursor-not-allowed' : 'hover:from-cyan-700 hover:to-orange-700'}`}
+ >
+ {isRegenerating ? 'Adding...' : 'Add Agent'}
+ </button>
+ );
+
+ const selectButton = (
+ <button
+ className={`w-full px-4 py-2 bg-gradient-to-r from-cyan-600 to-orange-600 rounded-md
+ flex items-center justify-center gap-2 text-white
+ ${isSelecting ? 'opacity-50 cursor-not-allowed' : 'hover:from-cyan-700 hover:to-orange-700'}`}
+ onClick={async (e) => {
+ e.stopPropagation();
+ if (isSelecting) return;
+ setIsSelecting(true);
+ try {
+ await onSelect(agent);
+ } finally {
+ setIsSelecting(false);
+ }
+ }}
+ disabled={isSelecting}
+ >
+ <CheckCircle className={`w-4 h-4 ${isSelecting ? 'animate-spin' : ''}`} />
+ {isSelecting ? 'Selecting...' : 'Select Agent'}
+ </button>
+ );
+
+ return (
+ <div className="relative">
+ {/* <div className="absolute top-2 right-2 bg-gray-700 text-white text-xs rounded px-2">
+ {isUserAgent ? 'Loaded Agent' : 'Randomly Generated'}
+ </div> */}
+ <div
+ className="perspective w-64 h-[500px]"
+ onMouseEnter={() => !isRegenerating && setIsFlipped(true)}
+ onMouseLeave={() => !isRegenerating && setIsFlipped(false)}
+ onClick={(e) => {
+ e.stopPropagation();
+ if (!isRegenerating) {
+ onSelect(agent);
+ }
+ }}
+ >
+ <div
+ className={`relative w-full h-full duration-500 preserve-3d ${
+ isFlipped && !isRegenerating ? 'rotate-y-180' : ''
+ }`}
+ >
+ {/* Front of card */}
+ <div className="absolute w-full h-full backface-hidden">
+ <div className="w-full h-full bg-slate-900/80 rounded-lg overflow-hidden shadow-xl border border-orange-500/30">
+ <div className="relative h-[400px]">
+ {(!showNewContent || agent.isLoading || isRegenerating || loadingProgress > 0) ? (
+ <div className="w-full h-full bg-slate-900/80 flex items-center justify-center">
+ <div className="w-3/4">
+ <LoadingBar progress={loadingProgress} />
+ </div>
+ </div>
+ ) : (
+ <img
+ src={agent.avatar || ''}
+ alt={agent.avatar ? '' : 'Please regenerate again'}
+ className="w-full h-full object-cover"
+ style={{ display: agent.avatar ? 'block' : 'none' }}
+ />
+ )}
+ {!agent.avatar && (
+ <div className="w-full h-full flex items-center justify-center text-white">
+ Please regenerate again
+ </div>
+ )}
+ <div className="absolute bottom-0 left-0 right-0 bg-gradient-to-t from-gray-900 to-transparent h-16" />
+ </div>
+ {/* Only show name and role when not flipped */}
+ <div className={`h-[100px] p-4 bg-slate-900/80 transition-opacity duration-200 ${
+ isFlipped ? 'opacity-0' : 'opacity-100'
+ }`}>
+ <h3 className="text-xl font-bold text-gray-100 mb-1 truncate">
+ {agentName}
+ </h3>
+ <p className="text-orange-300 text-sm truncate">{agent.role}</p>
+ </div>
+ </div>
+ </div>
+
+ {/* Back of card */}
+ <div className="absolute w-full h-full backface-hidden rotate-y-180">
+ <div className="w-full h-full bg-slate-900/80 rounded-lg p-4 shadow-xl border border-orange-500/30 flex flex-col">
+ {/* Header with small image */}
+ <div className="flex gap-4 mb-4">
+ <img
+ src={profileImageUrl}
+ alt=""
+ className="w-20 h-20 rounded-lg object-cover flex-shrink-0"
+ />
+ <div className="overflow-hidden">
+ <h3 className="text-xl font-bold text-gray-100 truncate">
+ {agentName}
+ </h3>
+ <p className="text-orange-400 text-sm truncate">{agent.role}</p>
+ </div>
+ </div>
+
+ {/* Content sections with better overflow handling */}
+ <div className="space-y-4 overflow-y-auto flex-grow mb-4 pr-2">
+ <div>
+ <div className="flex items-center gap-2 text-gray-300 mb-1">
+ <Heart className="w-4 h-4 text-orange-400 flex-shrink-0" />
+ <span className="font-medium">Personality</span>
+ </div>
+ <p className="text-gray-400 text-sm break-words line-clamp-3">
+ {agentPersonality.join(', ')}
+ </p>
+ </div>
+
+ <div>
+ <div className="flex items-center gap-2 text-gray-300 mb-1">
+ <MessageCircle className="w-4 h-4 text-orange-400 flex-shrink-0" />
+ <span className="font-medium">Communication Style</span>
+ </div>
+ <p className="text-gray-400 text-sm break-words line-clamp-3">
+ {agentCommunicationStyle.join(', ')}
+ </p>
+ </div>
+
+ <div>
+ <div className="flex items-center gap-2 text-gray-300 mb-1">
+ <Sparkles className="w-4 h-4 text-orange-400 flex-shrink-0" />
+ <span className="font-medium">Emojis</span>
+ </div>
+ <p className="text-gray-400 text-sm break-words line-clamp-2">
+ {agentEmojis.join(' ')}
+ </p>
+ </div>
+
+ {/* Tags */}
+ <div className="flex gap-2 flex-wrap">
+ {agentTags.map((tag, index) => (
+ <span
+ key={index}
+ className="px-2 py-1 bg-orange-900/50 rounded-full text-xs text-orange-300 truncate max-w-[150px]"
+ >
+ {tag}
+ </span>
+ ))}
+ </div>
+ </div>
+
+ {/* Action button - with solid background */}
+ <div className="absolute bottom-2 left-4 right-4">
+ {/* Solid background container */}
+ <div className="bg-slate-900 rounded-md"> {/* Removed opacity, added rounded corners */}
+ <div className="relative px-4 py-2"> {/* Added some vertical padding */}
+ {isUserAgent ? selectButton : addButton}
+ </div>
+ </div>
+ </div>
+ </div>
+ </div>
+ </div>
+ </div>
+
+ {/* Regenerate button below the card */}
+ {!isUserAgent && (
+ <div className="mt-2 w-64 mx-auto">
+ <button
+ data-agent-id={agent.id}
+ className={`w-full mt-2 py-2 bg-gradient-to-r from-[#F7F957] to-[#F9D02C] rounded-md
+ flex items-center justify-center gap-2 text-gray-700
+ ${isRegenerating ? 'opacity-50 cursor-not-allowed' : 'hover:from-cyan-700 hover:to-orange-700'}`}
+ onClick={async (e) => {
+ e.stopPropagation();
+ if (isRegenerating) return;
+ setIsRegenerating(true);
+ try {
+ await onRegenerate(agent.id?.toString() || Math.random().toString());
+ } finally {
+ setIsRegenerating(false);
+ }
+ }}
+ disabled={isRegenerating}
+ >
+ <RefreshCcw className={`w-4 h-4 ${isRegenerating ? 'animate-spin' : ''}`} />
+ {isRegenerating ? 'Regenerating...' : 'Regenerate'}
+ </button>
+ </div>
+ )}
+ </div>
+ );
+};
+
+export default RandomAgentCard;
\ No newline at end of file
diff --git a/client/src/components/TraitButtons.tsx b/client/src/components/TraitButtons.tsx
new file mode 100644
index 0000000..99a53e8
--- /dev/null
+++ b/client/src/components/TraitButtons.tsx
@@ -0,0 +1,51 @@
+import React, { useState } from 'react';
+import { Sparkles } from 'lucide-react';
+
+interface TraitButtonsProps {
+ field: 'personality' | 'communication_style' | 'topic_expertise' | 'hashtags' | 'emojis';
+ options: string[];
+ onTraitButtonClick: (field: 'personality' | 'communication_style' | 'topic_expertise' | 'hashtags' | 'emojis', value: string) => void;
+}
+
+// Component to render suggestion chips for a given field with provided options
+const TraitButtons: React.FC<TraitButtonsProps> = ({ field, options, onTraitButtonClick }) => {
+ const [selectedOption, setSelectedOption] = useState<string | null>(null); // Track selected option
+
+ const handleDeleteTrait = (option: string) => {
+ setSelectedOption(null); // Clear the selected option
+ onTraitButtonClick(field, option); // Call the click handler to delete the trait
+ };
+
+ return (
+ <div className="flex flex-wrap gap-2 mb-4">
+ {options.map((option, index) => (
+ <div key={index} className="flex items-center">
+ <button
+ className={`px-3 py-1 rounded-full transition-all duration-300 flex items-center justify-between
+ ${selectedOption === option ? 'bg-yellow-400 text-gray-600' : 'bg-gray-100/30 hover:bg-yellow-500/30 text-gray-700'}
+ border border-orange-500/30`}
+ onClick={() => {
+ setSelectedOption(option); // Update selected option
+ }}
+ >
+ <div className="flex items-center">
+ <Sparkles className="w-4 h-4 mr-2 text-orange-400" />
+ {option}
+ </div>
+ <span
+ className="text-red-500 ml-2 cursor-pointer" // Changed to span and added cursor pointer
+ onClick={(e) => {
+ e.stopPropagation(); // Prevent the click from bubbling up to the main button
+ handleDeleteTrait(option); // Handle delete action
+ }}
+ >
+ x
+ </span>
+ </button>
+ </div>
+ ))}
+ </div>
+ );
+};
+
+export default TraitButtons;
\ No newline at end of file
diff --git a/client/src/components/button.tsx b/client/src/components/button.tsx
new file mode 100644
index 0000000..04e60ac
--- /dev/null
+++ b/client/src/components/button.tsx
@@ -0,0 +1,16 @@
+import React from 'react';
+
+interface ButtonProps extends React.ButtonHTMLAttributes<HTMLButtonElement> {
+ children: React.ReactNode;
+}
+
+export const Button: React.FC<ButtonProps> = ({ children, className = '', ...props }) => {
+ return (
+ <button
+ className={`px-4 py-2 rounded-lg text-white ${className}`}
+ {...props}
+ >
+ {children}
+ </button>
+ );
+};
\ No newline at end of file
diff --git a/client/src/components/providers.tsx b/client/src/components/providers.tsx
new file mode 100644
index 0000000..1c90679
--- /dev/null
+++ b/client/src/components/providers.tsx
@@ -0,0 +1,35 @@
+import { PrivyProvider } from "@privy-io/react-auth";
+import { toSolanaWalletConnectors } from "@privy-io/react-auth/solana";
+
+const solanaConnectors = toSolanaWalletConnectors({
+ // By default, shouldAutoConnect is enabled
+ shouldAutoConnect: true,
+});
+
+export default function Providers({ children }: { children: React.ReactNode }) {
+ return (
+ <PrivyProvider
+ appId="cm6t7wm9y0i1wt680b4tukt8r"
+ config={{
+ appearance: {
+ theme: "dark",
+ showWalletLoginFirst: false,
+ walletChainType: "solana-only",
+ walletList: ["phantom"],
+ },
+ externalWallets: {
+ solana: {
+ connectors: solanaConnectors,
+ },
+ },
+ loginMethods: ["wallet", "email"],
+ embeddedWallets: {
+ createOnLogin: "all-users",
+ requireUserPasswordOnCreate: false,
+ },
+ }}
+ >
+ {children}
+ </PrivyProvider>
+ );
+}
diff --git a/client/src/components/sidebar.tsx b/client/src/components/sidebar.tsx
new file mode 100644
index 0000000..4208e44
--- /dev/null
+++ b/client/src/components/sidebar.tsx
@@ -0,0 +1,114 @@
+import React, { useState } from "react";
+import { motion } from "framer-motion";
+import {
+ ChevronLeft,
+ ChevronRight,
+ UserPlus,
+ Users,
+ MessageSquare,
+ LogOut,
+} from "lucide-react";
+import { Link, useNavigate } from "react-router-dom";
+import { usePrivy } from "@privy-io/react-auth";
+
+const Sidebar = () => {
+ const [isOpen, setIsOpen] = useState(true);
+ const {logout} = usePrivy()
+ const navigate = useNavigate()
+ const toggleSidebar = () => {
+ setIsOpen(!isOpen);
+ };
+
+ const sidebarVariants = {
+ open: {
+ width: "300px",
+ transition: {
+ type: "spring",
+ stiffness: 200,
+ damping: 25,
+ },
+ },
+ closed: {
+ width: "0px",
+ transition: {
+ type: "spring",
+ stiffness: 200,
+ damping: 25,
+ },
+ },
+ };
+
+ const menuItems = [
+ { icon: <UserPlus size={20} />, text: "Create Agent", link: "/create-agent" },
+ { icon: <Users size={20} />, text: "Browse Agents", link: "/browse-agents" },
+ { icon: <MessageSquare size={20} />, text: "Chat with Agent", link: "/chat" },
+ ];
+
+ return (
+ <div className="relative">
+ {/* Toggle Button - Positioned absolutely relative to the container */}
+ <button
+ onClick={toggleSidebar}
+ className="fixed top-8 left-0 z-50 bg-yellow-400 rounded-full p-1.5
+ hover:bg-yellow-500 transition-colors"
+ style={{
+ left: isOpen ? "282px" : "12px",
+ transition: "left 0.3s ease-in-out",
+ }}
+ >
+ {isOpen ? <ChevronLeft size={16} /> : <ChevronRight size={16} />}
+ </button>
+
+ <motion.div
+ initial="open"
+ animate={isOpen ? "open" : "closed"}
+ variants={sidebarVariants}
+ className="h-screen bg-white shadow-lg flex flex-col overflow-hidden"
+ >
+ {isOpen && (
+ <div className="flex flex-col h-full">
+ {/* Logo Section */}
+ <Link to="/" className="flex items-center p-4">
+ <img src="/logo.svg" alt="Logo" className="w-[80%] h-auto" />
+ </Link>
+
+ {/* Navigation Items */}
+ <div className="flex-1 px-3 py-8 flex flex-col gap-2 items-stretch">
+ {menuItems.map((item, index) => (
+ <Link to={item.link}>
+ <motion.button
+ key={index}
+ whileHover={{ scale: 1.02 }}
+ whileTap={{ scale: 0.98 }}
+ className="flex w-full items-center p-3 rounded-lg hover:bg-yellow-50
+ text-gray-700 hover:text-yellow-600 transition-colors justify-start"
+ >
+ {item.icon}
+ <span className="ml-3 whitespace-nowrap">{item.text}</span>
+ </motion.button>
+ </Link>
+ ))}
+ </div>
+
+ {/* Logout Button */}
+ <motion.button
+ whileHover={{ scale: 1.02 }}
+ whileTap={{ scale: 0.98 }}
+ onClick={()=>{
+ logout()
+ navigate("/")
+ }}
+ className="m-4 p-3 flex items-center rounded-lg bg-yellow-400
+ hover:bg-yellow-500 text-gray-800 transition-colors justify-start"
+ >
+ <LogOut size={20} />
+ <span className="ml-3 whitespace-nowrap">Logout</span>
+ </motion.button>
+ </div>
+ )}
+ </motion.div>
+ </div>
+ );
+};
+
+export default Sidebar;
diff --git a/client/src/components/tokenomics.tsx b/client/src/components/tokenomics.tsx
new file mode 100644
index 0000000..eb4a16d
--- /dev/null
+++ b/client/src/components/tokenomics.tsx
@@ -0,0 +1,92 @@
+import React from "react";
+import { PieChart, Pie, Cell, ResponsiveContainer } from "recharts";
+import { motion } from "framer-motion";
+
+const COLORS = ["#F7A600", "#FFD700", "#FFEC80", "#D3D3D3"];
+
+const data = [
+ { name: "Developer Wallet Allocation", value: 5, color: "#F7A600" },
+ { name: "Team", value: 2, color: "#FFD700" },
+ { name: "Marketing & Operations", value: 1, color: "#FFEC80" },
+ { name: "Treasury & Ecosystem", value: 2, color: "#D3D3D3" },
+];
+
+const vestingData = [
+ { name: "2% Locked (2 Years)", color: "#D3D3D3" },
+ { name: "0.5% for Marketing", color: "#E0E0E0" },
+ { name: "0.5% Treasury Liquidity", color: "#F0F0F0" },
+];
+
+const Tokenomics: React.FC = () => {
+ return (
+ <div id="tokenomics" className="grid md:grid-cols-2 gap-10 items-center justify-center p-10 w-full">
+ {/* Animated Pie Chart */}
+ <motion.div
+ initial={{ opacity: 0, scale: 0.8 }}
+ animate={{ opacity: 1, scale: 1 }}
+ transition={{ duration: 0.8 }}
+ className="flex justify-center"
+ >
+ <ResponsiveContainer width={600} height={600}>
+ <PieChart>
+ <Pie
+ data={data}
+ cx="50%"
+ cy="50%"
+ innerRadius={120}
+ outerRadius={180}
+ fill="#8884d8"
+ paddingAngle={5}
+ dataKey="value"
+ className="w-full h-full"
+ >
+ {data.map((entry, index) => (
+ <Cell key={`cell-${index}`} fill={entry.color} />
+ ))}
+ </Pie>
+ </PieChart>
+ </ResponsiveContainer>
+ </motion.div>
+
+ {/* Tokenomics Info */}
+ <motion.div
+ initial={{ opacity: 0, x: 50 }}
+ animate={{ opacity: 1, x: 0 }}
+ transition={{ duration: 0.8 }}
+ className="text-left"
+ >
+ <h2 className="text-4xl font-orbitron font-bold mb-4">Tokenomics</h2>
+ <p className="text-gray-500 mb-4">
+ Transparent Allocation – Designed for Growth and Sustainability
+ </p>
+
+ <div className="mb-6 space-y-2">
+ {data.map((item, index) => (
+ <div key={index} className="flex items-center">
+ <div
+ className="w-4 h-4 rounded mr-2"
+ style={{ backgroundColor: item.color }}
+ ></div>
+ <p className="text-lg">{item.name} ({item.value}%)</p>
+ </div>
+ ))}
+ </div>
+
+ <h3 className="text-xl font-semibold mb-3">Vesting Plan</h3>
+ <div className="space-y-2">
+ {vestingData.map((item, index) => (
+ <div key={index} className="flex items-center">
+ <div
+ className="w-4 h-4 rounded mr-2"
+ style={{ backgroundColor: item.color }}
+ ></div>
+ <p className="text-lg">{item.name}</p>
+ </div>
+ ))}
+ </div>
+ </motion.div>
+ </div>
+ );
+};
+
+export default Tokenomics;
diff --git a/client/src/components/ui/PixelCard.jsx b/client/src/components/ui/PixelCard.jsx
new file mode 100644
index 0000000..bbb411e
--- /dev/null
+++ b/client/src/components/ui/PixelCard.jsx
@@ -0,0 +1,274 @@
+import { useEffect, useRef } from "react";
+
+class Pixel {
+ constructor(canvas, context, x, y, color, speed, delay) {
+ this.width = canvas.width;
+ this.height = canvas.height;
+ this.ctx = context;
+ this.x = x;
+ this.y = y;
+ this.color = color;
+ this.speed = this.getRandomValue(0.1, 0.9) * speed;
+ this.size = 0;
+ this.sizeStep = Math.random() * 0.4;
+ this.minSize = 0.5;
+ this.maxSizeInteger = 2;
+ this.maxSize = this.getRandomValue(this.minSize, this.maxSizeInteger);
+ this.delay = delay;
+ this.counter = 0;
+ this.counterStep = Math.random() * 4 + (this.width + this.height) * 0.01;
+ this.isIdle = false;
+ this.isReverse = false;
+ this.isShimmer = false;
+ }
+
+ getRandomValue(min, max) {
+ return Math.random() * (max - min) + min;
+ }
+
+ draw() {
+ const centerOffset = this.maxSizeInteger * 0.5 - this.size * 0.5;
+ this.ctx.fillStyle = this.color;
+ this.ctx.fillRect(
+ this.x + centerOffset,
+ this.y + centerOffset,
+ this.size,
+ this.size
+ );
+ }
+
+ appear() {
+ this.isIdle = false;
+ if (this.counter <= this.delay) {
+ this.counter += this.counterStep;
+ return;
+ }
+ if (this.size >= this.maxSize) {
+ this.isShimmer = true;
+ }
+ if (this.isShimmer) {
+ this.shimmer();
+ } else {
+ this.size += this.sizeStep;
+ }
+ this.draw();
+ }
+
+ disappear() {
+ this.isShimmer = false;
+ this.counter = 0;
+ if (this.size <= 0) {
+ this.isIdle = true;
+ return;
+ } else {
+ this.size -= 0.1;
+ }
+ this.draw();
+ }
+
+ shimmer() {
+ if (this.size >= this.maxSize) {
+ this.isReverse = true;
+ } else if (this.size <= this.minSize) {
+ this.isReverse = false;
+ }
+ if (this.isReverse) {
+ this.size -= this.speed;
+ } else {
+ this.size += this.speed;
+ }
+ }
+}
+
+function getEffectiveSpeed(value, reducedMotion) {
+ const min = 0;
+ const max = 100;
+ const throttle = 0.001;
+ const parsed = parseInt(value, 10);
+
+ if (parsed <= min || reducedMotion) {
+ return min;
+ } else if (parsed >= max) {
+ return max * throttle;
+ } else {
+ return parsed * throttle;
+ }
+}
+
+/**
+ * You can change/expand these as you like.
+ */
+const VARIANTS = {
+ default: {
+ activeColor: null,
+ gap: 5,
+ speed: 35,
+ colors: "#f8fafc,#f1f5f9,#cbd5e1",
+ noFocus: false
+ },
+ blue: {
+ activeColor: "#e0f2fe",
+ gap: 10,
+ speed: 25,
+ colors: "#e0f2fe,#7dd3fc,#0ea5e9",
+ noFocus: false
+ },
+ yellow: {
+ activeColor: "#fef08a",
+ gap: 3,
+ speed: 20,
+ colors: "#fef08a,#fde047,#eab308",
+ noFocus: false
+ },
+ pink: {
+ activeColor: "#fecdd3",
+ gap: 6,
+ speed: 80,
+ colors: "#fecdd3,#fda4af,#e11d48",
+ noFocus: true
+ }
+};
+
+export default function PixelCard({
+ variant = "default",
+ gap,
+ speed,
+ colors,
+ noFocus,
+ className = "",
+ children
+}) {
+ const containerRef = useRef(null);
+ const canvasRef = useRef(null);
+ const pixelsRef = useRef([]);
+ const animationRef = useRef(null);
+ const timePreviousRef = useRef(performance.now());
+ const reducedMotion = useRef(
+ window.matchMedia("(prefers-reduced-motion: reduce)").matches
+ ).current;
+
+ const variantCfg = VARIANTS[variant] || VARIANTS.default;
+ const finalGap = gap ?? variantCfg.gap;
+ const finalSpeed = speed ?? variantCfg.speed;
+ const finalColors = colors ?? variantCfg.colors;
+ const finalNoFocus = noFocus ?? variantCfg.noFocus;
+
+ const initPixels = () => {
+ if (!containerRef.current || !canvasRef.current) return;
+
+ const rect = containerRef.current.getBoundingClientRect();
+ const width = Math.floor(rect.width);
+ const height = Math.floor(rect.height);
+ const ctx = canvasRef.current.getContext("2d");
+
+ canvasRef.current.width = width;
+ canvasRef.current.height = height;
+ canvasRef.current.style.width = `${width}px`;
+ canvasRef.current.style.height = `${height}px`;
+
+ const colorsArray = finalColors.split(",");
+ const pxs = [];
+ for (let x = 0; x < width; x += parseInt(finalGap, 10)) {
+ for (let y = 0; y < height; y += parseInt(finalGap, 10)) {
+ const color =
+ colorsArray[Math.floor(Math.random() * colorsArray.length)];
+
+ const dx = x - width / 2;
+ const dy = y - height / 2;
+ const distance = Math.sqrt(dx * dx + dy * dy);
+ const delay = reducedMotion ? 0 : distance;
+
+ pxs.push(
+ new Pixel(
+ canvasRef.current,
+ ctx,
+ x,
+ y,
+ color,
+ getEffectiveSpeed(finalSpeed, reducedMotion),
+ delay
+ )
+ );
+ }
+ }
+ pixelsRef.current = pxs;
+ };
+
+ const doAnimate = (fnName) => {
+ animationRef.current = requestAnimationFrame(() => doAnimate(fnName));
+ const timeNow = performance.now();
+ const timePassed = timeNow - timePreviousRef.current;
+ const timeInterval = 1000 / 60; // ~60 FPS
+
+ if (timePassed < timeInterval) return;
+ timePreviousRef.current = timeNow - (timePassed % timeInterval);
+
+ const ctx = canvasRef.current?.getContext("2d");
+ if (!ctx || !canvasRef.current) return;
+
+ ctx.clearRect(0, 0, canvasRef.current.width, canvasRef.current.height);
+
+ let allIdle = true;
+ for (let i = 0; i < pixelsRef.current.length; i++) {
+ const pixel = pixelsRef.current[i];
+ pixel[fnName]();
+ if (!pixel.isIdle) {
+ allIdle = false;
+ }
+ }
+ if (allIdle) {
+ cancelAnimationFrame(animationRef.current);
+ }
+ };
+
+ const handleAnimation = (name) => {
+ cancelAnimationFrame(animationRef.current);
+ animationRef.current = requestAnimationFrame(() => doAnimate(name));
+ };
+
+ const onMouseEnter = () => handleAnimation("appear");
+ const onMouseLeave = () => handleAnimation("disappear");
+ const onFocus = (e) => {
+ if (e.currentTarget.contains(e.relatedTarget)) return;
+ handleAnimation("appear");
+ };
+ const onBlur = (e) => {
+ if (e.currentTarget.contains(e.relatedTarget)) return;
+ handleAnimation("disappear");
+ };
+
+ useEffect(() => {
+ initPixels();
+ const observer = new ResizeObserver(() => {
+ initPixels();
+ });
+ if (containerRef.current) {
+ observer.observe(containerRef.current);
+ }
+ return () => {
+ observer.disconnect();
+ cancelAnimationFrame(animationRef.current);
+ };
+ // eslint-disable-next-line react-hooks/exhaustive-deps
+ }, [finalGap, finalSpeed, finalColors, finalNoFocus]);
+
+ return (
+ <div
+ ref={containerRef}
+ className={`h-[400px] w-[300px] relative shadow-lg overflow-hidden grid place-items-center aspect-[4/5] border border-[#c2c2c2] rounded-[25px] isolate transition-colors duration-200 ease-[cubic-bezier(0.5,1,0.89,1)] select-none ${className}`}
+
+ onMouseEnter={onMouseEnter}
+ onMouseLeave={onMouseLeave}
+
+ onFocus={finalNoFocus ? undefined : onFocus}
+ onBlur={finalNoFocus ? undefined : onBlur}
+ tabIndex={finalNoFocus ? -1 : 0}
+ >
+ <canvas
+ className="w-full h-full block"
+ ref={canvasRef}
+ />
+ {children}
+ </div>
+ );
+}
diff --git a/client/src/components/ui/button.tsx b/client/src/components/ui/button.tsx
new file mode 100644
index 0000000..dcfee0c
--- /dev/null
+++ b/client/src/components/ui/button.tsx
@@ -0,0 +1,57 @@
+import * as React from "react"
+import { Slot } from "@radix-ui/react-slot"
+import { cva, type VariantProps } from "class-variance-authority"
+
+import { cn } from "../../lib/utils"
+
+const buttonVariants = cva(
+ "inline-flex items-center justify-center gap-2 whitespace-nowrap rounded-md text-sm font-medium transition-colors focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-ring disabled:pointer-events-none disabled:opacity-50 [&_svg]:pointer-events-none [&_svg]:size-4 [&_svg]:shrink-0",
+ {
+ variants: {
+ variant: {
+ default:
+ "bg-primary text-primary-foreground shadow hover:bg-primary/90",
+ destructive:
+ "bg-destructive text-destructive-foreground shadow-sm hover:bg-destructive/90",
+ outline:
+ "border border-input bg-background shadow-sm hover:bg-accent hover:text-accent-foreground",
+ secondary:
+ "bg-secondary text-secondary-foreground shadow-sm hover:bg-secondary/80",
+ ghost: "hover:bg-accent hover:text-accent-foreground",
+ link: "text-primary underline-offset-4 hover:underline",
+ },
+ size: {
+ default: "h-9 px-4 py-2",
+ sm: "h-8 rounded-md px-3 text-xs",
+ lg: "h-10 rounded-md px-8",
+ icon: "h-9 w-9",
+ },
+ },
+ defaultVariants: {
+ variant: "default",
+ size: "default",
+ },
+ }
+)
+
+export interface ButtonProps
+ extends React.ButtonHTMLAttributes<HTMLButtonElement>,
+ VariantProps<typeof buttonVariants> {
+ asChild?: boolean
+}
+
+const Button = React.forwardRef<HTMLButtonElement, ButtonProps>(
+ ({ className, variant, size, asChild = false, ...props }, ref) => {
+ const Comp = asChild ? Slot : "button"
+ return (
+ <Comp
+ className={cn(buttonVariants({ variant, size, className }))}
+ ref={ref}
+ {...props}
+ />
+ )
+ }
+)
+Button.displayName = "Button"
+
+export { Button, buttonVariants }
diff --git a/client/src/components/ui/card-new.tsx b/client/src/components/ui/card-new.tsx
new file mode 100644
index 0000000..5e0de0a
--- /dev/null
+++ b/client/src/components/ui/card-new.tsx
@@ -0,0 +1,176 @@
+import React from 'react';
+import styled, { keyframes } from 'styled-components';
+
+interface CardProps {
+ heading?: string;
+ content?: string;
+ bgColor?: string;
+ bgColorLight?: string;
+ textColorHover?: string;
+ boxShadowColor?: string;
+ icon?: React.ReactNode;
+}
+
+const sparkleAnimation = keyframes`
+ 0% {
+ transform: scale(0) rotate(0deg);
+ opacity: 0;
+ }
+ 50% {
+ transform: scale(1.2) rotate(180deg);
+ opacity: 1;
+ }
+ 100% {
+ transform: scale(0) rotate(360deg);
+ opacity: 0;
+ }
+`;
+
+const CardWrapper = styled.div<CardProps>`
+ position: relative;
+
+ .sparkle {
+ position: absolute;
+ width: 20px;
+ height: 20px;
+ background: radial-gradient(circle, rgba(255,255,255,0.8) 0%, transparent 70%);
+ border-radius: 50%;
+ pointer-events: none;
+ z-index: 1000;
+ animation: ${sparkleAnimation} 1s ease-in-out infinite;
+ }
+
+ .custom-card {
+ width: 400px;
+ border-radius:15px;
+ height: 500px;
+ background: #fff;
+ border-top-right-radius: 10px;
+ overflow: hidden;
+ display: flex;
+ flex-direction: column;
+ justify-content: center;
+ align-items: center;
+ position: relative;
+ box-shadow: 0 14px 26px rgba(0,0,0,0.1);
+ transition: all 0.3s ease-out;
+ text-decoration: none;
+
+ /* Custom color variables */
+ --bg-color: ${props => props.bgColor || '#ffd861'};
+ --bg-color-light: ${props => props.bgColorLight || '#ffeeba'};
+ --text-color-hover: ${props => props.textColorHover || '#4C5656'};
+ --box-shadow-color: ${props => props.boxShadowColor || 'rgba(255, 215, 97, 0.48)'};
+ }
+
+ .custom-card:hover {
+ transform: translateY(-5px) scale(1.005) translateZ(0);
+ box-shadow: 0 24px 36px rgba(0,0,0,0.11),
+ 0 24px 46px var(--box-shadow-color);
+ }
+
+ .custom-card:hover .overlay {
+ transform: scale(8) translateZ(0);
+ }
+
+ .circle {
+ width: 131px;
+ height: 131px;
+ border-radius: 50%;
+ background: #fff;
+ border: 3px solid var(--bg-color);
+ display: flex;
+ justify-content: center;
+ align-items: center;
+ position: relative;
+ z-index: 1;
+ transition: all 0.3s ease-out;
+ margin-bottom: 20px;
+ }
+
+ .circle svg {
+ width: 60px;
+ height: 60px;
+ color: var(--bg-color);
+ }
+
+ .overlay {
+ width: 170px;
+ position: absolute;
+ height: 170px;
+ border-radius: 50%;
+ background: var(--bg-color);
+ top: 330px;
+ left: 400px;
+ z-index: 0;
+ transition: transform 0.3s ease-out;
+ }
+
+ .card-heading {
+ font-size: 20px;
+ font-weight: bold;
+ color: #333;
+ margin-bottom: 15px;
+ z-index: 1000;
+ }
+
+ .card-content {
+ font-size: 17px;
+ color: #4C5656;
+ text-align: center;
+ padding: 0 20px;
+ z-index: 1000;
+ transition: color 0.3s ease-out;
+ }
+`;
+
+const Card: React.FC<CardProps> = ({
+ heading,
+ content,
+ bgColor,
+ bgColorLight,
+ textColorHover,
+ boxShadowColor,
+ icon
+}) => {
+ const renderSparkles = () => {
+ const sparkles = [];
+ for (let i = 0; i < 5; i++) {
+ sparkles.push(
+ <div
+ key={i}
+ className="sparkle"
+ style={{
+ top: `${Math.random() * 100}%`,
+ left: `${Math.random() * 100}%`,
+ animationDelay: `${Math.random()}s`
+ }}
+ />
+ );
+ }
+ return sparkles;
+ };
+
+ return (
+ <CardWrapper
+ bgColor={bgColor}
+ bgColorLight={bgColorLight}
+ textColorHover={textColorHover}
+ boxShadowColor={boxShadowColor}
+ >
+ <div className="body">
+ <div className="custom-card p-3">
+ {renderSparkles()}
+ <div className="overlay" />
+ <div className="circle">
+ {icon}
+ </div>
+ {heading && <div className="text-2xl z-50 font-bold my-3 text-gray-700">{heading}</div>}
+ {content && <div className="card-content">{content}</div>}
+ </div>
+ </div>
+ </CardWrapper>
+ );
+};
+
+export default Card;
\ No newline at end of file
diff --git a/client/src/components/ui/hero-backround.tsx b/client/src/components/ui/hero-backround.tsx
new file mode 100644
index 0000000..6c15435
--- /dev/null
+++ b/client/src/components/ui/hero-backround.tsx
@@ -0,0 +1,127 @@
+import { Canvas, useFrame, useThree } from '@react-three/fiber';
+import { useRef, useEffect, useState } from 'react';
+import * as THREE from 'three';
+import Stats from 'three/addons/libs/stats.module.js';
+
+function SmokeParticles() {
+ const groupRef = useRef();
+ const particlesRef = useRef([]);
+ const clockRef = useRef(new THREE.Clock());
+ const statsRef = useRef();
+ const { scene, size } = useThree();
+ const [mousePos, setMousePos] = useState({ x: 0, y: 0 });
+
+
+
+ // Update mouse position
+ useEffect(() => {
+ const handleMouseMove = (event) => {
+ const x = (event.clientX / size.width) * 2 - 1;
+ const y = -(event.clientY / size.height) * 2 + 1;
+ setMousePos({ x, y });
+ };
+
+ window.addEventListener("mousemove", handleMouseMove);
+ return () => window.removeEventListener("mousemove", handleMouseMove);
+ }, [size]);
+
+ // Create smoke particles
+ useEffect(() => {
+ const loader = new THREE.TextureLoader();
+
+ loader.load('https://s3-us-west-2.amazonaws.com/s.cdpn.io/95637/Smoke-Element.png', (smokeTexture) => {
+ const smokeGeo = new THREE.PlaneGeometry(300, 300);
+
+ for (let p = 0; p < 150; p++) {
+ const colors = [0xFFD700, 0xFACC15, 0xFFA500]; // Gold, yellow-400, amber
+ const randomColor = colors[Math.floor(Math.random() * colors.length)];
+
+ const smokeMaterial = new THREE.MeshLambertMaterial({
+ color: randomColor,
+ map: smokeTexture,
+ transparent: true,
+ opacity: Math.random() * 0.6 + 0.4, // More vibrant
+ emissive: randomColor,
+ emissiveIntensity: 1.2, // Stronger glow
+ });
+
+ const particle = new THREE.Mesh(smokeGeo, smokeMaterial);
+ particle.position.set(
+ Math.random() * 500 - 250,
+ Math.random() * 500 - 250,
+ Math.random() * 1000 - 100
+ );
+ particle.rotation.z = Math.random() * 360;
+ particle.userData.originalPos = { ...particle.position };
+ groupRef.current.add(particle);
+ particlesRef.current.push(particle);
+ }
+ });
+
+ // Add a golden light for extra glow
+ const light = new THREE.PointLight(0xFFD700, 2.0, 2000);
+ light.position.set(0, 0, 500);
+ scene.add(light);
+
+ }, [scene]);
+
+ // Animation loop with smooth wind effect
+ useFrame(() => {
+ if (statsRef.current) statsRef.current.begin();
+
+ const delta = clockRef.current.getDelta();
+
+ particlesRef.current.forEach((particle) => {
+ if (particle) {
+ particle.rotation.z += delta * 0.2;
+
+ // Get particle position
+ const particlePos = particle.position;
+ const originalPos = particle.userData.originalPos;
+
+ // Calculate distance from mouse
+ const dx = (mousePos.x * 500) - particlePos.x;
+ const dy = (mousePos.y * 500) - particlePos.y;
+ const distance = Math.sqrt(dx * dx + dy * dy);
+
+ // Wind effect: move particles with a slight random offset
+ if (distance < 150) {
+ particlePos.x += dx * 0.02 + (Math.random() - 0.5) * 2;
+ particlePos.y += dy * 0.02 + (Math.random() - 0.5) * 2;
+ }
+
+ // Smoothly return particles to original position but with a slight offset
+ particlePos.x = THREE.MathUtils.lerp(particlePos.x, originalPos.x + (Math.random() - 0.5) * 5, delta);
+ particlePos.y = THREE.MathUtils.lerp(particlePos.y, originalPos.y + (Math.random() - 0.5) * 5, delta);
+ }
+ });
+
+ if (statsRef.current) statsRef.current.end();
+ });
+
+ return <group ref={groupRef} />;
+}
+
+export default function SmokeEffect() {
+ return (
+ <Canvas
+ style={{
+ position: 'absolute',
+ top: 0,
+ left: 0,
+ width: '100vw',
+ height: '100vh',
+ zIndex: -10,
+ background: 'white'
+ }}
+ camera={{
+ fov: 75,
+ position: [0, 0, 1000],
+ near: 1,
+ far: 10000
+ }}
+ >
+ <SmokeParticles />
+ </Canvas>
+ );
+}
diff --git a/client/src/components/ui/info-card.tsx b/client/src/components/ui/info-card.tsx
new file mode 100644
index 0000000..f966314
--- /dev/null
+++ b/client/src/components/ui/info-card.tsx
@@ -0,0 +1,41 @@
+import React from "react";
+import { motion } from "framer-motion";
+
+interface CardProps {
+ number: string;
+ heading: string;
+ description: string;
+ numberBgColor: string;
+ numberTextColor: string;
+}
+
+const InfoCard: React.FC<CardProps> = ({
+ number,
+ heading,
+ description,
+ numberBgColor,
+ numberTextColor,
+}) => {
+ return (
+ <motion.div
+ className="w-full p-6 my-4 bg-white rounded-lg shadow-md"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ <div className="flex items-center space-x-4">
+ <div
+ className="w-12 h-12 flex items-center justify-center rounded-md font-orbitron text-xl font-bold"
+ style={{ backgroundColor: numberBgColor, color: numberTextColor }}
+ >
+ <p>{number}</p>
+ </div>
+ <h3 className="text-2xl font-semibold text-gray-800">{heading}</h3>
+ </div>
+ <p className="mt-4 text-lg text-left text-gray-600">{description}</p>
+ </motion.div>
+ );
+};
+
+export default InfoCard;
diff --git a/client/src/components/ui/navbar-menu.tsx b/client/src/components/ui/navbar-menu.tsx
new file mode 100644
index 0000000..88cc418
--- /dev/null
+++ b/client/src/components/ui/navbar-menu.tsx
@@ -0,0 +1,119 @@
+import React from "react";
+import { motion } from "framer-motion";
+import {Link} from "react-router-dom"
+
+const transition = {
+ type: "spring",
+ mass: 0.5,
+ damping: 11.5,
+ stiffness: 100,
+ restDelta: 0.001,
+ restSpeed: 0.001,
+};
+
+export const MenuItem = ({
+ setActive,
+ active,
+ item,
+ children,
+}: {
+ setActive: (item: string) => void;
+ active: string | null;
+ item: string;
+ children?: React.ReactNode;
+}) => {
+ return (
+ <div onMouseEnter={() => setActive(item)} className="relative ">
+ <motion.p
+ transition={{ duration: 0.3 }}
+ className="cursor-pointer text-black hover:opacity-[0.9]"
+ >
+ {item}
+ </motion.p>
+ {active !== null && (
+ <motion.div
+ initial={{ opacity: 0, scale: 0.85, y: 10 }}
+ animate={{ opacity: 1, scale: 1, y: 0 }}
+ transition={transition}
+ >
+ {active === item && (
+ <div className="absolute top-[calc(100%_+_1.2rem)] left-1/2 transform -translate-x-1/2 pt-4">
+ <motion.div
+ transition={transition}
+ layoutId="active" // layoutId ensures smooth animation
+ className="bg-white backdrop-blur-sm rounded-2xl overflow-hidden border border-black/[0.2] shadow-xl"
+ >
+ <motion.div
+ layout // layout ensures smooth animation
+ className="w-max h-full p-4"
+ >
+ {children}
+ </motion.div>
+ </motion.div>
+ </div>
+ )}
+ </motion.div>
+ )}
+ </div>
+ );
+};
+
+export const Menu = ({
+ setActive,
+ children,
+}: {
+ setActive: (item: string | null) => void;
+ children: React.ReactNode;
+}) => {
+ return (
+ <nav
+ onMouseLeave={() => setActive(null)} // resets the state
+ className="relative rounded-xl border border-transparent shadow-input space-x-4 px-8 py-4 "
+ >
+ {children}
+ </nav>
+ );
+};
+
+export const ProductItem = ({
+ title,
+ description,
+ href,
+ src,
+}: {
+ title: string;
+ description: string;
+ href: string;
+ src: string;
+}) => {
+ return (
+ <Link to={href} className="flex space-x-2">
+ <img
+ src={src}
+ width={140}
+ height={70}
+ alt={title}
+ className="flex-shrink-0 rounded-md shadow-2xl"
+ />
+ <div>
+ <h4 className="text-xl font-bold mb-1 text-black">
+ {title}
+ </h4>
+ <p className="text-neutral-700 text-sm max-w-[10rem]">
+ {description}
+ </p>
+ </div>
+ </Link>
+ );
+};
+
+export const HoveredLink = ({ children, ...rest }: any) => {
+ return (
+ <Link
+ {...rest}
+ className="text-neutral-700 hover:text-black "
+ >
+ {children}
+ </Link>
+ );
+};
diff --git a/client/src/context/AgentContext.tsx b/client/src/context/AgentContext.tsx
new file mode 100644
index 0000000..051f2f3
--- /dev/null
+++ b/client/src/context/AgentContext.tsx
@@ -0,0 +1,27 @@
+import React, { createContext, useContext, useState } from 'react';
+import { Agent } from '../interfaces/AgentInterfaces';
+
+interface AgentContextType {
+ selectedAgent: Agent | null;
+ setSelectedAgent: (agent: Agent | null) => void;
+}
+
+const AgentContext = createContext<AgentContextType | undefined>(undefined);
+
+export const AgentProvider: React.FC<{ children: React.ReactNode }> = ({ children }) => {
+ const [selectedAgent, setSelectedAgent] = useState<Agent | null>(null);
+
+ return (
+ <AgentContext.Provider value={{ selectedAgent, setSelectedAgent }}>
+ {children}
+ </AgentContext.Provider>
+ );
+};
+
+export const useAgent = () => {
+ const context = useContext(AgentContext);
+ if (context === undefined) {
+ throw new Error('useAgent must be used within an AgentProvider');
+ }
+ return context;
+};
\ No newline at end of file
diff --git a/client/src/hooks/useCharacterSelection.ts b/client/src/hooks/useCharacterSelection.ts
new file mode 100644
index 0000000..3f93191
--- /dev/null
+++ b/client/src/hooks/useCharacterSelection.ts
@@ -0,0 +1,30 @@
+import { useState } from 'react';
+
+/**
+ * useCharacterSelection - A custom hook to manage character selection logic.
+ *
+ * Responsibilities:
+ * 1. Manages the `selectedCharacter` state.
+ * 2. Provides a function (`handleCharacterSelect`) to update the selected character.
+ * 3. Ensures the selected character data is validated before updating.
+ */
+const useCharacterSelection = () => {
+ const [selectedCharacter, setSelectedCharacter] = useState(null); // Holds the currently selected character.
+
+ /**
+ * handleCharacterSelect - Updates the selected character.
+ *
+ * @param character - The character to select.
+ */
+ const handleCharacterSelect = (character: any) => {
+ if (!character?.agent?.agent_details) {
+ console.error('Invalid character data'); // Log an error if the character data structure is invalid.
+ return;
+ }
+ setSelectedCharacter(character); // Update the selected character state.
+ };
+
+ return { selectedCharacter, handleCharacterSelect, setSelectedCharacter }; // Return the state and handlers for character selection.
+};
+
+export default useCharacterSelection;
diff --git a/client/src/hooks/useCharacters.ts b/client/src/hooks/useCharacters.ts
new file mode 100644
index 0000000..af60b0f
--- /dev/null
+++ b/client/src/hooks/useCharacters.ts
@@ -0,0 +1,37 @@
+import useFetchCharacters from './useFetchCharacters';
+import useProcessCharacters from './useProcessCharacters';
+import useCharacterSelection from './useCharacterSelection';
+import { useEffect } from 'react';
+
+/**
+ * useCharacters - A composed custom hook to manage character data and selection.
+ *
+ * Responsibilities:
+ * 1. Fetches raw character data using `useFetchCharacters`.
+ * 2. Processes raw character data into a usable format using `useProcessCharacters`.
+ * 3. Manages character selection logic using `useCharacterSelection`.
+ * 4. Automatically selects the first character by default when characters are loaded.
+ */
+const useCharacters = () => {
+ const { characters: rawCharacters, loading, error } = useFetchCharacters(); // Fetch raw characters and track fetch state.
+ const processedCharacters = useProcessCharacters(rawCharacters); // Process the raw characters into a usable format.
+ const { selectedCharacter, handleCharacterSelect, setSelectedCharacter } = useCharacterSelection(); // Manage character selection.
+
+ // Automatically select the first character when characters are loaded.
+ useEffect(() => {
+ if (!selectedCharacter && processedCharacters.length > 0) {
+ handleCharacterSelect(processedCharacters[0]); // Select the first character by default.
+ }
+ }, [processedCharacters, selectedCharacter, handleCharacterSelect]);
+
+ return {
+ characters: processedCharacters, // The processed characters data.
+ selectedCharacter, // The currently selected character.
+ setSelectedCharacter, // Function to manually update the selected character.
+ loading, // Whether the characters are still being fetched.
+ error, // Any error that occurred during the fetch.
+ handleCharacterSelect, // Function to select a character.
+ };
+};
+
+export default useCharacters;
diff --git a/client/src/hooks/useFetchCharacters.ts b/client/src/hooks/useFetchCharacters.ts
new file mode 100644
index 0000000..6ecc5ba
--- /dev/null
+++ b/client/src/hooks/useFetchCharacters.ts
@@ -0,0 +1,42 @@
+import { useState, useEffect } from 'react';
+import { getCharacters } from '../api/agentsAPI';
+import { Agent } from '../interfaces/AgentInterfaces';
+
+/**
+ * useFetchCharacters - A custom hook to fetch characters from the API.
+ *
+ * Responsibilities:
+ * 1. Handles the API call to GET/fetch character data.
+ * 2. Manages the state for `characters`, `loading`, and `error`.
+ * 3. Provides feedback on the fetch process (loading and error states).
+ */
+const useFetchCharacters = () => {
+const [characters, setCharacters] = useState<Agent[]>([]); // Explicitly typed with your Agent interface
+const [loading, setLoading] = useState(true); // Tracks whether the API call is in progress.
+ const [error, setError] = useState<Error | null>(null); // Tracks any errors during the fetch process.
+
+ useEffect(() => {
+ const fetchCharacters = async () => {
+ setLoading(true); // Mark the fetch as in progress.
+ try {
+ const data = await getCharacters(); // Call the API to fetch characters.
+ if (!Array.isArray(data)) {
+ throw new Error(`Expected array, received: ${typeof data}`); // Validate the response type.
+ }
+ setCharacters(data); // Save the fetched data to state.
+ } catch (err) {
+ // Handle errors during the fetch process.
+ const error = err instanceof Error ? err : new Error('Failed to fetch characters');
+ setError(error);
+ } finally {
+ setLoading(false); // Mark the fetch as complete, regardless of success or failure.
+ }
+ };
+
+ fetchCharacters(); // Execute the fetch when the hook is used.
+ }, []); // Dependency array is empty to ensure this runs only once on component mount.
+
+ return { characters, loading, error }; // Return the characters data and state for use in components.
+};
+
+export default useFetchCharacters;
diff --git a/client/src/hooks/useProcessCharacters.ts b/client/src/hooks/useProcessCharacters.ts
new file mode 100644
index 0000000..793a841
--- /dev/null
+++ b/client/src/hooks/useProcessCharacters.ts
@@ -0,0 +1,44 @@
+/**
+ * useProcessCharacters - A custom hook to transform raw character data into a usable format.
+ *
+ * Responsibilities:
+ * 1. Maps and processes raw data from the API.
+ * 2. Ensures all necessary fields are structured and default values are applied.
+ * 3. Provides a consistent and clean data structure for use in the application.
+ */
+const useProcessCharacters = (characters: any[]) => {
+ const processedCharacters = characters.map((char) => {
+ // Char is the raw data from the API
+ // Destructure the char object to extract the necessary fields
+ const { agent } = char;
+
+ return {
+ agent: {
+ agent_details: {
+ name: agent?.agent_details?.name || '',
+ personality: agent?.agent_details?.personality || [],
+ communication_style: agent?.agent_details?.communication_style || [],
+ backstory: agent?.agent_details?.backstory || '',
+ universe: agent?.agent_details?.universe || '',
+ topic_expertise: agent?.agent_details?.topic_expertise || [],
+ hashtags: agent?.agent_details?.hashtags || [],
+ emojis: agent?.agent_details?.emojis || [],
+ concept: agent?.concept || '',
+ },
+ profile_image: agent?.profile_image || {},
+ concept: agent?.concept || '',
+ profile_image_options: agent?.profile_image_options || [],
+ ai_model: agent?.ai_model || {},
+ connectors: agent?.connectors || {},
+ seasons: agent?.seasons || [],
+ tracker: agent?.tracker || {},
+ master_file_path: agent?.master_file_path || '',
+ }
+ };
+ });
+
+ return processedCharacters; // Return the processed data for use in the application.
+ };
+
+ export default useProcessCharacters;
+
\ No newline at end of file
diff --git a/client/src/index.css b/client/src/index.css
new file mode 100644
index 0000000..b6f33b5
--- /dev/null
+++ b/client/src/index.css
@@ -0,0 +1,26 @@
+@import url("https://fonts.googleapis.com/css2?family=Orbitron:wght@400..900&display=swap");
+@import url('https://fonts.googleapis.com/css2?family=Roboto:ital,wght@0,100..900;1,100..900&display=swap');
+
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+.font-orbitron{
+ font-family: "Orbitron", sans-serif;
+}
+
+/* @layer base {
+ body {
+ @apply m-0 min-h-screen bg-slate-950;
+ font-family: Inter, system-ui, Avenir, Helvetica, Arial, sans-serif;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ }
+}
+
+/* Only keep custom styles that can't be handled by Tailwind */
+/* @layer components {
+ .gradient-text {
+ @apply bg-gradient-to-r from-cyan-400 via-orange-400 to-red-400 bg-clip-text text-transparent;
+ }
+} */
diff --git a/client/src/interfaces/AgentInterfaces.ts b/client/src/interfaces/AgentInterfaces.ts
new file mode 100644
index 0000000..30e3e01
--- /dev/null
+++ b/client/src/interfaces/AgentInterfaces.ts
@@ -0,0 +1,143 @@
+export interface AgentDetails {
+ id?: string | number;
+ name: string;
+ personality: string[];
+ communication_style: string[];
+ backstory: string;
+ universe: string;
+ topic_expertise: string[];
+ hashtags: string[];
+ emojis: string[];
+}
+
+export interface ProfileImage {
+ details: {
+ url: string;
+ image_id: string;
+ generationId: string;
+ };
+ [key: string]: unknown;
+}
+
+export interface GeneratedImage {
+ url: string;
+ id: string;
+ generationId: string;
+}
+
+export interface GenerationsByPk {
+ id?: string;
+ prompt?: string;
+ generated_images: GeneratedImage[];
+}
+
+export interface ProfileImageOption {
+ generations_by_pk: GenerationsByPk;
+}
+
+export interface Agent {
+ id?: string | number;
+ name?: string;
+ avatar?: string;
+ shortDescription?: string;
+ tags?: string[];
+ personality?: string[];
+ communicationStyle?: string[];
+ emojis?: string[];
+ hashtags?: string[];
+ universe?: string;
+ backstory?: string;
+ concept?: string;
+ role?: string;
+ isLoading?: boolean;
+ leonardoResponse?: any;
+ leonardoImage?: any;
+ topic_expertise?: string[];
+ agent?: {
+ concept: string;
+ agent_details: AgentDetails;
+ ai_model: {
+ memory_store: string;
+ model_name: string;
+ model_type: string;
+ };
+ connectors: {
+ discord: boolean;
+ telegram: boolean;
+ twitter: boolean;
+ };
+ tracker: {
+ messages_sent: number;
+ total_interactions: number;
+ current_episode_number: number;
+ current_post_number: number;
+ current_season_number: number;
+ post_every_x_minutes: number;
+ };
+ seasons: any[];
+ profile_image: ProfileImage;
+ profile_image_options: ProfileImageOption[];
+ master_file_path?: string;
+ };
+ isExample?: boolean;
+}
+
+export function createBlankAgent(): Agent {
+ return {
+ id: '',
+ name: '',
+ avatar: '',
+ agent: {
+ concept: '',
+ agent_details: {
+ backstory: '',
+ communication_style: [],
+ emojis: [],
+ hashtags: [],
+ name: '',
+ personality: [],
+ topic_expertise: [],
+ universe: ''
+ },
+ ai_model: {
+ memory_store: '',
+ model_name: '',
+ model_type: ''
+ },
+ connectors: {
+ discord: false,
+ telegram: false,
+ twitter: false
+ },
+ tracker: {
+ messages_sent: 0,
+ total_interactions: 0,
+ current_episode_number: 0,
+ current_post_number: 0,
+ current_season_number: 0,
+ post_every_x_minutes: 0
+ },
+ seasons: [],
+ profile_image: {
+ details: {
+ url: '',
+ image_id: '',
+ generationId: ''
+ }
+ },
+ profile_image_options: [] as ProfileImageOption[]
+ }
+ };
+}
+
+export interface RandomAgentCardProps {
+ agent: Agent;
+ onSelect: (agent: Agent | null) => void;
+ onAddAgent: (agent: Agent) => void;
+ isUserAgent: boolean;
+ setRandomAgents: React.Dispatch<React.SetStateAction<Agent[]>>;
+ generateRandomAgentData: () => Promise<Agent>;
+ isLoadedAgent: boolean;
+ onRegenerate: (agentId: string) => Promise<void>;
+ isLoading?: boolean;
+}
\ No newline at end of file
diff --git a/client/src/interfaces/ChatInterfaces.ts b/client/src/interfaces/ChatInterfaces.ts
new file mode 100644
index 0000000..63304e8
--- /dev/null
+++ b/client/src/interfaces/ChatInterfaces.ts
@@ -0,0 +1,11 @@
+export interface Message {
+ role: string;
+ message?: string;
+ response?: string;
+ message_id: number;
+}
+
+export interface ChatHistory {
+ agent_name: string;
+ chat_history: Message[];
+}
\ No newline at end of file
diff --git a/client/src/interfaces/LeonardoInterfaces.ts b/client/src/interfaces/LeonardoInterfaces.ts
new file mode 100644
index 0000000..28ecb77
--- /dev/null
+++ b/client/src/interfaces/LeonardoInterfaces.ts
@@ -0,0 +1,8 @@
+// Define the payload type for better type-checking
+export interface LambdaPayload {
+ prompt: string;
+ modelId: string;
+ styleUUID: string;
+ num_images: number;
+ }
+
diff --git a/client/src/interfaces/PostsInterface.ts b/client/src/interfaces/PostsInterface.ts
new file mode 100644
index 0000000..7e1b57b
--- /dev/null
+++ b/client/src/interfaces/PostsInterface.ts
@@ -0,0 +1,21 @@
+export interface Post {
+ post_id: string;
+ post_number: number;
+ post_content: string;
+ post_highlights?: string;
+ post_posted: boolean;
+ seasonNumber?: number;
+ episodeNumber?: number;
+ episodeName?: string;
+ }
+
+export interface Episode {
+ episode_number: number;
+ episode_name: string;
+ posts: Post[];
+}
+
+export interface Season {
+ season_number: number;
+ episodes: Episode[];
+}
\ No newline at end of file
diff --git a/client/src/interfaces/SeasonInterfaces.ts b/client/src/interfaces/SeasonInterfaces.ts
new file mode 100644
index 0000000..00a5b5c
--- /dev/null
+++ b/client/src/interfaces/SeasonInterfaces.ts
@@ -0,0 +1,29 @@
+export interface Post {
+ post_id: string;
+ post_number: number;
+ post_content: string;
+ post_highlights: string;
+ post_posted: boolean;
+}
+
+export interface Episode {
+ episode_name: string;
+ episode_number: number;
+ episode_description: string;
+ episode_highlights: string;
+ episode_summary: string;
+ episode_posted: boolean;
+ current_post_number: number;
+ posts: Post[];
+}
+
+export interface Season {
+ season_name: string;
+ season_number: number;
+ season_description: string;
+ season_highlights: string;
+ season_summary: string;
+ season_posted: false;
+ current_episode_number: number;
+ episodes: Episode[];
+}
\ No newline at end of file
diff --git a/client/src/interfaces/SocialFeedInterfaces.ts b/client/src/interfaces/SocialFeedInterfaces.ts
new file mode 100644
index 0000000..e69de29
diff --git a/client/src/interfaces/TraitButtonsProps.ts b/client/src/interfaces/TraitButtonsProps.ts
new file mode 100644
index 0000000..71afab9
--- /dev/null
+++ b/client/src/interfaces/TraitButtonsProps.ts
@@ -0,0 +1,7 @@
+import { AgentDetails } from './AgentInterfaces';
+
+export interface TraitButtonsProps {
+ field: keyof AgentDetails;
+ options: string[];
+ onTraitButtonClick: (field: keyof AgentDetails, value: string) => void;
+}
\ No newline at end of file
diff --git a/client/src/lib/utils.ts b/client/src/lib/utils.ts
new file mode 100644
index 0000000..cec6ac9
--- /dev/null
+++ b/client/src/lib/utils.ts
@@ -0,0 +1,6 @@
+import { ClassValue, clsx } from "clsx";
+import { twMerge } from "tailwind-merge";
+
+export function cn(...inputs: ClassValue[]) {
+ return twMerge(clsx(inputs));
+}
diff --git a/client/src/main.tsx b/client/src/main.tsx
new file mode 100644
index 0000000..85624e3
--- /dev/null
+++ b/client/src/main.tsx
@@ -0,0 +1,20 @@
+import { StrictMode } from 'react'
+import { createRoot } from 'react-dom/client'
+import './index.css'
+import App from './App.tsx'
+import { BrowserRouter as Router } from 'react-router-dom';
+import Providers from './components/providers.tsx';
+import { AgentProvider } from './context/AgentContext.tsx';
+
+
+createRoot(document.getElementById('root')!).render(
+ <StrictMode>
+ <Providers>
+ <AgentProvider>
+ <Router>
+ <App />
+ </Router>
+ </AgentProvider>
+ </Providers>
+ </StrictMode>,
+)
diff --git a/client/src/pages/AgentCreator.tsx b/client/src/pages/AgentCreator.tsx
new file mode 100644
index 0000000..a26e8a6
--- /dev/null
+++ b/client/src/pages/AgentCreator.tsx
@@ -0,0 +1,999 @@
+import React, { useState, ChangeEvent, useEffect, KeyboardEvent } from "react";
+import { Brain, Wand2, MessageSquare, Save, RefreshCcw, Layers } from "lucide-react";
+import { createAgent, getCharacters } from "../api/agentsAPI";
+import {
+ GeneratedImage,
+ ProfileImageOption,
+} from "../interfaces/AgentInterfaces";
+import TraitButtons from "../components/TraitButtons"; // We'll still use your TraitButtons
+import useCharacters from "../hooks/useCharacters";
+import { inconsistentImageLambda } from "../api/leonardoApi";
+import LoadingBar from "../components/LoadingBar";
+import Sidebar from "../components/sidebar";
+import { motion } from "framer-motion";
+
+const LEONARDO_MODEL_ID = "e71a1c2f-4f80-4800-934f-2c68979d8cc8";
+const LEONARDO_STYLE_UUID = "b2a54a51-230b-4d4f-ad4e-8409bf58645f";
+
+
+/**
+ * AgentCreator Component
+ * A form-based interface for creating and editing AI agents with various attributes
+ * including personality traits, communication style, and profile images.
+ */
+const AgentCreator: React.FC = () => {
+
+ /**
+ * Main UI state management
+ * activeTab controls which section of the form is visible:
+ * - basic: name, universe, expertise
+ * - personality: personality traits, backstory
+ * - style: communication style, hashtags, emojis
+ */
+ const [activeTab, setActiveTab] = useState<"basic" | "personality" | "style">(
+ "basic"
+ );
+
+ /**
+ * Core agent state
+ * Maintains the complete agent object including:
+ * - agent_details: main characteristics and traits
+ * - profile_image: currently selected image
+ * - profile_image_options: available image choices
+ * - selectedImage: index of chosen image
+ * - seasons: associated seasons/episodes
+ */
+ const [agent, setAgent] = useState<{
+ agent_details: {
+ name: string;
+ personality: string[];
+ communication_style: string[];
+ backstory: string;
+ universe: string;
+ topic_expertise: string[];
+ hashtags: string[];
+ emojis: string[];
+ concept: string;
+ };
+ profile_image: {
+ details: {
+ url: string;
+ image_id: string;
+ generationId: string;
+ };
+ };
+ profile_image_options: ProfileImageOption[];
+ selectedImage: number | undefined;
+ seasons: any[];
+ }>({
+ agent_details: {
+ name: "",
+ personality: [],
+ communication_style: [],
+ backstory: "",
+ universe: "",
+ topic_expertise: [],
+ hashtags: [],
+ emojis: [],
+ concept: "",
+ },
+ profile_image: {
+ details: {
+ url: "",
+ image_id: "",
+ generationId: "",
+ },
+ },
+ profile_image_options: [],
+ selectedImage: undefined,
+ seasons: [],
+ });
+
+ // The fetched characters
+ const { characters, loading, error } = useCharacters();
+
+ /**
+ * Draft field management
+ * Maintains temporary states for text fields before they're committed to the main agent state
+ * Prevents immediate updates and allows for Enter-to-commit functionality
+ */
+ const [draftFields, setDraftFields] = useState({
+ name: "",
+ universe: "",
+ backstory: "",
+ imageDescription: "",
+ });
+
+ /**
+ * Synchronization Effects
+ * Keep draft states in sync with the main agent state
+ * Ensures drafts are updated when agent data changes
+ */
+ useEffect(() => {
+ setDraftFields({
+ name: agent.agent_details.name || "",
+ universe: agent.agent_details.universe || "",
+ backstory: agent.agent_details.backstory || "",
+ imageDescription:
+ agent.profile_image_options?.[0]?.generations_by_pk?.prompt || "",
+ });
+ }, [agent]);
+
+ /**
+ * Draft traits management
+ * Handles temporary states for array-based fields (traits, hashtags, etc.)
+ * Stores them as comma-separated strings until committed
+ */
+ const [draftTraits, setDraftTraits] = useState<{
+ topic_expertise: string;
+ personality: string;
+ communication_style: string;
+ hashtags: string;
+ emojis: string;
+ }>({
+ topic_expertise: "",
+ personality: "",
+ communication_style: "",
+ hashtags: "",
+ emojis: "",
+ });
+
+ /**
+ * Synchronization Effects
+ * Keep draft states in sync with the main agent state
+ * Ensures drafts are updated when agent data changes
+ */
+ useEffect(() => {
+ setDraftTraits({
+ topic_expertise: agent.agent_details.topic_expertise.join(", "),
+ personality: agent.agent_details.personality.join(", "),
+ communication_style: agent.agent_details.communication_style.join(", "),
+ hashtags: agent.agent_details.hashtags.join(", "),
+ emojis: agent.agent_details.emojis.join(" "),
+ });
+ }, [agent]);
+
+ /**
+ * Profile Image Management
+ * Handles initialization and updates of the agent's profile image
+ * Sets default placeholder if no images are available
+ */
+ useEffect(() => {
+ if (agent.profile_image_options.length > 0) {
+ const firstImage =
+ agent.profile_image_options[0]?.generations_by_pk
+ ?.generated_images?.[0] ??
+ ({
+ url: "https://via.placeholder.com/400x400?text=Brain+Placeholder",
+ id: "",
+ generationId: "",
+ } as GeneratedImage);
+
+ setAgent((prev) => ({
+ ...prev,
+ selectedImage:
+ prev.selectedImage !== undefined ? prev.selectedImage : 0,
+ profile_image: {
+ details: {
+ url: firstImage.url,
+ image_id: firstImage.id,
+ generationId: firstImage.generationId,
+ },
+ },
+ }));
+ } else {
+ setAgent((prev) => ({
+ ...prev,
+ profile_image: {
+ details: {
+ url: "https://via.placeholder.com/400x400?text=Brain+Placeholder",
+ image_id: "",
+ generationId: "",
+ },
+ },
+ }));
+ }
+ }, [agent.profile_image_options]);
+
+ /**
+ * Field Update Handlers
+ * Manages updates to regular text fields (name, universe, backstory)
+ * Commits changes when Enter is pressed
+ */
+ const handleDraftChange =
+ (field: keyof typeof draftFields) =>
+ (e: ChangeEvent<HTMLTextAreaElement | HTMLInputElement>) => {
+ setDraftFields((prev) => ({
+ ...prev,
+ [field]: e.target.value,
+ }));
+ };
+
+ const handleDraftKeyDown =
+ (field: keyof typeof draftFields) =>
+ (e: KeyboardEvent<HTMLTextAreaElement | HTMLInputElement>) => {
+ if (e.key === "Enter") {
+ e.preventDefault();
+
+ if (field === "imageDescription") {
+ setAgent((prev) => {
+ const newImageOption: ProfileImageOption = {
+ generations_by_pk: {
+ prompt: draftFields[field],
+ generated_images: [],
+ },
+ };
+
+ return {
+ ...prev,
+ profile_image_options: prev.profile_image_options?.length
+ ? prev.profile_image_options.map((option, index) =>
+ index === 0
+ ? {
+ ...option,
+ generations_by_pk: {
+ ...option.generations_by_pk,
+ prompt: draftFields[field],
+ },
+ }
+ : option
+ )
+ : [newImageOption],
+ };
+ });
+ } else {
+ setAgent((prev) => ({
+ ...prev,
+ agent_details: {
+ ...prev.agent_details,
+ [field]: draftFields[field],
+ },
+ }));
+ }
+ }
+ };
+
+ /**
+ * Trait Field Handlers
+ * Manages updates to array-based fields (personality, hashtags, etc.)
+ * Splits input by commas (or spaces for emojis) and commits on Enter
+ */
+ const handleTraitDraftChange =
+ (field: keyof typeof draftTraits) =>
+ (e: ChangeEvent<HTMLTextAreaElement>) => {
+ setDraftTraits((prev) => ({
+ ...prev,
+ [field]: e.target.value,
+ }));
+ };
+
+ const handleTraitDraftKeyDown =
+ (field: keyof typeof draftTraits) =>
+ (e: KeyboardEvent<HTMLTextAreaElement>) => {
+ if (e.key === "Enter") {
+ e.preventDefault();
+ // If field is "emojis", we split by space; otherwise, by comma
+ const separator = field === "emojis" ? " " : ",";
+ const arrayValue = draftTraits[field]
+ .split(separator)
+ .map((item) => item.trim())
+ .filter(Boolean);
+
+ setAgent((prev) => ({
+ ...prev,
+ agent_details: {
+ ...prev.agent_details,
+ [field]: arrayValue,
+ },
+ }));
+ }
+ };
+
+ // Deleting a single trait
+ type TraitField =
+ | "personality"
+ | "communication_style"
+ | "topic_expertise"
+ | "hashtags"
+ | "emojis";
+
+ const handleDeleteTrait = (field: TraitField, value: string) => {
+ setAgent((prev) => ({
+ ...prev,
+ agent_details: {
+ ...prev.agent_details,
+ [field]: prev.agent_details[field].filter(
+ (trait: string) => trait !== value
+ ),
+ },
+ }));
+ };
+
+ // State to manage the visibility of the success message
+ const [showSuccessMessage, setShowSuccessMessage] = useState(false);
+
+ // Add state for loading progress near other state declarations
+ const [loadingProgress, setLoadingProgress] = useState(0);
+
+ // Add a new state for tracking image generation
+ const [isGenerating, setIsGenerating] = useState(false);
+
+ const [selectedCharacterIndex, setSelectedCharacterIndex] =
+ useState<number>(-1);
+
+ /**
+ * Form Submission Handler
+ * Processes the final agent data and sends it to the server
+ * Shows success message on completion
+ */
+ const handleSubmitCreateAgent = async (
+ event:
+ | React.FormEvent<HTMLFormElement>
+ | React.MouseEvent<HTMLButtonElement>
+ ) => {
+ event.preventDefault();
+
+ type AgentState = typeof agent;
+
+ const updatedAgent: AgentState = {
+ ...agent,
+ agent_details: {
+ ...agent.agent_details,
+ name: draftFields.name || agent.agent_details.name,
+ universe: draftFields.universe || agent.agent_details.universe,
+ backstory: draftFields.backstory || agent.agent_details.backstory,
+ concept: agent.agent_details.concept,
+
+ personality: draftTraits.personality
+ ? draftTraits.personality
+ .split(",")
+ .map((item) => item.trim())
+ .filter(Boolean)
+ : agent.agent_details.personality,
+
+ communication_style: draftTraits.communication_style
+ ? draftTraits.communication_style
+ .split(",")
+ .map((item) => item.trim())
+ .filter(Boolean)
+ : agent.agent_details.communication_style,
+
+ topic_expertise: draftTraits.topic_expertise
+ ? draftTraits.topic_expertise
+ .split(",")
+ .map((item) => item.trim())
+ .filter(Boolean)
+ : agent.agent_details.topic_expertise,
+
+ hashtags: draftTraits.hashtags
+ ? draftTraits.hashtags
+ .split(",")
+ .map((item) => item.trim())
+ .filter(Boolean)
+ : agent.agent_details.hashtags,
+
+ emojis: draftTraits.emojis
+ ? draftTraits.emojis.split(" ").filter(Boolean)
+ : agent.agent_details.emojis,
+ },
+ profile_image: agent.profile_image,
+ profile_image_options: agent.profile_image_options.map((option, index) =>
+ index === 0
+ ? {
+ ...option,
+ generations_by_pk: {
+ ...option.generations_by_pk,
+ prompt:
+ draftFields.imageDescription ||
+ option.generations_by_pk?.prompt,
+ },
+ }
+ : option
+ ),
+ selectedImage: agent.selectedImage,
+ seasons: agent.seasons,
+ };
+
+ try {
+
+ await createAgent(updatedAgent);
+
+ setShowSuccessMessage(true);
+ setTimeout(() => setShowSuccessMessage(false), 3000);
+ setAgent(updatedAgent);
+ } catch (error) {
+ console.error("Error creating agent:", error);
+ }
+ };
+
+ //
+ // ──────────────────────────────────────────────────────────────────────────────
+ // 8) Load characters
+ // ──────────────────────────────────────────────────────────────────────────────
+ //
+ useEffect(() => {
+ const loadCharacters = async () => {
+ try {
+ const charactersData = await getCharacters();
+ if (!Array.isArray(charactersData)) {
+ console.error(
+ "Expected array of characters, received:",
+ typeof charactersData
+ );
+ return;
+ }
+
+ const processed = charactersData.map((char) => {
+ const agentProfileImageOptions = char.agent.profile_image_options;
+ const agentConcept = char.concept;
+ const { agent } = char;
+ if (!agent) return { agent: {} };
+
+ const {
+ agent_details: {
+ name = "",
+ personality = [],
+ communication_style = [],
+ backstory = "",
+ universe = "",
+ topic_expertise = [],
+ hashtags = [],
+ emojis = [],
+ } = {},
+ ai_model = {},
+ connectors = {},
+ seasons = [],
+ tracker = {},
+ } = agent;
+
+ return {
+ agent: {
+ agent_details: {
+ name,
+ personality,
+ communication_style,
+ backstory,
+ universe,
+ topic_expertise,
+ hashtags,
+ emojis,
+ },
+ ai_model,
+ connectors,
+ profile_image: agentProfileImageOptions || [],
+ seasons,
+ tracker,
+ },
+ concept: agentConcept || "",
+ };
+ });
+ console.log("Processed characters:", processed);
+ } catch (error) {
+ console.error("Error loading characters:", error);
+ }
+ };
+
+ loadCharacters();
+ }, []);
+
+ /**
+ * Character Selection Handler
+ * Populates the form with data from an existing character
+ * Updates both main agent state and draft states
+ */
+ const handleCharacterSelect = (e: ChangeEvent<HTMLSelectElement>) => {
+ const selectedIndex = parseInt(e.target.value);
+ setSelectedCharacterIndex(selectedIndex);
+ const char = characters[selectedIndex];
+ if (!char?.agent?.agent_details) return;
+
+ const details = char.agent.agent_details;
+
+ setAgent({
+ agent_details: {
+ name: details.name || "",
+ personality: details.personality || [],
+ communication_style: details.communication_style || [],
+ backstory: details.backstory || "",
+ universe: details.universe || "",
+ topic_expertise: details.topic_expertise || [],
+ hashtags: details.hashtags || [],
+ emojis: details.emojis || [],
+ concept: details.concept || "",
+ },
+ profile_image: char.agent?.profile_image_options || [],
+ profile_image_options: char.agent?.profile_image_options || [],
+ selectedImage: char.agent?.profile_image_options?.[0]?.generations_by_pk
+ ?.generated_images?.length
+ ? 0
+ : undefined,
+ seasons: char.agent?.seasons || [],
+ });
+
+ // Sync local drafts
+ setDraftFields({
+ name: details.name || "",
+ universe: details.universe || "",
+ backstory: details.backstory || "",
+ imageDescription:
+ char.agent?.profile_image_options?.[0]?.generations_by_pk?.prompt || "",
+ });
+
+ setDraftTraits({
+ topic_expertise: (details.topic_expertise || []).join(", "),
+ personality: (details.personality || []).join(", "),
+ communication_style: (details.communication_style || []).join(", "),
+ hashtags: (details.hashtags || []).join(", "),
+ emojis: (details.emojis || []).join(" "),
+ });
+ };
+
+ //
+ // ──────────────────────────────────────────────────────────────────────────────
+ // 10) Render
+ // ──────────────────────────────────────────────────────────────────────────────
+ //
+ return (
+ <div className="flex h-screen">
+ <Sidebar />
+
+ <div className="w-full overflow-y-scroll flex flex-col min-h-screen bg-gray-50">
+ {/* Success Message */}
+
+ {showSuccessMessage && (
+ <div className="fixed inset-0 flex items-center justify-center z-50">
+ <div className="bg-gradient-to-r from-orange-600 to-red-600 text-white px-6 py-3 rounded-md shadow-lg">
+ Agent successfully saved!
+ </div>
+ </div>
+ )}
+
+ {/* div */}
+ <div className="p-5 my-4 mx-12 rounded-xl shadow-xl flex items-center justify-between">
+ <div className="text-3xl font-semibold text-gray-600 font-orbitron">
+ Create Agent
+ </div>
+ <div className="flex items-center space-x-3 justify-end">
+ </div>
+ </div>
+ <div className="flex-grow flex p-5 m-5">
+
+
+ {/* Left Panel */}
+ <div className="w-1/2 p-6 rounded-2xl h-fit shadow-xl bg-white">
+ <div className="flex gap-4 mb-6 bg-gray-50/80 p-2 rounded-lg">
+ {[
+ { id: "basic" as const, icon: Brain, label: "Basic Info" },
+ { id: "personality" as const, icon: Wand2, label: "Personality" },
+ { id: "style" as const, icon: MessageSquare, label: "Style" },
+ ].map(({ id, icon: Icon, label }) => (
+ <button
+ key={id}
+ onClick={() => {
+ setActiveTab(id);
+ }}
+ className={`flex-1 flex items-center justify-center px-4 py-2
+ rounded-md text-gray-700 ${activeTab === id
+ ? "bg-gradient-to-r from-[#F7F957] to-[#F9D02C]"
+ : "bg-gray-200"
+ }`}
+ >
+ <Icon className="w-4 h-4 mr-2" />
+ {label}
+ </button>
+ ))}
+ </div>
+
+ {/* Form */}
+ <div className="space-y-6">
+ {activeTab === "basic" && (
+ <div className="space-y-6">
+ {/* Agent Name => local draft */}
+ <div>
+ <label className="text-sm text-gray-700 font-medium block mb-2">
+ Agent Name
+ </label>
+ <textarea
+ value={draftFields.name}
+ onChange={handleDraftChange("name")}
+ onKeyDown={handleDraftKeyDown("name")}
+ placeholder="Enter agent name (Press Enter to commit)"
+ rows={2}
+ className="w-full px-3 py-2 rounded-md bg-gray-50/80 border border-orange-500/30 text-gray-800 focus:outline-none focus:ring-2 focus:ring-orange-500/50"
+ />
+ </div>
+
+ {/* Universe => local draft */}
+ <div>
+ <label className="text-sm text-gray-700 font-medium block mb-2">
+ Universe
+ </label>
+ <textarea
+ value={draftFields.universe}
+ onChange={handleDraftChange("universe")}
+ onKeyDown={handleDraftKeyDown("universe")}
+ placeholder="Enter universe (Press Enter to commit)"
+ rows={2}
+ className="w-full px-3 py-2 rounded-md bg-gray-50/80 border border-orange-500/30 text-gray-800 focus:outline-none focus:ring-2 focus:ring-orange-500/50"
+ />
+ </div>
+
+ {/* Topic Expertise => local draft => commit on Enter */}
+ <div>
+ <label className="text-sm text-gray-700 font-medium block mb-2">
+ Topic Expertise
+ </label>
+ <TraitButtons
+ field="topic_expertise"
+ options={agent.agent_details.topic_expertise}
+ onTraitButtonClick={handleDeleteTrait}
+ />
+ <textarea
+ value={draftTraits.topic_expertise}
+ onChange={handleTraitDraftChange("topic_expertise")}
+ onKeyDown={handleTraitDraftKeyDown("topic_expertise")}
+ placeholder="Comma-separated (e.g. 'AI, Robotics, Music') (Press Enter to commit)"
+ rows={2}
+ className="w-full px-3 py-2 rounded-md bg-gray-50/80 border border-orange-500/30 text-gray-800 focus:outline-none focus:ring-2 focus:ring-orange-500/50"
+ />
+ </div>
+ </div>
+ )}
+
+ {activeTab === "personality" && (
+ <div className="space-y-6">
+ {/* Personality => local draft => commit on Enter */}
+ <div>
+ <label className="text-sm text-gray-700 font-medium block mb-2">
+ Personality
+ </label>
+ <TraitButtons
+ field="personality"
+ options={agent.agent_details.personality}
+ onTraitButtonClick={handleDeleteTrait}
+ />
+ <textarea
+ value={draftTraits.personality}
+ onChange={handleTraitDraftChange("personality")}
+ onKeyDown={handleTraitDraftKeyDown("personality")}
+ placeholder="Comma-separated personality traits (Press Enter to commit)"
+ rows={2}
+ className="w-full px-3 py-2 rounded-md bg-gray-50/80 border border-orange-500/30 text-gray-700 focus:outline-none focus:ring-2 focus:ring-orange-500/50"
+ />
+ </div>
+
+ {/* Backstory => local draft */}
+ <div>
+ <label className="text-sm text-gray-700 font-medium block mb-2">
+ Backstory
+ </label>
+ <textarea
+ value={draftFields.backstory}
+ onChange={handleDraftChange("backstory")}
+ onKeyDown={handleDraftKeyDown("backstory")}
+ placeholder="Enter agent backstory (Press Enter to commit)"
+ rows={3}
+ className="w-full px-3 py-2 rounded-md bg-gray-50/80 border border-orange-500/30 text-gray-700 focus:outline-none focus:ring-2 focus:ring-orange-500/50"
+ />
+ </div>
+ </div>
+ )}
+
+ {activeTab === "style" && (
+ <div className="space-y-6">
+ {/* Communication Style => local draft => commit on Enter */}
+ <div>
+ <label className="text-sm text-gray-700 font-medium block mb-2">
+ Communication Style
+ </label>
+ <TraitButtons
+ field="communication_style"
+ options={agent.agent_details.communication_style}
+ onTraitButtonClick={handleDeleteTrait}
+ />
+ <textarea
+ value={draftTraits.communication_style}
+ onChange={handleTraitDraftChange("communication_style")}
+ onKeyDown={handleTraitDraftKeyDown("communication_style")}
+ placeholder="Comma-separated (Press Enter to commit)"
+ rows={2}
+ className="w-full px-3 py-2 rounded-md bg-gray-50/80 border border-orange-500/30 text-gray-700 focus:outline-none focus:ring-2 focus:ring-orange-500/50"
+ />
+ </div>
+
+ {/* Hashtags => local draft => commit on Enter */}
+ <div>
+ <label className="text-sm text-gray-700 font-medium block mb-2">
+ Hashtags
+ </label>
+ <TraitButtons
+ field="hashtags"
+ options={agent.agent_details.hashtags}
+ onTraitButtonClick={handleDeleteTrait}
+ />
+ <textarea
+ value={draftTraits.hashtags}
+ onChange={handleTraitDraftChange("hashtags")}
+ onKeyDown={handleTraitDraftKeyDown("hashtags")}
+ placeholder="Comma-separated #tags (Press Enter to commit)"
+ rows={2}
+ className="w-full px-3 py-2 rounded-md bg-gray-50/80 border border-orange-500/30 text-gray-700 focus:outline-none focus:ring-2 focus:ring-orange-500/50"
+ />
+ </div>
+
+ {/* Emojis => local draft => commit on Enter => splitted by space */}
+ <div>
+ <label className="text-sm text-gray-700 font-medium block mb-2">
+ Emojis
+ </label>
+ <TraitButtons
+ field="emojis"
+ options={agent.agent_details.emojis}
+ onTraitButtonClick={handleDeleteTrait}
+ />
+ <textarea
+ value={draftTraits.emojis}
+ onChange={handleTraitDraftChange("emojis")}
+ onKeyDown={handleTraitDraftKeyDown("emojis")}
+ placeholder="Split by space (e.g. '✨ 🚀') (Press Enter to commit)"
+ rows={2}
+ className="w-full px-3 py-2 rounded-md bg-gray-50/80 border border-orange-500/30 text-gray-700 focus:outline-none focus:ring-2 focus:ring-orange-500/50"
+ />
+ </div>
+ </div>
+ )}
+ </div>
+
+ <button
+ type="button"
+ onClick={(e) =>
+ handleSubmitCreateAgent(
+ e as unknown as React.FormEvent<HTMLFormElement>
+ )
+ }
+ className="mt-6 w-full px-4 py-2 rounded-md bg-gradient-to-r from-[#F7F957] to-[#F9D02C] hover:scale-95 font-semibold
+ text-gray-700 transition-all duration-300 flex items-center justify-center"
+ >
+ <Save className="w-4 h-4 mr-2" />
+ Save Agent
+ </button>
+
+ {/* Character Selection */}
+ <div className="mt-6 p-4 bg-gray-50/80 rounded-lg border border-orange-500/30">
+ <label className="text-sm text-gray-700 font-medium block mb-2">
+ Select Existing Character
+ </label>
+ {loading ? (
+ <div className="text-gray-100">Loading characters...</div>
+ ) : error ? (
+ <div className="text-red-400">
+ No Existing Agents - {error.message}
+ </div>
+ ) : (
+ <>
+ <select
+ className="w-full px-3 py-2 rounded-md bg-gray-50/80 border
+ border-orange-500/30 text-gray-700 focus:ring-2
+ focus:ring-orange-500/50 focus:outline-none"
+ onChange={handleCharacterSelect}
+ value={selectedCharacterIndex}
+ >
+ <option value={-1}>-- Select a Character --</option>
+ {characters.map((char, index) => (
+ <option key={index} value={index}>
+ {char.agent?.agent_details?.name || "Unnamed Character"}
+ </option>
+ ))}
+ </select>
+ </>
+ )}
+ </div>
+ </div>
+
+ {/* Right Panel */}
+ <div className="w-1/2 p-6 border-r border-orange-500/20">
+ <div className="h-full flex flex-col space-y-6">
+ {/* Main Character Image */}
+ <div
+ className="relative -mt-7 aspect-square rounded-lg flex items-center border-2 bg-yellow-400/50 justify-center"
+ style={{
+ backgroundImage:
+ agent.selectedImage !== undefined &&
+ agent.profile_image?.details?.url &&
+ loadingProgress === 0
+ ? `url(${agent.profile_image.details.url})`
+ : "none",
+ backgroundSize: "cover",
+ backgroundPosition: "center",
+ }}
+ >
+ {loadingProgress > 0 && (
+ <div className="absolute inset-0 flex items-center justify-center bg-slate-900/80">
+ <div className="w-3/4">
+ <LoadingBar progress={loadingProgress} />
+ </div>
+ </div>
+ )}
+ {agent.selectedImage === undefined && loadingProgress === 0 && (
+ <Layers className="w-32 h-32 text-amber-500" />
+ )}
+ <button
+ className={`absolute bottom-4 right-4 px-4 py-2 rounded-md bg-gradient-to-r from-[#F7F957] to-[#F9D02C] text-gray-700 flex items-center
+ ${isGenerating
+ ? "opacity-50 cursor-not-allowed"
+ : "hover:scale-95"
+ }`}
+ onClick={async () => {
+ if (isGenerating) return; // Prevent multiple clicks while generating
+
+ try {
+ setIsGenerating(true);
+ let prompt =
+ agent.profile_image_options?.[0]?.generations_by_pk
+ ?.prompt ||
+ draftFields.imageDescription ||
+ "";
+
+ setLoadingProgress(10);
+
+ const payload = {
+ prompt: prompt,
+ modelId: LEONARDO_MODEL_ID,
+ styleUUID: LEONARDO_STYLE_UUID,
+ num_images: 4
+ };
+ const imageResponse = await inconsistentImageLambda(
+ payload
+ );
+
+ if (
+ !imageResponse?.generations_by_pk?.generated_images?.[0]
+ ?.url
+ ) {
+ throw new Error("No image URL received");
+ }
+
+ setLoadingProgress(50);
+ const imageUrl =
+ imageResponse.generations_by_pk.generated_images[0].url;
+
+ setLoadingProgress(90);
+ setAgent((prev) => ({
+ ...prev,
+ profile_image: {
+ details: {
+ url: imageUrl,
+ image_id:
+ imageResponse.generations_by_pk.generated_images[0]
+ .id,
+ generationId: imageResponse.generations_by_pk.id,
+ },
+ },
+ profile_image_options: [
+ {
+ generations_by_pk: {
+ ...imageResponse.generations_by_pk,
+ prompt,
+ },
+ },
+ ],
+ }));
+
+ setLoadingProgress(100);
+ setTimeout(() => setLoadingProgress(0), 500);
+ } catch (error) {
+ console.error("Error generating new image:", error);
+ setLoadingProgress(0);
+ } finally {
+ setIsGenerating(false);
+ }
+ }}
+ disabled={isGenerating}
+ >
+ <RefreshCcw
+ className={`w-4 h-4 mr-2 ${isGenerating ? "animate-spin" : ""}`}
+ />
+ {isGenerating ? "Generating..." : "Regenerate All"}
+ </button>
+ </div>
+
+ {/* Image Selection Grid */}
+ <div className="grid grid-cols-4 gap-4">
+ {agent.profile_image_options?.[0]?.generations_by_pk?.generated_images?.map(
+ (image: GeneratedImage, index: number) => (
+ <div
+ key={index}
+ className={`relative aspect-square rounded-lg cursor-pointer
+ ${agent.selectedImage === index
+ ? "ring-2 ring-orange-500"
+ : ""
+ }`}
+ onClick={async () => {
+
+ try {
+ setLoadingProgress(30);
+
+ setLoadingProgress(70);
+
+ setAgent((prev) => ({
+ ...prev,
+ selectedImage: index,
+ profile_image: {
+ details: {
+ url: image?.url || "",
+ image_id: image?.id || "",
+ generationId: image?.generationId || "",
+ },
+ },
+ }));
+
+ setLoadingProgress(100);
+ setTimeout(() => setLoadingProgress(0), 500);
+ } catch (error) {
+ console.error("Error loading image:", error);
+ setLoadingProgress(0);
+ }
+ }}
+ style={{
+ backgroundImage: image?.url ? `url(${image.url})` : "none",
+ backgroundSize: "cover",
+ backgroundPosition: "center",
+ }}
+ >
+ {agent.selectedImage === index && (
+ <div className="absolute inset-0 flex items-center justify-center bg-gray-800 bg-opacity-50">
+ <svg
+ className="w-8 h-8 text-white"
+ fill="none"
+ stroke="currentColor"
+ viewBox="0 0 24 24"
+ xmlns="http://www.w3.org/2000/svg"
+ >
+ <path
+ strokeLinecap="round"
+ strokeLinejoin="round"
+ strokeWidth="2"
+ d="M5 13l4 4L19 7"
+ />
+ </svg>
+ </div>
+ )}
+ </div>
+ )
+ )}
+ </div>
+
+ {/* Character Info Card */}
+ <div className="p-4 rounded-lg bg-gray-50/80 border border-orange-500/30">
+ <div className="mb-4">
+ <div className="text-lg font-medium text-gray-700">
+ Image Generation Description
+ </div>
+ <textarea
+ value={draftFields.imageDescription || ""}
+ onChange={handleDraftChange("imageDescription")}
+ onKeyDown={handleDraftKeyDown("imageDescription")}
+ placeholder="Enter image generation description (Press Enter to commit)"
+ rows={3}
+ className="w-full px-3 py-2 mt-3 rounded-md bg-gray-50/80 border border-orange-500/30 text-gray-700 focus:outline-none focus:ring-2 focus:ring-orange-500/50"
+ />
+ </div>
+ </div>
+ <div className="p-4 rounded-lg bg-gray-50/80 border border-orange-500/30">
+ <div className="mb-4">
+ <div className="text-lg font-medium text-gray-700">
+ Agent Name
+ </div>
+ <div className="text-gray-700 mt-4">{agent.agent_details.name}</div>
+ </div>
+ </div>
+ </div>
+ </div>
+
+ </div>
+ </div>
+ </div>
+ );
+};
+
+export default AgentCreator;
diff --git a/client/src/pages/AgentGallery.tsx b/client/src/pages/AgentGallery.tsx
new file mode 100644
index 0000000..9fdd661
--- /dev/null
+++ b/client/src/pages/AgentGallery.tsx
@@ -0,0 +1,538 @@
+import React, { useState, useEffect, useRef } from "react";
+import { Agent, GenerationsByPk } from "../interfaces/AgentInterfaces";
+import useCharacters from "../hooks/useCharacters";
+import RandomAgentCard from "../components/RandomAgentCard"; // Import the new component
+import LoadedAgentCard from "../components/LoadedAgentCard"; // Import the new component
+import { inconsistentImageLambda } from "../api/leonardoApi";
+import { createBlankAgent } from "../utils/agentUtils";
+import { createAgent } from "../api/agentsAPI";
+import { generateRandomAgent } from "../utils/generateRandomAgent";
+import imageTraits from "../assets/generate-random-agents/imageTraits.json";
+import characterConcepts from "../assets/generate-random-agents/characterConcepts.json";
+import famousFigures from "../assets/generate-random-agents/famousFigures.json";
+import LunaQuantumchef from "../assets/example-agents/Luna_Quantumchef_master.json";
+import CosmicCurator from "../assets/example-agents/Cosmic_Curator_master.json";
+import GavelGlitch from "../assets/example-agents/Gavel_Glitch_master.json";
+import Sidebar from "../components/sidebar";
+import { Search, Filter as FilterIcon } from "lucide-react";
+import { motion } from "framer-motion";
+import { useMemo } from "react";
+import { useNavigate } from "react-router-dom";
+import { useAgent } from '../context/AgentContext';
+
+const LEONARDO_MODEL_ID = "e71a1c2f-4f80-4800-934f-2c68979d8cc8";
+const LEONARDO_STYLE_UUID = "b2a54a51-230b-4d4f-ad4e-8409bf58645f";
+
+// Helper function to get random trait
+const getRandomTrait = (traitArray: string[]): string => {
+ return traitArray[Math.floor(Math.random() * traitArray.length)];
+};
+
+// Add a utility function to handle image loading
+const loadImageWithFallback = async (url: string): Promise<string> => {
+ try {
+ const response = await fetch(url, {
+ mode: "cors",
+ credentials: "omit", // Don't send cookies
+ headers: {
+ Accept: "image/*",
+ },
+ });
+
+ if (!response.ok) {
+ throw new Error("Image failed to load");
+ }
+
+ return url;
+ } catch (error) {
+ console.error("[AgentGallery] Error loading image:", error);
+ return "Error loading image. Regenerate again";
+ }
+};
+
+// Add this helper function near the top with other utility functions
+const generateCharacterConcept = (): string => {
+ const profession = getRandomTrait(characterConcepts.professions);
+ const personality = getRandomTrait(characterConcepts.personalities);
+ const origin = getRandomTrait(characterConcepts.origins);
+ const power = getRandomTrait(characterConcepts.specialPowers);
+ const goal = getRandomTrait(characterConcepts.goals);
+
+ // Randomly choose between different concept formats
+ const conceptFormats = [
+ `A ${personality} ${profession} who ${power} and is ${origin}`,
+ `${profession} ${origin}, who ${power} while ${goal}`,
+ `A ${personality} individual ${origin} working as a ${profession}, with the ability to ${power}`,
+ `An extraordinary ${profession} ${origin} on a mission of ${goal}`,
+ `A remarkable ${personality} being who ${power}, working as a ${profession} while ${goal}`,
+ `Create a name and try to incorporate the ${profession} into the name. For example,
+ if the profession was a Dr. then the name could be Dr.{Name} `,
+ ];
+ const conceptFormat2 = [
+ `Create a parody meme of ${getRandomFamousPerson()}. The meme should be a funny and clever meme that captures the essence of the person and their achievements. Make it witty and memorable while staying respectful. Include their most iconic features, expressions, or famous quotes if applicable.
+ Make sure to use their name as part of their agent name. It is best to make a variation of their name. For example, if the person is Elon Musk, then the agent name could be Elon Musk Jr., Elon Gate, Trump Bot, Trump Tron, etc. Make something unique and memorable that could go viral within the first 24 hours of being posted.`,
+ ];
+
+ // 80% chance of conceptFormat1, 20% chance of conceptFormat2
+ return Math.random() < 0.35
+ ? getRandomTrait(conceptFormats)
+ : getRandomTrait(conceptFormat2);
+};
+
+// Add this helper function near other utility functions
+const getRandomFamousPerson = (): string => {
+ const categories = Object.keys(famousFigures);
+ const randomCategory =
+ categories[Math.floor(Math.random() * categories.length)];
+ const figures = famousFigures[randomCategory as keyof typeof famousFigures];
+ return figures[Math.floor(Math.random() * figures.length)];
+};
+
+// Add this function to convert master JSON to Agent type
+const convertMasterJsonToAgent = (masterJson: any): Agent => {
+ return {
+ id: Math.floor(Math.random() * 1000000).toString(),
+ name: masterJson.agent.agent_details.name || "",
+ avatar: masterJson.agent.profile_image?.details?.url || "",
+ shortDescription: masterJson.agent.agent_details.backstory || "",
+ tags: masterJson.agent.agent_details.hashtags || [],
+ personality: masterJson.agent.agent_details.personality || [],
+ communicationStyle:
+ masterJson.agent.agent_details.communication_style || [],
+ emojis: masterJson.agent.agent_details.emojis || [],
+ universe: masterJson.agent.agent_details.universe || "",
+ backstory: masterJson.agent.agent_details.backstory || "",
+ topic_expertise: masterJson.agent.agent_details.topic_expertise || [],
+ isExample: true, // Add this flag to identify example agents
+ };
+};
+
+const AgentGallery: React.FC = () => {
+ // Add selected agent state
+ // const [selectedAgent, setSelectedAgent] = useState<Agent | null>(null);
+ const { characters: loadedAgents } = useCharacters();
+ const [randomAgents, setRandomAgents] = useState<Agent[]>([]);
+ const [filter, setFilter] = useState("all");
+ const [searchQuery, setSearchQuery] = useState("");
+ const [showFilters, setShowFilters] = useState(false);
+ const initialMount = useRef(true);
+ const navigate = useNavigate()
+
+ // Inside AgentGallery component:
+ const { setSelectedAgent } = useAgent();
+
+ // Define generateRandomAgentData inside AgentGallery so it's accessible to child components
+ const generateRandomAgentData = async (): Promise<Agent> => {
+ try {
+ const concept = generateCharacterConcept();
+
+ const newRandomAgentData = await generateRandomAgent(concept);
+ const agentObject = newRandomAgentData.agent;
+ const agentDetails = agentObject.agent_details;
+
+ return {
+ id: Math.floor(Math.random() * 1000000).toString(),
+ name: agentDetails.name.replace("_", " ") || "",
+ avatar: "",
+ shortDescription: "...",
+ tags: agentDetails.hashtags || [],
+ personality: agentDetails.personality || [],
+ communicationStyle: agentDetails.communication_style || [],
+ emojis: agentDetails.emojis || [],
+ universe: agentDetails.universe || "",
+ backstory: agentDetails.backstory || "",
+ concept: concept,
+ topic_expertise: agentDetails.topic_expertise || [],
+ };
+ } catch (error) {
+ console.error("[generateRandomAgentData] Error:", error);
+ throw error;
+ }
+ };
+
+ const filterAgentsByName = (
+ agents: any[],
+ isLoadedAgent: boolean = false
+ ): any[] => {
+ if (!searchQuery) return agents;
+
+ const lowerQuery = searchQuery.toLowerCase().trim();
+
+ return agents.filter((agent) => {
+ if (isLoadedAgent) {
+ // Handle loaded agents structure where properties are in agent.agent.agent_details
+ const details = agent?.agent?.agent_details;
+ if (!details) return false;
+
+ return (
+ details.name?.toLowerCase().includes(lowerQuery) ||
+ details.backstory?.toLowerCase().includes(lowerQuery) ||
+ details.universe?.toLowerCase().includes(lowerQuery) ||
+ details.hashtags?.some((tag: string) =>
+ tag.toLowerCase().includes(lowerQuery)
+ ) ||
+ details.personality?.some((trait: string) =>
+ trait.toLowerCase().includes(lowerQuery)
+ ) ||
+ details.topic_expertise?.some((topic: string) =>
+ topic.toLowerCase().includes(lowerQuery)
+ ) ||
+ details.communication_style?.some((style: string) =>
+ style.toLowerCase().includes(lowerQuery)
+ )
+ );
+ } else {
+ // Handle random agents structure (keeping existing logic)
+ return (
+ agent.name?.toLowerCase().includes(lowerQuery) ||
+ agent.shortDescription?.toLowerCase().includes(lowerQuery) ||
+ agent.backstory?.toLowerCase().includes(lowerQuery) ||
+ agent.tags?.some((tag) => tag.toLowerCase().includes(lowerQuery)) ||
+ agent.personality?.some((trait) =>
+ trait.toLowerCase().includes(lowerQuery)
+ ) ||
+ agent.topic_expertise?.some((topic) =>
+ topic.toLowerCase().includes(lowerQuery)
+ )
+ );
+ }
+ });
+ };
+
+ const filteredRandomAgents = filterAgentsByName(randomAgents);
+ const filteredLoadedAgents = filterAgentsByName(loadedAgents, true);
+
+ useEffect(() => {
+ if (initialMount.current) {
+ initialMount.current = false;
+ const exampleAgents = [
+ convertMasterJsonToAgent(LunaQuantumchef),
+ convertMasterJsonToAgent(CosmicCurator),
+ convertMasterJsonToAgent(GavelGlitch),
+ ];
+ setRandomAgents(exampleAgents);
+ }
+ }, []);
+
+ const handleAddAgent = async (agent: Agent, leonardoResponse: any) => {
+ try {
+ // Create a new blank agent with proper initialization
+ const newAgent = createBlankAgent();
+
+ if (!newAgent.agent) {
+ console.error(
+ "[handleAddAgent] - New agent object is not properly initialized"
+ );
+ return;
+ }
+
+ // Ensure agent.agent_details exists before accessing
+ if (!newAgent.agent.agent_details) {
+ console.error(
+ "[handleAddAgent] - Agent details are not properly initialized"
+ );
+ return;
+ }
+
+ // Log the concept being saved
+
+ // populate our concept
+ newAgent.agent.concept = agent.concept || "";
+
+ // populate our agent details
+ const agentDetails = newAgent.agent.agent_details;
+ agentDetails.name = agent?.name || "";
+ agentDetails.personality = agent?.personality || [];
+ agentDetails.communication_style = agent?.communicationStyle || [];
+ agentDetails.emojis = agent?.emojis || [];
+ agentDetails.hashtags = agent?.hashtags || [];
+ agentDetails.universe = agent?.universe || "";
+ agentDetails.topic_expertise = agent?.topic_expertise || [];
+ agentDetails.backstory = agent?.backstory || "";
+
+ // Ensure profile_image_options array exists and has at least one element
+ if (!newAgent.agent.profile_image_options) {
+ newAgent.agent.profile_image_options = [];
+ }
+ if (newAgent.agent.profile_image_options.length === 0) {
+ newAgent.agent.profile_image_options.push({
+ generations_by_pk: {} as GenerationsByPk,
+ });
+ }
+
+ // Ensure profile_image exists and has details
+ if (!newAgent.agent.profile_image) {
+ newAgent.agent.profile_image = {
+ details: {
+ url: "",
+ image_id: "",
+ generationId: "",
+ },
+ };
+ }
+
+ // Populate the image data
+ if (leonardoResponse?.generations_by_pk) {
+ const generation_by_pk = leonardoResponse.generations_by_pk;
+ newAgent.agent.profile_image_options[0].generations_by_pk =
+ generation_by_pk;
+
+ if (generation_by_pk.generated_images?.[0]) {
+ const imageDetails = newAgent.agent.profile_image.details;
+ imageDetails.url = generation_by_pk.generated_images[0].url;
+ imageDetails.image_id = generation_by_pk.generated_images[0].id;
+ imageDetails.generationId = generation_by_pk.id;
+ }
+ }
+
+ // Call our api to save the new agent
+ const newAgentResponse = await createAgent(newAgent);
+
+ return newAgentResponse;
+ } catch (error) {
+ console.error("[handleAddAgent] Error:", error);
+ throw error;
+ }
+ };
+
+ // Update the handleSingleAgentRegeneration function
+ const handleSingleAgentRegeneration = async (
+ agentId: string
+ ): Promise<void> => {
+ try {
+ // Show loading state immediately with loading name
+ setRandomAgents((prevAgents) =>
+ prevAgents.map((agent) =>
+ agent.id === agentId
+ ? {
+ ...agent,
+ name: "Generating Agent...", // Add loading name
+ isLoading: true,
+ personality: [],
+ communicationStyle: [],
+ emojis: [],
+ hashtags: [],
+ }
+ : agent
+ )
+ );
+
+ const currentAgent = randomAgents.find((agent) => agent.id === agentId);
+ if (!currentAgent) return;
+
+ // Generate new agent data
+ const newAgentData = await generateRandomAgentData();
+
+ const newAgent = {
+ ...newAgentData,
+ id: currentAgent.id,
+ };
+
+ // Update UI to show we're now generating the image
+ setRandomAgents((prevAgents) =>
+ prevAgents.map((agent) =>
+ agent.id === agentId
+ ? {
+ ...newAgent,
+ isLoading: true,
+ }
+ : agent
+ )
+ );
+
+ // Incorporate the concept into the prompt
+ const prompt = `Generate an anime character portrait of ${newAgent.name
+ } with ${getRandomTrait(imageTraits.hairStyles)} ${getRandomTrait(
+ imageTraits.hairColors
+ )} hair, ${getRandomTrait(
+ imageTraits.eyeColors
+ )} eyes, wearing ${getRandomTrait(
+ imageTraits.clothingStyles
+ )} style clothing. Their personality can be described as ${newAgent.personality?.join(", ") || "unknown"
+ }. Scene: ${getRandomTrait(
+ imageTraits.backgrounds
+ )}. Style: high quality, detailed anime art, character portrait. Concept: ${newAgent.concept
+ } `;
+
+ const payload = {
+ prompt: prompt,
+ modelId: LEONARDO_MODEL_ID,
+ styleUUID: LEONARDO_STYLE_UUID,
+ num_images: 4,
+ };
+ const imageResponse = await inconsistentImageLambda(payload);
+
+ if (!imageResponse?.generations_by_pk?.generated_images?.[0]?.url) {
+ throw new Error("No image URL received");
+ }
+
+ const imageUrl = imageResponse.generations_by_pk.generated_images[0].url;
+ const loadedImageUrl = await loadImageWithFallback(imageUrl);
+
+ // Update with all necessary data for saving
+ setRandomAgents((prevAgents) =>
+ prevAgents.map((agent) =>
+ agent.id === agentId
+ ? {
+ ...newAgent,
+ avatar: loadedImageUrl,
+ isLoading: false,
+ leonardoResponse: imageResponse, // Add the full Leonardo response
+ leonardoImage:
+ imageResponse.generations_by_pk.generated_images[0], // Add the image data
+ }
+ : agent
+ )
+ );
+ } catch (error) {
+ console.error("[AgentGallery] Error regenerating single agent:", error);
+ setRandomAgents((prevAgents) =>
+ prevAgents.map((agent) =>
+ agent.id === agentId
+ ? {
+ ...agent,
+ isLoading: false,
+ }
+ : agent
+ )
+ );
+ }
+ };
+
+
+
+ // Update handleSelectAgent:
+ const handleSelectAgent = async (agent: Agent) => {
+ setSelectedAgent(agent);
+ navigate('/chat');
+ };
+ // // Add handleSelectAgent function
+ // const handleSelectAgent = async (agent: Agent) => {
+ // console.log("[handleSelectAgent] Selecting agent:", agent);
+ // try {
+ // // Add a small delay to show the loading state
+ // navigate("/chat")
+ // } catch (error) {
+ // console.error("[handleSelectAgent] Error:", error);
+ // throw error;
+ // }
+ // };
+
+ useEffect(() => {
+ if (initialMount.current) {
+ initialMount.current = false;
+ // Load example agents instead of generating new ones
+ const exampleAgents = [
+ convertMasterJsonToAgent(LunaQuantumchef),
+ convertMasterJsonToAgent(CosmicCurator),
+ convertMasterJsonToAgent(GavelGlitch),
+ ];
+ setRandomAgents(exampleAgents);
+ }
+ }, []);
+
+ return (
+ <div className="flex">
+ <Sidebar />
+ <div className="h-screen w-full overflow-y-scroll bg-gray-50 p-10">
+ <div className="p-5 mb-[4rem] rounded-xl shadow-xl flex items-center justify-between">
+ <div className="text-3xl font-semibold text-gray-600 font-orbitron">
+ Browse Agents
+ </div>
+ <div className="flex items-center space-x-3 justify-end"></div>
+ </div>
+ <div className="max-w-7xl mx-auto">
+ {/* Search and Filter Header */}
+ <div className="mb-8 space-y-4">
+ <div className="flex gap-4 items-center">
+ <div className="relative flex-1">
+ <Search
+ className="absolute left-3 top-1/2 -translate-y-1/2 text-gray-400"
+ size={20}
+ />
+ <input
+ type="text"
+ placeholder="Search agents by name, description, or tags..."
+ className="w-full pl-10 pr-4 py-3 placeholder:text-neutral-600 bg-neutral-200 shadow-inner text-neutral-600 rounded-lg outline-none focus:ring-2 focus:ring-yellow-400 focus:border-transparent"
+ value={searchQuery}
+ onChange={(e) => setSearchQuery(e.target.value)}
+ />
+ </div>
+ <button
+ onClick={() => setShowFilters(!showFilters)}
+ className="flex items-center gap-2 px-4 py-3 bg-gradient-to-r from-yellow-400 to-yellow-500 hover:from-yellow-500 hover:to-yellow-600 text-gray-900 rounded-lg font-medium transition-all"
+ >
+ <FilterIcon size={20} />
+ Filters
+ </button>
+ </div>
+
+ {/* Filter Pills */}
+ <div className="flex flex-wrap gap-3">
+ {["all", "random", "yourAgents"].map((filterOption) => (
+ <button
+ key={filterOption}
+ className={`px-4 py-2 rounded-full font-medium transition-all ${filter === filterOption
+ ? "bg-gradient-to-r from-yellow-400 to-yellow-500 text-gray-900"
+ : "bg-neutral-200 text-neutral-600 hover:bg-neutral-400"
+ }`}
+ onClick={() => setFilter(filterOption)}
+ >
+ {filterOption === "all"
+ ? "All"
+ : filterOption === "random"
+ ? "Random"
+ : "Your Agents"}
+ </button>
+ ))}
+ </div>
+ </div>
+
+ {/* Display message when no results are found */}
+ {searchQuery &&
+ !filteredRandomAgents.length &&
+ !filteredLoadedAgents.length && (
+ <div className="text-center py-8 text-gray-500">
+ No agents found matching "{searchQuery}"
+ </div>
+ )}
+
+ <div>
+ {(filter === "all" || filter === "yourAgents") &&
+ filteredLoadedAgents.length > 0 && (
+ <>
+ <h2 className="text-xl font-semibold mt-8 mb-4 text-gray-700">
+ Agents Gallery
+ </h2>
+ <div className="flex gap-6 pb-4 flex-wrap">
+ {/* Horizontal scrolling container */}
+ {filteredLoadedAgents.map((agent) => {
+ const randomWidth =
+ Math.floor(Math.random() * (500 - 200 + 1)) + 200;
+ return (
+ <div
+ key={
+ agent?.agent?.agent_details?.name ||
+ Math.random().toString()
+ }
+ style={{ width: `${randomWidth}px` }}
+ className="flex-grow"
+ >
+ <LoadedAgentCard
+ agent={agent}
+ onSelect={handleSelectAgent}
+ />
+ </div>
+ );
+ })}
+ </div>
+ </>
+ )}
+ </div>
+ </div>
+ </div>
+ </div>
+ );
+};
+
+export default AgentGallery;
diff --git a/client/src/pages/ChatToAgent.tsx b/client/src/pages/ChatToAgent.tsx
new file mode 100644
index 0000000..5b1a331
--- /dev/null
+++ b/client/src/pages/ChatToAgent.tsx
@@ -0,0 +1,281 @@
+import React, { useState, useRef, useEffect } from 'react';
+import { sendChatMessage, getChatHistory } from '../api/agentsAPI';
+import { Agent } from '../interfaces/AgentInterfaces';
+import useCharacters from '../hooks/useCharacters';
+import { User, Send } from 'lucide-react';
+import { Message, ChatHistory } from '../interfaces/ChatInterfaces';
+import Sidebar from '../components/sidebar';
+import { motion, AnimatePresence } from 'framer-motion';
+import { useAgent } from '../context/AgentContext';
+
+const ChatToAgent: React.FC = () => {
+ const { selectedAgent: contextAgent, setSelectedAgent: setContextAgent } = useAgent();
+ const [selectedAgent, setSelectedAgent] = useState<Agent | null>(null);
+ const { characters } = useCharacters();
+ const [input, setInput] = useState('');
+ const [chatHistory, setChatHistory] = useState<ChatHistory>({
+ agent_name: '',
+ chat_history: []
+ });
+ const [displayChatHistory, setDisplayChatHistory] = useState<Message[]>([]);
+ const [isLoading, setIsLoading] = useState(false);
+ const messagesEndRef = useRef<HTMLDivElement>(null);
+ const [selectedCharacterIndex, setSelectedCharacterIndex] = useState<number>(-1);
+
+ const scrollToBottom = () => {
+ messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' });
+ };
+
+ useEffect(() => {
+ scrollToBottom();
+ }, [displayChatHistory]);
+
+ useEffect(() => {
+ if (contextAgent) {
+ const index = characters.findIndex(
+ (char) => char.agent.agent_details.name === contextAgent.agent.agent_details.name
+ );
+ setSelectedCharacterIndex(index);
+ setSelectedAgent(contextAgent);
+ }
+ }, [contextAgent, characters]);
+
+ useEffect(() => {
+ const loadChatHistory = async () => {
+ if (selectedAgent?.agent?.agent_details?.name) {
+ try {
+ const agentName = selectedAgent.agent.agent_details.name;
+ const masterFilePath = selectedAgent.agent.master_file_path ||
+ `configs/${agentName}/${agentName}_master.json`;
+ const history = await getChatHistory(masterFilePath);
+ setChatHistory(history);
+ setDisplayChatHistory(history.chat_history || []);
+ } catch (error) {
+ console.error('Error loading chat history:', error);
+ setChatHistory({
+ agent_name: selectedAgent.agent.agent_details.name,
+ chat_history: []
+ });
+ setDisplayChatHistory([]);
+ }
+ }
+ };
+ loadChatHistory();
+ }, [selectedAgent]);
+
+ const handleSubmit = async (e: React.FormEvent) => {
+ e.preventDefault();
+ if (!input.trim() || !selectedAgent) return;
+
+ const userMessage: Message = {
+ role: 'user',
+ prompt: input.trim(),
+ message_id: chatHistory.chat_history.length
+ };
+
+ // Immediately update the display with the user's message
+ const updatedHistory = {
+ ...chatHistory,
+ chat_history: [...chatHistory.chat_history, userMessage]
+ };
+ setChatHistory(updatedHistory);
+ setDisplayChatHistory(updatedHistory.chat_history);
+
+ setInput('');
+ setIsLoading(true);
+
+ try {
+ const agentName = selectedAgent?.agent?.agent_details?.name || '';
+ const masterFilePath = selectedAgent?.agent?.master_file_path ||
+ `configs/${agentName.replace(/\s+/g, '_')}/${agentName.replace(/\s+/g, '_')}_master.json`;
+
+ const response = await sendChatMessage(
+ masterFilePath,
+ userMessage.prompt,
+ chatHistory // Send original chatHistory
+ );
+
+ // Update with the complete response including both user message and agent response
+ if (response.chat_history) {
+ setChatHistory(response.chat_history);
+ setDisplayChatHistory(response.chat_history.chat_history);
+ }
+ } catch (error) {
+ console.error('Error sending message:', error);
+ // Error state is already handled since we showed the user message
+ } finally {
+ setIsLoading(false);
+ }
+ };
+
+ const handleAgentSelect = (index: number) => {
+ const char = characters[index];
+ if (char) {
+ setSelectedCharacterIndex(index);
+ setSelectedAgent(char);
+ setContextAgent(char); // Update the context when selecting from dropdown
+ setDisplayChatHistory([]);
+ }
+ };
+
+ const getMessageContent = (message: Message) => {
+ if (message.role === 'user') {
+ return message.prompt || message.message;
+ }
+ return message.response;
+ };
+
+ return (
+ <div className="flex min-h-screen bg-gray-50">
+ <Sidebar />
+ <div className="flex-1 flex flex-col">
+ <div className="p-6 bg-white shadow-lg">
+ <div className="max-w-7xl mx-auto flex items-center justify-between">
+ <h1 className="text-3xl font-bold text-gray-600 font-orbitron">
+ Interact with Agents
+ </h1>
+ <div className="flex items-center space-x-4">
+ </div>
+ </div>
+ </div>
+
+ <div className="p-6">
+ <div className="max-w-7xl mx-auto">
+ <motion.div
+ className="mb-6 flex justify-center"
+ initial={{ opacity: 0, y: -20 }}
+ animate={{ opacity: 1, y: 0 }}
+ >
+ <select
+ className="px-4 py-2 rounded-lg bg-white border-2 border-yellow-400 text-gray-800 font-medium focus:outline-none focus:ring-2 focus:ring-yellow-400 transition-all duration-300"
+ onChange={(e) => handleAgentSelect(parseInt(e.target.value))}
+ value={selectedCharacterIndex}
+ >
+ <option value={-1}>Select an Agent</option>
+ {characters.map((char, index) => (
+ <option key={index} value={index}>
+ {char.agent.agent_details.name}
+ {char.agent.master_file_path?.includes('_1') ? ' (1)' : ''}
+ </option>
+ ))}
+ </select>
+ </motion.div>
+
+ <div className="bg-white rounded-2xl shadow-xl h-[calc(100vh-300px)] flex flex-col">
+ <div className="flex-1 overflow-y-auto p-6 space-y-6">
+ <AnimatePresence>
+ {displayChatHistory.map((message) => (
+ <motion.div
+ key={message.message_id}
+ initial={{ opacity: 0, y: 20 }}
+ animate={{ opacity: 1, y: 0 }}
+ exit={{ opacity: 0, y: -20 }}
+ className={`flex ${message.role === 'user' ? 'justify-end' : 'justify-start'}`}
+ >
+ <div className="flex items-start space-x-4 max-w-[70%]">
+ {message.role !== 'user' && (
+ <div
+ className="w-10 h-10 rounded-full bg-gradient-to-r from-yellow-400 to-yellow-600 flex-shrink-0"
+ style={{
+ backgroundImage: selectedAgent?.agent?.profile_image?.details?.url
+ ? `url(${selectedAgent?.agent?.profile_image?.details?.url})`
+ : undefined,
+ backgroundSize: 'cover',
+ backgroundPosition: 'center'
+ }}
+ />
+ )}
+
+ <div className={`flex flex-col ${message.role === 'user' ? 'items-end' : 'items-start'}`}>
+ <span className="text-sm text-gray-600 mb-1">
+ {message.role === 'user' ? 'You' : selectedAgent?.agent?.agent_details?.name}
+ </span>
+ <div
+ className={`p-4 rounded-2xl ${
+ message.role === 'user'
+ ? 'bg-yellow-400 text-gray-800'
+ : 'bg-gray-100 text-gray-800'
+ }`}
+ >
+ <p>{getMessageContent(message)}</p>
+ </div>
+ </div>
+
+ {message.role === 'user' && (
+ <div className="w-10 h-10 rounded-full bg-gray-200 flex items-center justify-center">
+ <User className="w-6 h-6 text-gray-600" />
+ </div>
+ )}
+ </div>
+ </motion.div>
+ ))}
+ </AnimatePresence>
+
+ {isLoading && (
+ <motion.div
+ initial={{ opacity: 0, y: 20 }}
+ animate={{ opacity: 1, y: 0 }}
+ className="flex justify-start"
+ >
+ <div className="flex items-center space-x-4">
+ <div className="w-10 h-10 rounded-full bg-gradient-to-r from-yellow-400 to-yellow-600" />
+ <div className="bg-gray-100 p-4 rounded-2xl">
+ <div className="flex space-x-2">
+ <motion.div
+ animate={{ y: [-3, 0, -3] }}
+ transition={{ repeat: Infinity, duration: 1 }}
+ className="w-2 h-2 bg-yellow-400 rounded-full"
+ />
+ <motion.div
+ animate={{ y: [-3, 0, -3] }}
+ transition={{ repeat: Infinity, duration: 1, delay: 0.2 }}
+ className="w-2 h-2 bg-yellow-400 rounded-full"
+ />
+ <motion.div
+ animate={{ y: [-3, 0, -3] }}
+ transition={{ repeat: Infinity, duration: 1, delay: 0.4 }}
+ className="w-2 h-2 bg-yellow-400 rounded-full"
+ />
+ </div>
+ </div>
+ </div>
+ </motion.div>
+ )}
+ <div ref={messagesEndRef} />
+ </div>
+
+ <form onSubmit={handleSubmit} className="p-4 border-t border-gray-200">
+ <div className="max-w-7xl mx-auto flex gap-4">
+ <input
+ type="text"
+ value={input}
+ onChange={(e) => setInput(e.target.value)}
+ placeholder={selectedAgent ? 'Type your message...' : 'Please select an agent first...'}
+ disabled={!selectedAgent || isLoading}
+ className="flex-1 px-4 py-2 rounded-lg bg-gray-100 border-2 border-transparent
+ focus:border-yellow-400 focus:outline-none transition-all duration-300
+ text-gray-800 placeholder-gray-500"
+ />
+ <motion.button
+ type="submit"
+ disabled={!selectedAgent || isLoading}
+ whileHover={{ scale: 1.05 }}
+ whileTap={{ scale: 0.95 }}
+ className="px-6 py-2 rounded-lg bg-yellow-400 text-gray-800 font-medium
+ disabled:opacity-50 disabled:cursor-not-allowed flex items-center gap-2
+ hover:bg-yellow-500 transition-colors duration-300"
+ >
+ <span>Send</span>
+ <Send className="w-4 h-4" />
+ </motion.button>
+ </div>
+ </form>
+ </div>
+ </div>
+ </div>
+ </div>
+ </div>
+ );
+};
+
+export default ChatToAgent;
\ No newline at end of file
diff --git a/client/src/pages/Home.tsx b/client/src/pages/Home.tsx
new file mode 100644
index 0000000..49ffc03
--- /dev/null
+++ b/client/src/pages/Home.tsx
@@ -0,0 +1,510 @@
+import { Blocks, BookOpen, Leaf, PictureInPicture2 } from "lucide-react";
+import { NavbarDemo } from "../components/Navbar";
+import { Button } from "../components/ui/button";
+import Card from "../components/ui/card-new";
+// @ts-ignore
+import PixelCard from "../components/ui/PixelCard.jsx";
+import HeroBackground from "../components/ui/hero-backround.js";
+import InfoCard from "../components/ui/info-card.js";
+import Tokenomics from "../components/tokenomics.js";
+import { motion } from "framer-motion";
+import { Link, useNavigate } from "react-router-dom";
+import cardIcon1 from "../../public/icons/Star Fall Minimalistic 3.svg";
+import cardIcon2 from "../../public/icons/Box.svg";
+import cardIcon3 from "../../public/icons/Share Circle.svg";
+import cardIcon4 from "../../public/icons/SSD Square.svg";
+import cardIcon5 from "../../public/icons/Posts Carousel Horizontal.svg";
+import { usePrivy, useSolanaWallets } from '@privy-io/react-auth';
+import { useEffect, useState } from "react";
+import Spline from '@splinetool/react-spline'
+import axios from 'axios'
+
+const Home = () => {
+
+ const { login, authenticated, ready, connectOrCreateWallet } = usePrivy()
+ const navigate = useNavigate()
+ const { wallets } = useSolanaWallets()
+
+ const handleLogin = async () => {
+
+ if (authenticated || wallets[0]) {
+ navigate("/create-agent")
+ } else {
+ connectOrCreateWallet()
+ }
+ }
+ const [hash, setHash] = useState("efgunyhed5frvgtbyu8799j");
+
+ useEffect(() => {
+ axios.get("https://catools.dev3vds1.link/get/equilink")
+ .then((res) => {
+ setHash(res.data[0].address)
+ }).catch((err) => {
+ console.log(err)
+ })
+ }, [])
+ const [isCopied, setIsCopied] = useState(false);
+
+ // Function to copy the hash to clipboard
+ const copyToClipboard = () => {
+ navigator.clipboard.writeText(hash);
+ setIsCopied(true); // Update button state
+ setTimeout(() => setIsCopied(false), 2000); // Reset state after 2 seconds
+ };
+
+ return (
+ <div className="">
+ <NavbarDemo />
+
+ <Spline
+ className=" absolute -z-10"
+ scene="https://prod.spline.design/WZteebe2UUTrQRl8/scene.splinecode"
+ />
+
+ <div className="z-50 mx-auto min-h-screen flex flex-col items-center justify-center container">
+ <div>
+
+ {/* CA Button with Yellow-Gold-White Theme */}
+ <motion.div
+ className="flex justify-center items-center mb-4"
+ initial={{ opacity: 0, y: -50 }}
+ animate={{ opacity: 1, y: 0 }}
+ transition={{ type: "spring", stiffness: 120, damping: 15 }}
+ whileHover={{ scale: 1.05 }}
+ whileTap={{ scale: 0.95 }}
+ >
+ <button
+ onClick={copyToClipboard}
+ className={`relative inline-flex items-center justify-center px-6 py-3 text-lg font-bold rounded-full shadow-lg overflow-hidden transition-all duration-300 ease-in-out ${isCopied ? "bg-yellow-500 text-white" : "bg-white text-black border border-yellow-500"
+ }`}
+ >
+ {/* Text */}
+ <span className="z-10 relative">
+ {isCopied ? "Copied!" : `CA: ${hash}`}
+ </span>
+
+ {/* Background Animation */}
+ {!isCopied && (
+ <motion.span
+ className="absolute inset-0 bg-gradient-to-r from-yellow-400 via-gold-500 to-yellow-200 blur-md opacity-50 group-hover:opacity-75 transition-opacity duration-500"
+ initial={{ scale: 0 }}
+ animate={{ scale: 1 }}
+ transition={{ repeat: Infinity, duration: 3, ease: "linear" }}
+ />
+ )}
+ </button>
+ </motion.div>
+
+ <motion.h2
+ className="text-7xl font-bold font-orbitron text-gray-800/70 text-center"
+ initial={{ opacity: 0, y: -50 }}
+ animate={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ >
+ The AI Agent Framework
+ </motion.h2>
+ <motion.h2
+ className="text-7xl my-3 font-bold text-gray-800/70 text-center"
+ initial={{ opacity: 0, y: 50 }}
+ animate={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1, delay: 0.3 }}
+ >
+ for the Next Era
+ </motion.h2>
+ </div>
+
+ <div className="flex justify-center items-center my-5 space-x-6">
+ <motion.div whileHover={{ scale: 1.1 }} whileTap={{ scale: 0.95 }}>
+ {/* <Link to="create-agent"> */}
+ <Button onClick={() => handleLogin()} className="bg-amber-500 text-lg text-center p-6">
+ Build your AI agent
+ </Button>
+ {/* </Link> */}
+ </motion.div>
+
+ <motion.div whileHover={{ scale: 1.1 }} whileTap={{ scale: 0.95 }}>
+ <Link to="browse-agents">
+ <Button
+ className="bg-gray-50/50 text-lg hover:bg-amber-400 p-6"
+ variant={"outline"}
+ onClick={() => handleLogin()}
+ >
+ Explore the ecosystem
+ </Button>
+ </Link>
+ </motion.div>
+ </div>
+ </div>
+
+ <div className="container mx-auto mt-[15rem]">
+ <div className="text-center">
+ <div>
+ <motion.h3
+ className="my-4 text-6xl font-semibold font-orbitron"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ Revolutionizing AI with
+ </motion.h3>
+
+ <motion.h3
+ className="my-4 text-6xl font-semibold font-orbitron"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ Scalable Automation
+ </motion.h3>
+
+ <motion.p
+ className="text-base my-5"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ Create, deploy and scale AI agents effortlessly
+ </motion.p>
+ </div>
+
+ <div className="grid grid-cols-3 place-items-center mt-[10rem]">
+ <motion.div
+ className="col-span-1"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ <Card
+ icon={<Blocks />}
+ heading="Seamless AI agent creation"
+ content="No-code / low-code framework for rapid deployment."
+ />
+ </motion.div>
+
+ <motion.div
+ className="col-span-1"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ <Card
+ icon={<PictureInPicture2 />}
+ heading="Multi Platform Integration"
+ content="Connect AI to Discord, Twitter, Telegram & more."
+ />
+ </motion.div>
+
+ <motion.div
+ className="col-span-1"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ <Card
+ icon={<Leaf />}
+ heading="Scalable and Efficient"
+ content="AI designed for high-performance execution."
+ />
+ </motion.div>
+ </div>
+
+ <h3 className="mt-[15rem] font-orbitron text-left text-6xl font-semibold">
+ How it Works
+ </h3>
+ <div className="grid grid-cols-2 gap-6">
+ <div className="mt-[5rem] px-[4rem]">
+ <InfoCard
+ number="01"
+ heading="Generate AI Agents"
+ description="Select a use case & train your AI."
+ numberBgColor="#007AFF11"
+ numberTextColor="#007AFFFF"
+ />
+ <InfoCard
+ number="02"
+ heading="Deploy & Integrate"
+ description="Connect across platform with simple APIs."
+ numberBgColor="#8177EA11"
+ numberTextColor="#8177EAFF"
+ />
+ <InfoCard
+ number="03"
+ heading="Optimize & Scale"
+ description="Utilize memory, chaining, prompt tuning"
+ numberBgColor="#EA433611"
+ numberTextColor="#EA4336FF"
+ />
+ </div>
+ <motion.div
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ className="place-self-center"
+ >
+ <img src="/img-side.png" alt="side-img" />
+ </motion.div>
+ </div>
+ </div>
+ </div>
+ <div id="agents" className="mt-[15rem] mb-[10rem]">
+ <motion.p
+ className="text-5xl font-bold text-center font-orbitron"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ The Power of AI,
+ </motion.p>
+ <motion.p
+ className="text-5xl font-bold text-center font-orbitron"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ Unlocked
+ </motion.p>
+
+ <div className="container mx-auto">
+ <div className="flex items-center flex-wrap justify-center gap-8 mt-16">
+ {/* First set of PixelCards */}
+ <motion.div
+ className="w-[250px] h-64 relative"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ <PixelCard variant="yellow" className="w-full h-full">
+ <div className="absolute flex items-center justify-center flex-col gap-4">
+ <img src={cardIcon1} />
+ <p className="text-2xl font-bold text-neutral-700">
+ Multiple AI Models
+ </p>
+ <p className="text-base text-center">
+ Connect to OpenAI, <br />
+ Anthropic, and more
+ </p>
+ </div>
+ </PixelCard>
+ </motion.div>
+
+ <motion.div
+ className="w-[250px] h-64 relative"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ <PixelCard variant="yellow" className="w-full h-full">
+ <div className="absolute flex items-center justify-center flex-col gap-4">
+ <img src={cardIcon2} />
+ <p className="text-2xl font-bold mb-4 text-neutral-700">
+ Modular Architecture
+ </p>
+ <p className="text-base text-center">
+ Plug & play connectors for <br /> X/Twitter, Discord, etc.
+ </p>
+ </div>
+ </PixelCard>
+ </motion.div>
+
+ <motion.div
+ className="w-[250px] h-64 relative"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ <PixelCard variant="yellow" className="w-full h-full">
+ <div className="absolute flex items-center justify-center flex-col gap-4">
+ <img src={cardIcon3} />
+ <p className="text-2xl font-bold mb-4 text-neutral-700">
+ Prompt Chaining
+ </p>
+ <p className="text-base text-center">
+ Non-repetitive, intelligent <br /> responses.
+ </p>
+ </div>
+ </PixelCard>
+ </motion.div>
+
+ {/* Second set of PixelCards */}
+
+ <motion.div
+ className="w-[250px] h-64 relative"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ <PixelCard variant="yellow" className="w-full h-full">
+ <div className="absolute flex items-center justify-center flex-col gap-4">
+ <img src={cardIcon4} />
+ <p className="text-2xl font-bold mb-4 text-neutral-700">
+ Memory Storage
+ </p>
+ <p className="text-base text-center">
+ Store Conversations and start <br />
+ where you left off
+ </p>
+ </div>
+ </PixelCard>
+ </motion.div>
+
+ <motion.div
+ className="w-[250px] h-64 relative"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ <PixelCard variant="yellow" className="w-full h-full">
+ <div className="absolute flex items-center justify-center flex-col gap-4">
+ <img src={cardIcon5} />
+ <p className="text-2xl font-bold mb-4 text-neutral-700">
+ Real-time Monitoring
+ </p>
+ <p className="text-base text-center">
+ Track & manage AI <br /> performance via CLI.
+ </p>
+ </div>
+ </PixelCard>
+ </motion.div>
+ </div>
+ </div>
+ </div>
+ <motion.div
+ className="mt-[15rem]"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ <Tokenomics />
+ </motion.div>
+
+ {/* RoadMap Section */}
+ <motion.div
+ id="roadmap"
+ className="relative w-[85%] mt-[5rem] container mx-auto"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ <h2 className="text-4xl font-semibold font-orbitron text-left text-gray-800 -mb-28">
+ Our RoadMap
+ </h2>
+ <div className="flex items-center min-h-screen justify-center">
+ <img src="/roadmap.svg" alt="" className="absolute" />
+ </div>
+ </motion.div>
+
+ {/* Footer Section */}
+ <motion.div
+ className="container mx-auto max-w-[90%]"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ <div
+ className="w-full h-[300px] md:h-[350px] flex flex-col items-center justify-center text-center p-6 rounded-3xl"
+ style={{
+ backgroundImage: "url('/img-footer.png')",
+ backgroundSize: "cover",
+ backgroundPosition: "center",
+ }}
+ >
+ <motion.h1
+ className="text-3xl md:text-4xl font-bold font-orbitron text-black mb-3"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ Ready to Build Your <br className="hidden md:block" />
+ AI Agent?
+ </motion.h1>
+ <motion.p
+ className="text-gray-700 text-lg md:text-xl mb-5"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ >
+ Join our AI community to share ideas, collaborate, and stay updated!
+ </motion.p>
+ <Link to="/create-agent">
+ <motion.button
+ className="bg-yellow-400 hover:bg-yellow-500 text-black font-semibold px-6 py-3 rounded-lg shadow-lg flex items-center gap-2 transition"
+ initial={{ opacity: 0, y: 50 }}
+ whileInView={{ opacity: 1, y: 0 }}
+ transition={{ duration: 1 }}
+ viewport={{ once: true }}
+ onClick={() => handleLogin()}
+ >
+ Launch App Now
+ </motion.button>
+ </Link>
+ </div>
+ </motion.div>
+ <footer className="w-full mt-32 py-4 px-12 flex flex-col items-stretch text-gray-700 text-sm">
+ <div className="flex justify-between items-end">
+ <nav className="flex space-x-8 mb-4">
+ <a href="#agents" className="hover:underline">
+ Features
+ </a>
+ <a href="#tokenomics" className="hover:underline">
+ Tokenomics
+ </a>
+ <a href="#roadmap" className="hover:underline">
+ Roadmap
+ </a>
+ </nav>
+
+ <div className="flex items-center space-x-6 mb-4">
+ <a href="https://equilink.gitbook.io" target="_blank">
+ <button className="p-2 rounded-lg duration-300 border hover:bg-yellow-300">
+ <img src="/gitbook.svg" className="w-6 h-auto" />
+ </button>
+ </a>
+ <a href="https://github.com/Equilink-Suite" target="_blank">
+ <button className="p-2 rounded-lg duration-300 border hover:bg-yellow-300">
+ <img src="/github.svg" className="w-6 h-auto" />
+ </button>
+ </a>
+ <a href="/" target="_blank">
+ <button className="p-2 rounded-lg duration-300 border hover:bg-yellow-300">
+ <img src="/X.svg" className="w-6 h-auto" />
+ </button>
+ </a>
+ </div>
+ </div>
+
+ <div className="flex pt-8 mt-4 border-t justify-between px-4">
+ <span>© 2025 EQUILINK</span>
+ <div className="flex space-x-4">
+ <a href="#" className="hover:underline">
+ Privacy Policy
+ </a>
+ <a href="#" className="hover:underline">
+ Terms of Use
+ </a>
+ </div>
+ </div>
+ </footer>
+ </div>
+ );
+};
+
+export default Home;
diff --git a/client/src/styles/flowControls.css b/client/src/styles/flowControls.css
new file mode 100644
index 0000000..a8bfc97
--- /dev/null
+++ b/client/src/styles/flowControls.css
@@ -0,0 +1,31 @@
+.react-flow__controls {
+ box-shadow: 0 0 10px rgba(0, 0, 0, 0.2);
+ border-radius: 8px;
+ font-family: inherit;
+ background: rgba(15, 23, 42, 0.9) !important;
+ border: 1px solid rgba(249, 115, 22, 0.3) !important;
+ padding: 4px;
+}
+
+.react-flow__controls-button {
+ background: rgba(15, 23, 42, 0.9) !important;
+ border-radius: 4px !important;
+ border: 1px solid rgba(249, 115, 22, 0.3) !important;
+ color: white !important;
+ padding: 4px !important;
+ margin: 2px !important;
+}
+
+.react-flow__controls-button:hover {
+ background: rgba(30, 41, 59, 0.9) !important;
+}
+
+.react-flow__controls-button svg {
+ fill: white !important;
+ width: 16px !important;
+ height: 16px !important;
+}
+
+.react-flow__controls-button path {
+ fill: white !important;
+}
\ No newline at end of file
diff --git a/client/src/utils/agentUtils.ts b/client/src/utils/agentUtils.ts
new file mode 100644
index 0000000..8fc7609
--- /dev/null
+++ b/client/src/utils/agentUtils.ts
@@ -0,0 +1,47 @@
+import { Agent } from '../interfaces/AgentInterfaces';
+import { Season } from '../interfaces/SeasonInterfaces';
+import { ProfileImageOption } from '../interfaces/AgentInterfaces';
+export function createBlankAgent(): Agent {
+ return {
+ agent: {
+ concept: '',
+ agent_details: {
+ backstory: '',
+ communication_style: [],
+ emojis: [],
+ hashtags: [],
+ name: '',
+ personality: [],
+ topic_expertise: [],
+ universe: ''
+ },
+ ai_model: {
+ memory_store: '',
+ model_name: '',
+ model_type: ''
+ },
+ connectors: {
+ discord: false,
+ telegram: false,
+ twitter: false
+ },
+ tracker: {
+ messages_sent: 0,
+ total_interactions: 0,
+ current_episode_number: 0,
+ current_post_number: 0,
+ current_season_number: 0,
+ post_every_x_minutes: 0
+ },
+ seasons: [] as Season[],
+ profile_image: {
+ details: {
+ url: '',
+ image_id: '',
+ generationId: ''
+ }
+ },
+ profile_image_options: [] as ProfileImageOption[]
+ }
+ };
+}
\ No newline at end of file
diff --git a/client/src/utils/generateRandomAgent.ts b/client/src/utils/generateRandomAgent.ts
new file mode 100644
index 0000000..0afc5c9
--- /dev/null
+++ b/client/src/utils/generateRandomAgent.ts
@@ -0,0 +1,12 @@
+import { createRandomAgent } from '../api/agentsAPI';
+
+export async function generateRandomAgent(concept?: string) {
+ try {
+ console.log('Requesting a new random agent...');
+ const randomAgent = await createRandomAgent(concept);
+ console.log('Random agent created:', randomAgent);
+ return randomAgent;
+ } catch (error) {
+ console.error('Error creating random agent:', error);
+ }
+}
diff --git a/client/src/utils/imageUtils.ts b/client/src/utils/imageUtils.ts
new file mode 100644
index 0000000..8372096
--- /dev/null
+++ b/client/src/utils/imageUtils.ts
@@ -0,0 +1,15 @@
+export const loadImageWithFallback = (url: string): Promise<string> => {
+ return new Promise((resolve, reject) => {
+ const img = new Image();
+
+ img.onload = () => {
+ resolve(url);
+ };
+
+ img.onerror = () => {
+ reject(new Error('Failed to load image'));
+ };
+
+ img.src = url;
+ });
+};
\ No newline at end of file
diff --git a/client/src/vite-env.d.ts b/client/src/vite-env.d.ts
new file mode 100644
index 0000000..11f02fe
--- /dev/null
+++ b/client/src/vite-env.d.ts
@@ -0,0 +1 @@
+/// <reference types="vite/client" />
diff --git a/client/tailwind.config.js b/client/tailwind.config.js
new file mode 100644
index 0000000..05c5810
--- /dev/null
+++ b/client/tailwind.config.js
@@ -0,0 +1,24 @@
+/** @type {import('tailwindcss').Config} */
+export default {
+ content: ["./index.html", "./src/**/*.{js,ts,jsx,tsx}"],
+ theme: {
+ extend: {
+ colors: {
+ slate: {
+ 950: "#020617",
+ },
+ },
+ backgroundImage: {
+ "gradient-radial": "radial-gradient(var(--tw-gradient-stops))",
+ },
+ fontFamily:{
+ roboto:["Roboto","sans-serif"],
+ orbitron:["Orbitron","sans-serif"],
+ },
+ container:{
+ padding:"2rem"
+ }
+ },
+ },
+ plugins: [],
+};
diff --git a/client/tsconfig.app.json b/client/tsconfig.app.json
new file mode 100644
index 0000000..c2d501f
--- /dev/null
+++ b/client/tsconfig.app.json
@@ -0,0 +1,26 @@
+{
+ "compilerOptions": {
+ "incremental": true,
+ "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo",
+ "target": "ES2020",
+ "useDefineForClassFields": true,
+ "lib": ["ES2020", "DOM", "DOM.Iterable"],
+ "module": "ESNext",
+ "skipLibCheck": true,
+
+ /* Bundler mode */
+ "moduleResolution": "bundler",
+ "allowImportingTsExtensions": true,
+ "isolatedModules": true,
+ "moduleDetection": "force",
+ "noEmit": true,
+ "jsx": "react-jsx",
+
+ /* Linting */
+ "strict": true,
+ "noUnusedLocals": true,
+ "noUnusedParameters": true,
+ "noFallthroughCasesInSwitch": true
+ },
+ "include": ["src"]
+}
diff --git a/client/tsconfig.json b/client/tsconfig.json
new file mode 100644
index 0000000..1072c8c
--- /dev/null
+++ b/client/tsconfig.json
@@ -0,0 +1,11 @@
+{
+ "files": [],
+ "references": [
+ { "path": "./tsconfig.app.json" },
+ { "path": "./tsconfig.node.json" }
+ ],
+ "compilerOptions": {
+ "jsx": "react-jsx",
+ "types": ["react", "react-dom"]
+ }
+}
diff --git a/client/tsconfig.node.json b/client/tsconfig.node.json
new file mode 100644
index 0000000..a464d80
--- /dev/null
+++ b/client/tsconfig.node.json
@@ -0,0 +1,24 @@
+{
+ "compilerOptions": {
+ "incremental": true,
+ "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo",
+ "target": "ES2022",
+ "lib": ["ES2023"],
+ "module": "ESNext",
+ "skipLibCheck": true,
+
+ /* Bundler mode */
+ "moduleResolution": "bundler",
+ "allowImportingTsExtensions": true,
+ "isolatedModules": true,
+ "moduleDetection": "force",
+ "noEmit": true,
+
+ /* Linting */
+ "strict": true,
+ "noUnusedLocals": true,
+ "noUnusedParameters": true,
+ "noFallthroughCasesInSwitch": true
+ },
+ "include": ["vite.config.ts"]
+}
diff --git a/client/vite.config.ts b/client/vite.config.ts
new file mode 100644
index 0000000..2328e17
--- /dev/null
+++ b/client/vite.config.ts
@@ -0,0 +1,7 @@
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react-swc'
+
+// https://vite.dev/config/
+export default defineConfig({
+ plugins: [react()],
+})
diff --git a/code_diagrams/png export versions/1_ai_model_setup.codediagram b/code_diagrams/png export versions/1_ai_model_setup.codediagram
new file mode 100644
index 0000000..bb2782e
--- /dev/null
+++ b/code_diagrams/png export versions/1_ai_model_setup.codediagram
@@ -0,0 +1 @@
+{"id":-1,"name":"Onboarding diagram","userId":-1,"createdAt":"","updatedAt":"","content":{"items":[{"uid":"M8WEbfcUL5","position":{"x":-660,"y":1030},"sizes":{"width":399.75,"height":105.0625},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"This is a diagram that shows you how to setup an ai model for prompting."}]}]},"nodeType":"block"},{"uid":"-6set05o5f","position":{"x":710,"y":480},"sizes":{"width":609.9375,"height":447.96875},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"Pro Tip."}]},{"type":"paragraph","content":[{"type":"text","text":"You can initialize a Gemini AI connection with your API Key, and then change the model type based on your needs. So you are not stuck to the one model. Be creative and swap to models best suited for different tasks."}]},{"type":"paragraph"},{"type":"codeBlock","attrs":{"language":"python","wrapCode":true},"content":[{"type":"text","text":"# Model is set latest\nself.model = genai.GenerativeModel('gemini-1.5-pro-latest')\nresponse = model.generate_content(\"Advanced mathematics problem.\")\n\n# Need a cheaper model or one a model that has different capabilites better suited for the task\nself.model = genai.GenerativeModel('gemini-pro-vision')\nresponse = model.generate_content(\"What is this a picture of.\")\n\n# Switch back to the original model\nself.model = genai.GenerativeModel('gemini-1.5-pro-latest')\nresponse = model.generate_content(\"Need a more creative and detailed response.\")"}]}]},"nodeType":"block"},{"uid":"96O7Za_QQa","position":{"x":740,"y":1010},"sizes":{"width":559.921875,"height":443.203125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"Google AI Studio ("},{"type":"text","marks":[{"type":"code"}],"text":"google-generativeai"},{"type":"text","text":")"}]},{"type":"paragraph","content":[{"type":"text","text":"1. "},{"type":"text","marks":[{"type":"bold"}],"text":"Import:"},{"type":"text","text":" Import the "},{"type":"text","marks":[{"type":"code"}],"text":"google.generativeai"},{"type":"text","text":" library. "}]},{"type":"paragraph","content":[{"type":"text","text":"2. "},{"type":"text","marks":[{"type":"bold"}],"text":"Configure API Key:"},{"type":"text","text":" Set your API key using "},{"type":"text","marks":[{"type":"code"}],"text":"genai.configure()"},{"type":"text","text":". "}]},{"type":"paragraph","content":[{"type":"text","text":"3. "},{"type":"text","marks":[{"type":"bold"}],"text":"List Models (Optional):"},{"type":"text","text":" The "},{"type":"text","marks":[{"type":"code"}],"text":"genai.list_models()"},{"type":"text","text":" function lets you see which models are available to your API key. The code filters the list to show only models that support the "},{"type":"text","marks":[{"type":"code"}],"text":"generateContent"},{"type":"text","text":" method. "}]},{"type":"paragraph","content":[{"type":"text","text":"4. "},{"type":"text","marks":[{"type":"bold"}],"text":"Select Model:"},{"type":"text","text":" Create a "},{"type":"text","marks":[{"type":"code"}],"text":"GenerativeModel"},{"type":"text","text":" instance, specifying the model name: "},{"type":"text","marks":[{"type":"italic"}],"text":" "}]},{"type":"paragraph","content":[{"type":"text","marks":[{"type":"italic"}],"text":"`'models/gemini-1.5-pro-latest'` "},{"type":"text","text":" "}]},{"type":"paragraph","content":[{"type":"text","text":"`'models/gemini-pro'` "},{"type":"text","marks":[{"type":"italic"}],"text":" "}]},{"type":"paragraph","content":[{"type":"text","marks":[{"type":"italic"}],"text":"`'models/gemini-1.5-flash-latest'` "},{"type":"text","text":" "}]},{"type":"paragraph","content":[{"type":"text","marks":[{"type":"code"}],"text":"'models/gemini-pro-vision'"},{"type":"text","text":" (for multi-modal input) "},{"type":"text","marks":[{"type":"italic"}],"text":" "}]},{"type":"paragraph","content":[{"type":"text","marks":[{"type":"code"}],"text":"'models/embedding-001'"},{"type":"text","marks":[{"type":"italic"}],"text":" (for text embedding) "}]},{"type":"paragraph","content":[{"type":"text","marks":[{"type":"italic"}],"text":"5. "},{"type":"text","text":"*Generate Content:** Use the "},{"type":"text","marks":[{"type":"code"}],"text":"generate_content()"},{"type":"text","text":" method to send a prompt and get a response."}]}]},"nodeType":"block"},{"uid":"GmjIPVQhKH","position":{"x":740,"y":1520},"sizes":{"width":569.953125,"height":367.828125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"Vertex AI ("},{"type":"text","marks":[{"type":"code"}],"text":"google-cloud-aiplatform"},{"type":"text","text":")"}]},{"type":"paragraph","content":[{"type":"text","text":"1. "},{"type":"text","marks":[{"type":"bold"}],"text":"Import:"},{"type":"text","text":" Import the "},{"type":"text","marks":[{"type":"code"}],"text":"aiplatform"},{"type":"text","text":" module from "},{"type":"text","marks":[{"type":"code"}],"text":"google.cloud"},{"type":"text","text":". "}]},{"type":"paragraph","content":[{"type":"text","text":"2. "},{"type":"text","marks":[{"type":"bold"}],"text":"Initialize:"},{"type":"text","text":" Call "},{"type":"text","marks":[{"type":"code"}],"text":"aiplatform.init()"},{"type":"text","text":", providing your Google Cloud project ID and the region where you want to use Vertex AI. "}]},{"type":"paragraph","content":[{"type":"text","text":"3. "},{"type":"text","marks":[{"type":"bold"}],"text":"Select Model:"},{"type":"text","text":" "},{"type":"text","marks":[{"type":"italic"}],"text":" Create a "},{"type":"text","marks":[{"type":"code"}],"text":"GenerativeModel"},{"type":"text","marks":[{"type":"italic"}],"text":" instance, specifying the model using its resource name: "},{"type":"text","text":" "}]},{"type":"paragraph","content":[{"type":"text","text":"`\"publishers/google/models/gemini-1.5-pro-latest\"` "},{"type":"text","marks":[{"type":"italic"}],"text":" `\"publishers/google/models/gemini-pro\"` "},{"type":"text","text":" "}]},{"type":"paragraph","content":[{"type":"text","text":"`\"publishers/google/models/gemini-1.5-flash-latest\"` "},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","marks":[{"type":"code"}],"text":"\"publishers/google/models/gemini-pro-vision\""},{"type":"text","marks":[{"type":"italic"}],"text":" (for multi-modal input) "}]},{"type":"paragraph","content":[{"type":"text","marks":[{"type":"italic"}],"text":"4. "},{"type":"text","text":"*Generate Content:** Use the "},{"type":"text","marks":[{"type":"code"}],"text":"generate_content()"},{"type":"text","text":" method to send a prompt and get a response."}]}]},"nodeType":"block"},{"uid":"wB6-SMyzPl","position":{"x":1510,"y":810},"sizes":{"width":819.9375,"height":707.8125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Main Gemini Models (Available through Google AI Studio or Vertex AI):"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Gemini 1.5 Pro:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"The most advanced and widely useful model, striking a good balance of capabilities and performance."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Excels at a wide range of tasks including complex reasoning, translation, code generation, and multi-modal tasks."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Has a much larger context window than the 1.0 Ultra."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Can handle up to 1 million tokens in its context window."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Can process information from various modalities, such as text, images, audio, and video."}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Gemini 1.0 Ultra:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"The largest and most capable Gemini model."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Designed for highly complex tasks, including reasoning, following instructions, coding, and collaboration."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Excels in areas like math, physics, and commonsense reasoning."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Generally recommended for specialized or very demanding tasks where you need the highest level of performance."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"(Still available, but often 1.5 pro is a better option)"}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Gemini 1.0 Pro:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"A more efficient model compared to Ultra."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Suitable for a wide variety of tasks."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Offers a good balance of performance and cost-effectiveness."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Often the recommended starting point for general use cases."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"(Still available, but often 1.5 pro is a better option)"}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Gemini 1.5 Flash:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"A very fast and efficient model, suitable for high-volume tasks where speed is critical."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Has a smaller context window than Pro models."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Can handle up to 1 million tokens in its context window."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Can process information from various modalities, such as text, images, audio, and video."}]}]}]}]}]},{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Other Gemini Models or Variants:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Gemma:"},{"type":"text","text":" Open models built from the same research and technology used to create the Gemini models."}]}]}]}]},"nodeType":"block"},{"uid":"roB4TT0pdT","position":{"x":-660,"y":1190},"sizes":{"width":399.984375,"height":175.046875},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"arai_ai_agents\\main.py"}]},{"type":"codeBlock","attrs":{"language":"python","wrapCode":true},"content":[{"type":"text","text":"# Instantiate your AI model\nai_model = GeminiModel()"}]}]},"nodeType":"block"},{"uid":"JtR0I3kx0C","position":{"x":-70,"y":1020},"sizes":{"width":619.96875,"height":510},"autoheight":false,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"arai_ai_agents\\models\\gemini_model.py"}]},{"type":"codeBlock","attrs":{"language":"python","wrapCode":true},"content":[{"type":"text","text":"class GeminiModel(ModelInterface):\n \"\"\"Gemini model implementation.\n \"\"\"\n\n def __init__(self, api_key=None, model_name=\"gemini-exp-1206\"):\n \"\"\"Initialize the Gemini model.\n\n Args:\n api_key (str): The API key to use for the Gemini model.\n model_name (str): The name of the Gemini model to use.\n\n Example:\n >>> gemini_model = GeminiModel()\n \"\"\"\n if api_key:\n genai.configure(api_key=api_key)\n else:\n genai.configure(api_key=os.environ.get('GOOGLE_GEMINI_API_KEY'))\n self.model = genai.GenerativeModel(model_name)"}]}]},"nodeType":"block"},{"uid":"ZxGxet3ART","position":{"x":0,"y":760},"sizes":{"width":489.984375,"height":175.046875},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":".env"}]},{"type":"codeBlock","attrs":{"language":null,"wrapCode":false},"content":[{"type":"text","text":"# Google Gemini API Key - Read and write API resources \nGOOGLE_GEMINI_API_KEY="}]}]},"nodeType":"block"},{"uid":"l1f4lb0y4R","position":{"x":40,"y":490},"sizes":{"width":399.984375,"height":189.984375},"autoheight":false,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":".gitignore"}]},{"type":"codeBlock","attrs":{"language":null,"wrapCode":false},"content":[{"type":"text","text":"# API Keys\n.env\ndont_share/"}]}]},"nodeType":"block"}],"configs":{"centerX":781.2556550025422,"centerY":-387.9922429083383,"zoomLevel":0.79999997138977},"arrowData":{"arrowsMap":{"arrow-point-bI0wBh3Ufk-bottom-point-PSPLIYKa9J-top":{"to":"point-PSPLIYKa9J-top","from":"point-bI0wBh3Ufk-bottom","label":"Normal Box","direction":"ft","selectable":true},"arrow-point-bI0wBh3Ufk-bottom-point-ytXK_ayIc1-top":{"to":"point-ytXK_ayIc1-top","from":"point-bI0wBh3Ufk-bottom","label":"Code 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+{"id":-1,"name":"FROM_FILE","userId":-1,"createdAt":"","updatedAt":"","content":{"items":[{"uid":"GYDy5rTCq1","position":{"x":520,"y":90},"sizes":{"width":399.890625,"height":83.953125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"Generate Tweets"}]}]},"nodeType":"block"},{"uid":"RXAZ3XJl2h","position":{"x":30,"y":90},"sizes":{"width":399.890625,"height":83.953125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"Tweet Secret Sauce"}]}]},"nodeType":"block"},{"uid":"Qr8DFz08Mu","position":{"x":980,"y":90},"sizes":{"width":399.890625,"height":83.953125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"Process Tweets"}]}]},"nodeType":"block"},{"uid":"o6cnZDZcI6","position":{"x":30,"y":240},"sizes":{"width":399.890625,"height":699.625},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"Non Repetitive Tweets using:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Prompt Chains"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"AI Creates Character Sheet"}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"AI creates story arcs"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Think of a TV with Seasons and epsidoes."}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"AI bulk generates tweets for arcs."}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"One at a time or all at once."}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"When an arc is finished. 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Maintain the same personality traits and comedic tone.”"}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Chain Prompting"},{"type":"text","text":":"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"This cyclical process—"},{"type":"text","marks":[{"type":"bold"}],"text":"generate"},{"type":"text","text":" → "},{"type":"text","marks":[{"type":"bold"}],"text":"store"},{"type":"text","text":" → "},{"type":"text","marks":[{"type":"bold"}],"text":"retrieve"},{"type":"text","text":" → "},{"type":"text","marks":[{"type":"bold"}],"text":"generate next"},{"type":"text","text":"—is effectively chain prompting."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Because the model sees the relevant context from your DB each time, you get a coherent, non-repetitive storyline."}]}]}]}]}]}]},"nodeType":"block"},{"uid":"PqniHZ2UCW","position":{"x":190,"y":-750},"sizes":{"width":399.890625,"height":549.953125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"heading","attrs":{"level":2},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Establish the Universe & Story Arcs (High-Level Prompt)"}]},{"type":"orderedList","attrs":{"start":1},"content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"High-Level Prompt"},{"type":"text","text":":"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Prompt your LLM with instructions like:"},{"type":"hardBreak"},{"type":"text","marks":[{"type":"italic"}],"text":"“Create the setting for an alien reconnaissance story starring Zorpnax. Describe the planet Xylos, its technology, cultural norms, major characters, and overall mission. Then outline 3–5 story arcs that could span multiple tweets.”"}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"This step yields a "},{"type":"text","marks":[{"type":"bold"}],"text":"universe bible"},{"type":"text","text":" and an "},{"type":"text","marks":[{"type":"bold"}],"text":"arc outline"},{"type":"text","text":" (e.g., Arc 1 = ‘Landing on Earth’, Arc 2 = ‘First Encounter with Humans’, Arc 3 = ‘Alien Technology Dilemmas’, etc.)."}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Store the Results"},{"type":"text","text":":"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Save the entire setting (lore, characters, arcs overview) in a file or a database."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"You might also create embeddings for sections of this lore and put them in a vector DB, tagging them as "},{"type":"text","marks":[{"type":"code"}],"text":"[lore]"},{"type":"text","text":", "},{"type":"text","marks":[{"type":"code"}],"text":"[characters]"},{"type":"text","text":", "},{"type":"text","marks":[{"type":"code"}],"text":"[arcs_overview]"},{"type":"text","text":", etc."}]}]}]}]}]}]},"nodeType":"block"},{"uid":"MaTuF2Y12L","position":{"x":520,"y":1350},"sizes":{"width":399.890625,"height":601.71875},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"heading","attrs":{"level":2},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Generate a Bulk of Tweets per Arc"}]},{"type":"orderedList","attrs":{"start":1},"content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Arc-Focused Prompt"},{"type":"text","text":":"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"For a given arc, say Arc 1, feed the relevant background from your "},{"type":"text","marks":[{"type":"bold"}],"text":"universe bible"},{"type":"text","text":" + "},{"type":"text","marks":[{"type":"italic"}],"text":"that arc’s specific outline"},{"type":"text","text":" back to the LLM."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Instruct:"},{"type":"hardBreak"},{"type":"text","marks":[{"type":"italic"}],"text":"“Generate 20 tweets that advance the storyline for Arc 1 (Landing on Earth). Each tweet <280 chars. Include references to Zorpnax’s personality, Xylos technology, comedic misunderstandings with humans, etc.”"}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Now you have a cohesive "},{"type":"text","marks":[{"type":"bold"}],"text":"batch of tweets"},{"type":"text","text":" dedicated to Arc 1."}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Tag and Store in Vector DB"},{"type":"text","text":":"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"For each tweet, store it in your vector DB along with embeddings, and tag it with:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Arc"},{"type":"text","text":": “Arc 1”"}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Characters"},{"type":"text","text":": “Zorpnax, Glaxmar, etc.”"}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Keywords"},{"type":"text","text":": “landing, technology, confusion, comedic”, etc."}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"This tagging makes it easy to "},{"type":"text","marks":[{"type":"bold"}],"text":"retrieve"},{"type":"text","text":" them later if you need reference or continuity checks."}]}]}]}]}]}]},"nodeType":"block"},{"uid":"DbuW3zS4O_","position":{"x":720,"y":-750},"sizes":{"width":399.890625,"height":540.515625},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"heading","attrs":{"level":2},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Ensuring Uniqueness & Non-Repetition"}]},{"type":"orderedList","attrs":{"start":1},"content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Embedding Similarity Checks"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"You can embed newly generated tweets and compare them to existing tweets in the DB. If the similarity is too high (e.g. >0.85), you know it’s repeating."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Prompt the model to re-generate or modify any tweets that are near-duplicates."}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Memory of Character Traits"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"By consistently retrieving the stored character profiles (Zorpnax’s personality, etc.), the LLM will "},{"type":"text","marks":[{"type":"bold"}],"text":"maintain"},{"type":"text","text":" those traits across arcs."}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Global Summaries"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"If your story grows large (1000+ tweets), you can keep “arc-level” or “season-level” summaries instead of feeding "},{"type":"text","marks":[{"type":"italic"}],"text":"all"},{"type":"text","text":" tweets each time. Summaries preserve essential info while staying under context window limits."}]}]}]}]}]}]},"nodeType":"block"},{"uid":"Oo6rzV77sP","position":{"x":-1000,"y":420},"sizes":{"width":399.890625,"height":549.25},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"heading","attrs":{"level":2},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Advantages of This Approach"}]},{"type":"orderedList","attrs":{"start":1},"content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Consistency Over Time"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Because each new generation references previous arcs/characters from the DB, your story remains internally consistent (names, motivations, references)."}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Non-Repetitive, Natural Continuation"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Using vector search + embedding similarity ensures you don’t inadvertently re-post near-identical tweets."}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Rich, Expanding Universe"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"You can expand arcs, introduce new characters, or revisit old arcs. The DB always has the prior lore to keep you from “losing track” of your own universe."}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Chain-of-Thought"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"You can feed partial contexts to the LLM, letting it reason step-by-step to maintain continuity without rehashing everything from scratch."}]}]}]}]}]}]},"nodeType":"block"},{"uid":"oYwKLSfRtO","position":{"x":-540,"y":420},"sizes":{"width":399.890625,"height":427.8125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"heading","attrs":{"level":2},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Practical Tips"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Use Summaries"},{"type":"text","text":": For large stories, feed the LLM concise summaries (or the top relevant chunks from the DB) rather than raw text."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Tag Carefully"},{"type":"text","text":": Good tagging ("},{"type":"text","marks":[{"type":"code"}],"text":"arc_id"},{"type":"text","text":", "},{"type":"text","marks":[{"type":"code"}],"text":"characters_involved"},{"type":"text","text":", "},{"type":"text","marks":[{"type":"code"}],"text":"location"},{"type":"text","text":", "},{"type":"text","marks":[{"type":"code"}],"text":"plot_points"},{"type":"text","text":") makes retrieving relevant context easier."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Check Similarity"},{"type":"text","text":": Before finalizing each new tweet batch, run a quick similarity check to avoid duplicates."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Plan Arc Overlaps"},{"type":"text","text":": If arcs overlap or re-introduce characters, mention it in the prompt so the LLM merges continuity seamlessly."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Limit Tweet Volume"},{"type":"text","text":": Large arcs can lead to hundreds of tweets. Summaries or chunking helps keep the model’s context within workable limits."}]}]}]}]},"nodeType":"block"},{"uid":"_HXtiY7fHw","position":{"x":-540,"y":150},"sizes":{"width":399.890625,"height":244.921875},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"heading","attrs":{"level":3},"content":[{"type":"text","text":"Final Thoughts"}]},{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Yes"},{"type":"text","text":", prompting first for the "},{"type":"text","marks":[{"type":"bold"}],"text":"universe + arcs"},{"type":"text","text":" and then bulk-generating tweets for each arc (while storing them in a vector DB) is an excellent way to get "},{"type":"text","marks":[{"type":"bold"}],"text":"cohesive"},{"type":"text","text":" and "},{"type":"text","marks":[{"type":"bold"}],"text":"non-repetitive"},{"type":"text","text":" storylines. Tagging tweets by arc and characters ensures that you can "},{"type":"text","marks":[{"type":"bold"}],"text":"chain"},{"type":"text","text":" your prompts in future arcs, letting the AI “remember” what’s already happened—and keep the narrative consistent and engaging."}]}]},"nodeType":"block"},{"uid":"wOvb4QTCxK","position":{"x":-1010,"y":150},"sizes":{"width":399.890625,"height":209.921875},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"Absolutely—this is a "},{"type":"text","marks":[{"type":"bold"}],"text":"great"},{"type":"text","text":" approach if you want "},{"type":"text","marks":[{"type":"bold"}],"text":"consistent"},{"type":"text","text":" story arcs, world-building, and character continuity over many tweets. The method you’re describing is essentially a "},{"type":"text","marks":[{"type":"bold"}],"text":"multi-stage (or chain) prompting"},{"type":"text","text":" workflow combined with a "},{"type":"text","marks":[{"type":"bold"}],"text":"knowledge base"},{"type":"text","text":" (the vector database) for long-term memory. Below is a suggested process and why it makes sense:"}]}]},"nodeType":"block"},{"uid":"FAiQhNrs1B","position":{"x":1810,"y":1140},"sizes":{"width":399.90625,"height":132.9375},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Prompt 7:"}]},{"type":"paragraph","content":[{"type":"text","text":"Summarize all Arc stories into Season."},{"type":"hardBreak"}]}]},"nodeType":"block"},{"uid":"VwX7qK2jp4","position":{"x":1260,"y":1110},"sizes":{"width":399.90625,"height":181.9375},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Prompt 8:"}]},{"type":"paragraph","content":[{"type":"text","text":"Generate a new batch of arcs and stories based on previous arc stories, and season summary."}]},{"type":"paragraph","content":[{"type":"text","text":"As well as the original background to the character and universe."}]}]},"nodeType":"block"},{"uid":"wl3VMoF1v1","position":{"x":-780,"y":-310},"sizes":{"width":399.90625,"height":293.796875},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"Chain these prompts:"}]},{"type":"orderedList","attrs":{"start":1},"content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Start with Prompt 1 to set the character."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Prompt 2 to flesh out the backstory and universe."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Prompt 3 to outline story arcs."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Prompt 4 to generate tweets for each arc."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Use Prompt 5 to create arc 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Or can make panels from perspective of multiple characters in the story. With the main character being the drone pilot."}]}]}]}]}]},{"type":"paragraph"}]},"nodeType":"block"},{"uid":"nhgY0tjdhS","position":{"x":520,"y":250},"sizes":{"width":399.90625,"height":224.5},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"Create Character idea or Supply oo"}]},{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Prompt 1:"}]},{"type":"paragraph","content":[{"type":"text","text":"Create the setting for an alien reconnaissance story starring Zorpnax. Describe the planet Xylos, its technology, cultural norms, major characters, and overall mission. 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The method you’re describing is essentially a "},{"type":"text","marks":[{"type":"bold"}],"text":"multi-stage (or chain) prompting"},{"type":"text","text":" workflow combined with a "},{"type":"text","marks":[{"type":"bold"}],"text":"knowledge base"},{"type":"text","text":" (the vector database) for long-term memory. 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+{"id":-1,"name":"Onboarding diagram","userId":-1,"createdAt":"","updatedAt":"","content":{"items":[{"uid":"BVH2G9gxwl","position":{"x":-490,"y":740},"sizes":{"width":399.765625,"height":188.8125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"filePathNode","attrs":{"pathToFile":"","version":1},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"main.py"}]},{"type":"codeBlock","attrs":{"language":"python","wrapCode":true},"content":[{"type":"text","text":"# Instantiate your AI model\r\nai_model = GeminiModel()\r\n"}]}]},"nodeType":"block"},{"uid":"YNbOjafkh4","position":{"x":120,"y":280},"sizes":{"width":509.765625,"height":171.3125},"autoheight":true,"blockContent":{"content":[{"type":"filePathNode","attrs":{"pathToFile":"","version":1},"content":[{"type":"text","marks":[{"type":"bold"}],"text":".env"}]},{"type":"codeBlock","attrs":{"language":"env","wrapCode":true},"content":[{"type":"text","text":"# Google Gemini API Key - Read and write API resources\r\nGOOGLE_GEMINI_API_KEY="}]}]},"nodeType":"block"},{"uid":"jRY1ZQZUGR","position":{"x":70,"y":530},"sizes":{"width":609.953125,"height":489.796875},"autoheight":false,"blockContent":{"type":"doc","content":[{"type":"filePathNode","attrs":{"pathToFile":"","version":1},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"models\\gemini_model.py"}]},{"type":"codeBlock","attrs":{"language":"python","wrapCode":true},"content":[{"type":"text","text":"import google.generativeai as genai # import the gemini model\nfrom dotenv import load_dotenv # import the dotenv library\n \nload_dotenv() # load the environment variables from the .env file\r\n\r\nclass GeminiModel:\r\n '''\r\n Gemini model implementation.\r\n '''\r\n\r\n def __init__(self, api_key=None, model_name=\"gemini-pro\"):\r\n '''\r\n Initialize the Gemini model.\r\n '''\n\r\n if api_key:\r\n genai.configure(api_key=api_key)\r\n else:\r\n genai.configure(api_key=os.environ.get('GOOGLE_GEMINI_API_KEY'))\n self.model = genai.GenerativeModel(model_name)"}]}]},"nodeType":"block"},{"uid":"-Z5MInqll-","position":{"x":-490,"y":530},"sizes":{"width":399.734375,"height":104.8125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"This is a diagram that shows you how to setup an ai model for prompting."}]}]},"nodeType":"block"},{"uid":"RbPx6SIt7X","position":{"x":1530,"y":280},"sizes":{"width":809.796875,"height":718.703125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Main Gemini Models (Available through Google AI Studio or Vertex AI):"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Gemini 1.5 Pro:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"The most advanced and widely useful model, striking a good balance of capabilities and performance."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Excels at a wide range of tasks including complex reasoning, translation, code generation, and multi-modal tasks."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Has a much larger context window than the 1.0 Ultra."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Can handle up to 1 million tokens in its context window."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Can process information from various modalities, such as text, images, audio, and video."}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Gemini 1.0 Ultra:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"The largest and most capable Gemini model."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Designed for highly complex tasks, including reasoning, following instructions, coding, and collaboration."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Excels in areas like math, physics, and commonsense reasoning."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Generally recommended for specialized or very demanding tasks where you need the highest level of performance."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"(Still available, but often 1.5 pro is a better option)"}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Gemini 1.0 Pro:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"A more efficient model compared to Ultra."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Suitable for a wide variety of tasks."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Offers a good balance of performance and cost-effectiveness."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Often the recommended starting point for general use cases."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"(Still available, but often 1.5 pro is a better option)"}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Gemini 1.5 Flash:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"A very fast and efficient model, suitable for high-volume tasks where speed is critical."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Has a smaller context window than Pro models."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Can handle up to 1 million tokens in its context window."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Can process information from various modalities, such as text, images, audio, and video."}]}]}]}]}]},{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Other Gemini Models or Variants:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","marks":[{"type":"bold"}],"text":"Gemma:"},{"type":"text","text":" Open models built from the same research and technology used to create the Gemini models."}]}]}]}]},"nodeType":"block"},{"uid":"JiXmN8atG2","position":{"x":840,"y":470},"sizes":{"width":550,"height":462.75},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"Google AI Studio ("},{"type":"text","marks":[{"type":"code"}],"text":"google-generativeai"},{"type":"text","text":")"}]},{"type":"paragraph","content":[{"type":"text","text":"1. "},{"type":"text","marks":[{"type":"bold"}],"text":"Import:"},{"type":"text","text":" Import the "},{"type":"text","marks":[{"type":"code"}],"text":"google.generativeai"},{"type":"text","text":" library. "}]},{"type":"paragraph","content":[{"type":"text","text":"2. "},{"type":"text","marks":[{"type":"bold"}],"text":"Configure API Key:"},{"type":"text","text":" Set your API key using "},{"type":"text","marks":[{"type":"code"}],"text":"genai.configure()"},{"type":"text","text":". "}]},{"type":"paragraph","content":[{"type":"text","text":"3. "},{"type":"text","marks":[{"type":"bold"}],"text":"List Models (Optional):"},{"type":"text","text":" The "},{"type":"text","marks":[{"type":"code"}],"text":"genai.list_models()"},{"type":"text","text":" function lets you see which models are available to your API key. The code filters the list to show only models that support the "},{"type":"text","marks":[{"type":"code"}],"text":"generateContent"},{"type":"text","text":" method. "}]},{"type":"paragraph","content":[{"type":"text","text":"4. "},{"type":"text","marks":[{"type":"bold"}],"text":"Select Model:"},{"type":"text","text":" Create a "},{"type":"text","marks":[{"type":"code"}],"text":"GenerativeModel"},{"type":"text","text":" instance, specifying the model name: "},{"type":"text","marks":[{"type":"italic"}],"text":" "}]},{"type":"paragraph","content":[{"type":"text","marks":[{"type":"italic"}],"text":"`'models/gemini-1.5-pro-latest'` "},{"type":"text","text":" "}]},{"type":"paragraph","content":[{"type":"text","text":"`'models/gemini-pro'` "},{"type":"text","marks":[{"type":"italic"}],"text":" "}]},{"type":"paragraph","content":[{"type":"text","marks":[{"type":"italic"}],"text":"`'models/gemini-1.5-flash-latest'` "},{"type":"text","text":" "}]},{"type":"paragraph","content":[{"type":"text","marks":[{"type":"code"}],"text":"'models/gemini-pro-vision'"},{"type":"text","text":" (for multi-modal input) "},{"type":"text","marks":[{"type":"italic"}],"text":" "}]},{"type":"paragraph","content":[{"type":"text","marks":[{"type":"code"}],"text":"'models/embedding-001'"},{"type":"text","marks":[{"type":"italic"}],"text":" (for text embedding) "}]},{"type":"paragraph","content":[{"type":"text","marks":[{"type":"italic"}],"text":"5. "},{"type":"text","text":"*Generate Content:** Use the "},{"type":"text","marks":[{"type":"code"}],"text":"generate_content()"},{"type":"text","text":" method to send a prompt and get a response."}]}]},"nodeType":"block"},{"uid":"q0nfZMIznq","position":{"x":840,"y":1020},"sizes":{"width":550,"height":366.765625},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"Vertex AI ("},{"type":"text","marks":[{"type":"code"}],"text":"google-cloud-aiplatform"},{"type":"text","text":")"}]},{"type":"paragraph","content":[{"type":"text","text":"1. "},{"type":"text","marks":[{"type":"bold"}],"text":"Import:"},{"type":"text","text":" Import the "},{"type":"text","marks":[{"type":"code"}],"text":"aiplatform"},{"type":"text","text":" module from "},{"type":"text","marks":[{"type":"code"}],"text":"google.cloud"},{"type":"text","text":". "}]},{"type":"paragraph","content":[{"type":"text","text":"2. "},{"type":"text","marks":[{"type":"bold"}],"text":"Initialize:"},{"type":"text","text":" Call "},{"type":"text","marks":[{"type":"code"}],"text":"aiplatform.init()"},{"type":"text","text":", providing your Google Cloud project ID and the region where you want to use Vertex AI. "}]},{"type":"paragraph","content":[{"type":"text","text":"3. "},{"type":"text","marks":[{"type":"bold"}],"text":"Select Model:"},{"type":"text","text":" "},{"type":"text","marks":[{"type":"italic"}],"text":" Create a "},{"type":"text","marks":[{"type":"code"}],"text":"GenerativeModel"},{"type":"text","marks":[{"type":"italic"}],"text":" instance, specifying the model using its resource name: "},{"type":"text","text":" "}]},{"type":"paragraph","content":[{"type":"text","text":"`\"publishers/google/models/gemini-1.5-pro-latest\"` "},{"type":"text","marks":[{"type":"italic"}],"text":" `\"publishers/google/models/gemini-pro\"` "},{"type":"text","text":" "}]},{"type":"paragraph","content":[{"type":"text","text":"`\"publishers/google/models/gemini-1.5-flash-latest\"` "},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","marks":[{"type":"code"}],"text":"\"publishers/google/models/gemini-pro-vision\""},{"type":"text","marks":[{"type":"italic"}],"text":" (for multi-modal input) "}]},{"type":"paragraph","content":[{"type":"text","marks":[{"type":"italic"}],"text":"4. "},{"type":"text","text":"*Generate Content:** Use the "},{"type":"text","marks":[{"type":"code"}],"text":"generate_content()"},{"type":"text","text":" method to send a prompt and get a response."}]}]},"nodeType":"block"},{"uid":"GB6t3ipoJQ","position":{"x":170,"y":20},"sizes":{"width":399.84375,"height":188.8125},"autoheight":true,"blockContent":{"content":[{"type":"filePathNode","attrs":{"pathToFile":"","version":1},"content":[{"type":"text","marks":[{"type":"bold"}],"text":".gitignore"}]},{"type":"codeBlock","attrs":{"language":"gitignore","wrapCode":true},"content":[{"type":"text","text":"# API Keys\r\n.env\r\ndont_share/"}]}]},"nodeType":"block"},{"uid":"m_9bpoosTx","position":{"x":800,"y":-30},"sizes":{"width":629.953125,"height":437.296875},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"Pro Tip."}]},{"type":"paragraph","content":[{"type":"text","text":"You can initialize a Gemini AI connection with your API Key, and then change the model type based on your needs. So you are not stuck to the one model. Be creative and swap to models best suited for different tasks."}]},{"type":"codeBlock","attrs":{"language":"python","wrapCode":true},"content":[{"type":"text","text":"# Model is set latest\nself.model = genai.GenerativeModel('gemini-1.5-pro-latest')\nresponse = model.generate_content(\"Advanced mathematics problem.\")\n\n# Need a cheaper model or one a model that has different capabilites better suited for the task\nself.model = genai.GenerativeModel('gemini-pro-vision')\nresponse = model.generate_content(\"What is this a picture of.\")\n\n# Switch back to the original model\nself.model = genai.GenerativeModel('gemini-1.5-pro-latest')\nresponse = model.generate_content(\"Need a more creative and detailed response.\")\n"}]}]},"nodeType":"block"}],"configs":{"centerX":415.77115280647536,"centerY":19.553587890839225,"zoomLevel":0.19259258891329367},"arrowData":{"arrowsMap":{"arrow-point-bI0wBh3Ufk-bottom-point-PSPLIYKa9J-top":{"to":"point-PSPLIYKa9J-top","from":"point-bI0wBh3Ufk-bottom","label":"Normal Box","direction":"ft","selectable":true},"arrow-point-bI0wBh3Ufk-bottom-point-ytXK_ayIc1-top":{"to":"point-ytXK_ayIc1-top","from":"point-bI0wBh3Ufk-bottom","label":"Code Box","direction":"ft","selectable":true},"arrow-point-hyyRZE3E8u-right-point-6ZopTaEaDZ-left":{"to":"point-6ZopTaEaDZ-left","from":"point-hyyRZE3E8u-right","label":"call","direction":"ft","selectable":true}},"pointsMap":{"point-PSPLIYKa9J-top":{"x":805.9999797489683,"y":60,"id":"point-PSPLIYKa9J-top","direction":"top"},"point-ytXK_ayIc1-top":{"x":205.99999493724206,"y":60,"id":"point-ytXK_ayIc1-top","direction":"top"},"point-6ZopTaEaDZ-left":{"x":220,"y":605.9999898744841,"id":"point-6ZopTaEaDZ-left","direction":"left"},"point-hyyRZE3E8u-right":{"x":100,"y":606,"id":"point-hyyRZE3E8u-right","direction":"right"},"point-bI0wBh3Ufk-bottom":{"x":515.9999797489683,"y":-40,"id":"point-bI0wBh3Ufk-bottom","direction":"bottom"}},"edgesMap":{"edge-BVH2G9gxwl-BVH2G9gxwl-right-jRY1ZQZUGR-jRY1ZQZUGR-left":{"uid":"edge-BVH2G9gxwl-BVH2G9gxwl-right-jRY1ZQZUGR-jRY1ZQZUGR-left","fromNodeId":"BVH2G9gxwl","fromHandleId":"BVH2G9gxwl-right","toNodeId":"jRY1ZQZUGR","toHandleId":"jRY1ZQZUGR-left","direction":"ft","selectable":true,"type":"solid","content":{"label":""}},"edge-YNbOjafkh4-YNbOjafkh4-bottom-jRY1ZQZUGR-jRY1ZQZUGR-top":{"uid":"edge-YNbOjafkh4-YNbOjafkh4-bottom-jRY1ZQZUGR-jRY1ZQZUGR-top","fromNodeId":"YNbOjafkh4","fromHandleId":"YNbOjafkh4-bottom","toNodeId":"jRY1ZQZUGR","toHandleId":"jRY1ZQZUGR-top","direction":"ft","selectable":true,"type":"solid","content":{"label":""}},"edge-JiXmN8atG2-JiXmN8atG2-left-jRY1ZQZUGR-jRY1ZQZUGR-right":{"uid":"edge-JiXmN8atG2-JiXmN8atG2-left-jRY1ZQZUGR-jRY1ZQZUGR-right","fromNodeId":"JiXmN8atG2","fromHandleId":"JiXmN8atG2-left","toNodeId":"jRY1ZQZUGR","toHandleId":"jRY1ZQZUGR-right","direction":"ft","selectable":true,"type":"solid","content":{"label":""}},"edge-RbPx6SIt7X-RbPx6SIt7X-left-JiXmN8atG2-JiXmN8atG2-right":{"uid":"edge-RbPx6SIt7X-RbPx6SIt7X-left-JiXmN8atG2-JiXmN8atG2-right","fromNodeId":"RbPx6SIt7X","fromHandleId":"RbPx6SIt7X-left","toNodeId":"JiXmN8atG2","toHandleId":"JiXmN8atG2-right","direction":"ft","selectable":true,"type":"solid","content":{"label":""}},"edge-q0nfZMIznq-q0nfZMIznq-left-jRY1ZQZUGR-jRY1ZQZUGR-right":{"uid":"edge-q0nfZMIznq-q0nfZMIznq-left-jRY1ZQZUGR-jRY1ZQZUGR-right","fromNodeId":"q0nfZMIznq","fromHandleId":"q0nfZMIznq-left","toNodeId":"jRY1ZQZUGR","toHandleId":"jRY1ZQZUGR-right","direction":"ft","selectable":true,"type":"solid","content":{"label":""}},"edge--Z5MInqll---Z5MInqll--bottom-BVH2G9gxwl-BVH2G9gxwl-top":{"uid":"edge--Z5MInqll---Z5MInqll--bottom-BVH2G9gxwl-BVH2G9gxwl-top","fromNodeId":"-Z5MInqll-","fromHandleId":"-Z5MInqll--bottom","toNodeId":"BVH2G9gxwl","toHandleId":"BVH2G9gxwl-top","direction":"ft","selectable":true,"type":"solid","content":{"label":""}},"edge-GB6t3ipoJQ-GB6t3ipoJQ-bottom-YNbOjafkh4-YNbOjafkh4-top":{"uid":"edge-GB6t3ipoJQ-GB6t3ipoJQ-bottom-YNbOjafkh4-YNbOjafkh4-top","fromNodeId":"GB6t3ipoJQ","fromHandleId":"GB6t3ipoJQ-bottom","toNodeId":"YNbOjafkh4","toHandleId":"YNbOjafkh4-top","direction":"ft","selectable":true,"type":"solid","content":{"label":""}},"edge-m_9bpoosTx-m_9bpoosTx-bottom-JiXmN8atG2-JiXmN8atG2-top":{"uid":"edge-m_9bpoosTx-m_9bpoosTx-bottom-JiXmN8atG2-JiXmN8atG2-top","fromNodeId":"m_9bpoosTx","fromHandleId":"m_9bpoosTx-bottom","toNodeId":"JiXmN8atG2","toHandleId":"JiXmN8atG2-top","direction":"ft","selectable":true,"type":"solid","content":{"label":""}}}}}}
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+{"id":-1,"name":"Onboarding diagram","userId":-1,"createdAt":"","updatedAt":"","content":{"items":[{"uid":"tXz3sfRHmC","position":{"x":-620,"y":180},"sizes":{"width":469.6875,"height":482.25},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"heading","attrs":{"level":1},"content":[{"type":"text","text":"ARAI Agent Basic"}]},{"type":"paragraph","content":[{"type":"text","text":"Simple way of sending a prompt and receiving a response from ai."}]},{"type":"paragraph"},{"type":"paragraph","content":[{"type":"text","text":"We use messages as a dictionary as it creates the habit of allowing for more complex prompts in the future."}]},{"type":"paragraph"},{"type":"paragraph","content":[{"type":"text","text":"This will come in handy for:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Personality, Style or Persona."}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Tells the AI what type of expert it is so it can draft a more accurate response."}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"History [ ] "}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"for prompts and responses for ai to have context. "}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Memories []"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"for vector db on most recent memories, and most relevant"}]}]}]}]}]},{"type":"paragraph"}]},"nodeType":"block"},{"uid":"zT1dxIBB8b","position":{"x":570,"y":570},"sizes":{"width":679.921875,"height":801.3125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"filePathNode","attrs":{"pathToFile":"","version":1},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"models\\gemini_model.py"}]},{"type":"codeBlock","attrs":{"language":"python","wrapCode":true},"content":[{"type":"text","text":" def generate_response_from_string(self, prompt, **kwargs):\n # Extract personality and style from kwargs, or use defaults from agent_template\n if kwargs:\n if \"personality\" in kwargs:\n personality = kwargs.get(\"personality\")\n if \"communication_style\" in kwargs:\n communication_style = kwargs.get(\"communication_style\")\n else:\n personality = \"\"\n communication_style = \"\" \n\n try:\n # instructions being sent to the ai model\n messages = []\n\n # add personality and style to the instructions\n if personality or communication_style:\n persona_prompt = f\"{personality} {communication_style}\"\n messages.append({\n \"role\": \"user\",\n \"parts\": [persona_prompt]\n })\n\n # user message\n messages.append({\n \"role\": \"user\",\n \"parts\": [prompt]\n })\n\n # Make sure that what is being sent to the model is correct\n # print(messages)\n\n # generate the response\n response = self.model.generate_content(messages)\n return response.text.strip()\n\n except Exception as e:\n return f\"Error generating response: {str(e)}\""}]}]},"nodeType":"block"},{"uid":"vg7Z0EZd1u","position":{"x":-640,"y":720},"sizes":{"width":509.6875,"height":293.8125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"filePathNode","attrs":{"pathToFile":"","version":1},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"models\\gemini_model.py"}]},{"type":"codeBlock","attrs":{"language":"python","wrapCode":true},"content":[{"type":"text","text":"messages = []\n\nmessages.append({\n \"role\": \"user\",\r\n \"parts\": \"what is the capital of france\"\n})\n\nresponse = self.model.generate_content(messages)\nprint(response)"}]}]},"nodeType":"block"},{"uid":"CYddWFZB2g","position":{"x":-30,"y":800},"sizes":{"width":479.6875,"height":213.296875},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"heading","attrs":{"level":1},"content":[{"type":"text","text":"Intermediate."}]},{"type":"paragraph","content":[{"type":"text","text":"This example shows sending the persona the ai takes on in order for a better response to our prompt."}]},{"type":"paragraph","content":[{"type":"text","text":"Right now we are checking for personality and communication_style, but we could also iterate over the kwarg and add each parameter detected to the prompt."}]}]},"nodeType":"block"},{"uid":"c91Ur0p-AI","position":{"x":-670,"y":1380},"sizes":{"width":589.921875,"height":626.296875},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"Write your note here..."}]},{"type":"filePathNode","attrs":{"pathToFile":"","version":1}},{"type":"codeBlock","attrs":{"language":"python","wrapCode":true},"content":[{"type":"text","text":" def fix_response(self, prompt):\n try:\n # instructions being sent to the ai model\n messages = []\n\n # add personality and style to the instructions \n messages.append({\n \"role\": \"user\",\n \"parts\": [prompt]\n })\n\n # user message\n messages.append({\n \"role\": \"user\",\n \"parts\": [response]\n })\n\n # Make sure that what is being sent to the model is correct\n # print(messages)\n\n # generate the response\n response = self.model.generate_content(messages)\n return response.text.strip()\n\n except Exception as e:\n return f\"Error generating response: {str(e)}\""}]}]},"nodeType":"block"},{"uid":"0nFxh-9nUb","position":{"x":-1260,"y":410},"sizes":{"width":499.78125,"height":314.8125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"Write your note here..."}]},{"type":"codeBlock","attrs":{"language":"python","wrapCode":true},"content":[{"type":"text","text":"import google.generativeai as genai\n\ngenai.configure(api_key=\"YOUR_API_KEY\")\nmodel = genai.GenerativeModel(\"gemini-pro\")\n\nprompt = \"What is the capital of France?\"\n\nresponse = model.generate_content(prompt)\n\nprint(response.text)"}]}]},"nodeType":"block"},{"uid":"RFyphDOklg","position":{"x":-1230,"y":220},"sizes":{"width":469.78125,"height":122.3125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"heading","attrs":{"level":1},"content":[{"type":"text","text":"Basic"}]},{"type":"paragraph","content":[{"type":"text","text":"Simple way of sending a prompt and receiving a response from ai."}]}]},"nodeType":"block"},{"uid":"8x6mKpEQV1","position":{"x":-580,"y":1090},"sizes":{"width":399.78125,"height":230.78125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"I like to put my responses in a try and exception block."}]},{"type":"paragraph","content":[{"type":"text","text":"This allows for situations if your internet is down, or an issue with the ai model for whatever reason there maybe."}]},{"type":"paragraph","content":[{"type":"text","text":"It allows your program to keep running."}]},{"type":"paragraph","content":[{"type":"text","text":"You can even do a sleep for x seconds and try again, or have other functions that print off network status, or wait for user input before continuining. "}]}]},"nodeType":"block"}],"configs":{"centerX":86.56464462548138,"centerY":-679.1082958542006,"zoomLevel":0.655555533479762},"arrowData":{"arrowsMap":{"arrow-point-bI0wBh3Ufk-bottom-point-PSPLIYKa9J-top":{"to":"point-PSPLIYKa9J-top","from":"point-bI0wBh3Ufk-bottom","label":"Normal Box","direction":"ft","selectable":true},"arrow-point-bI0wBh3Ufk-bottom-point-ytXK_ayIc1-top":{"to":"point-ytXK_ayIc1-top","from":"point-bI0wBh3Ufk-bottom","label":"Code Box","direction":"ft","selectable":true},"arrow-point-hyyRZE3E8u-right-point-6ZopTaEaDZ-left":{"to":"point-6ZopTaEaDZ-left","from":"point-hyyRZE3E8u-right","label":"call","direction":"ft","selectable":true}},"pointsMap":{"point-PSPLIYKa9J-top":{"x":805.9999797489683,"y":60,"id":"point-PSPLIYKa9J-top","direction":"top"},"point-ytXK_ayIc1-top":{"x":205.99999493724206,"y":60,"id":"point-ytXK_ayIc1-top","direction":"top"},"point-6ZopTaEaDZ-left":{"x":220,"y":605.9999898744841,"id":"point-6ZopTaEaDZ-left","direction":"left"},"point-hyyRZE3E8u-right":{"x":100,"y":606,"id":"point-hyyRZE3E8u-right","direction":"right"},"point-bI0wBh3Ufk-bottom":{"x":515.9999797489683,"y":-40,"id":"point-bI0wBh3Ufk-bottom","direction":"bottom"}},"edgesMap":{"edge-RFyphDOklg-RFyphDOklg-bottom-0nFxh-9nUb-0nFxh-9nUb-top":{"uid":"edge-RFyphDOklg-RFyphDOklg-bottom-0nFxh-9nUb-0nFxh-9nUb-top","fromNodeId":"RFyphDOklg","fromHandleId":"RFyphDOklg-bottom","toNodeId":"0nFxh-9nUb","toHandleId":"0nFxh-9nUb-top","direction":"ft","selectable":true,"type":"solid","content":{"label":""}},"edge-tXz3sfRHmC-tXz3sfRHmC-bottom-vg7Z0EZd1u-vg7Z0EZd1u-top":{"uid":"edge-tXz3sfRHmC-tXz3sfRHmC-bottom-vg7Z0EZd1u-vg7Z0EZd1u-top","fromNodeId":"tXz3sfRHmC","fromHandleId":"tXz3sfRHmC-bottom","toNodeId":"vg7Z0EZd1u","toHandleId":"vg7Z0EZd1u-top","direction":"ft","selectable":true,"type":"solid","content":{"label":""}},"edge-vg7Z0EZd1u-vg7Z0EZd1u-bottom-8x6mKpEQV1-8x6mKpEQV1-top":{"uid":"edge-vg7Z0EZd1u-vg7Z0EZd1u-bottom-8x6mKpEQV1-8x6mKpEQV1-top","fromNodeId":"vg7Z0EZd1u","fromHandleId":"vg7Z0EZd1u-bottom","toNodeId":"8x6mKpEQV1","toHandleId":"8x6mKpEQV1-top","direction":"ft","selectable":true,"type":"solid","content":{"label":""}},"edge-8x6mKpEQV1-8x6mKpEQV1-bottom-c91Ur0p-AI-c91Ur0p-AI-top":{"uid":"edge-8x6mKpEQV1-8x6mKpEQV1-bottom-c91Ur0p-AI-c91Ur0p-AI-top","fromNodeId":"8x6mKpEQV1","fromHandleId":"8x6mKpEQV1-bottom","toNodeId":"c91Ur0p-AI","toHandleId":"c91Ur0p-AI-top","direction":"ft","selectable":true,"type":"solid","content":{"label":""}},"edge-vg7Z0EZd1u-vg7Z0EZd1u-right-CYddWFZB2g-CYddWFZB2g-left":{"uid":"edge-vg7Z0EZd1u-vg7Z0EZd1u-right-CYddWFZB2g-CYddWFZB2g-left","fromNodeId":"vg7Z0EZd1u","fromHandleId":"vg7Z0EZd1u-right","toNodeId":"CYddWFZB2g","toHandleId":"CYddWFZB2g-left","direction":"ft","selectable":true,"type":"solid","content":{"label":""}},"edge-CYddWFZB2g-CYddWFZB2g-right-zT1dxIBB8b-zT1dxIBB8b-left":{"uid":"edge-CYddWFZB2g-CYddWFZB2g-right-zT1dxIBB8b-zT1dxIBB8b-left","fromNodeId":"CYddWFZB2g","fromHandleId":"CYddWFZB2g-right","toNodeId":"zT1dxIBB8b","toHandleId":"zT1dxIBB8b-left","direction":"ft","selectable":true,"type":"solid","content":{"label":""}}}}}}
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+{"id":-1,"name":"Onboarding diagram","userId":-1,"createdAt":"","updatedAt":"","content":{"items":[{"uid":"lW0CrByFpI","position":{"x":-680,"y":800},"sizes":{"width":399.75,"height":223.921875},"autoheight":true,"blockContent":{"content":[{"type":"filePathNode","attrs":{"pathToFile":"","version":1},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"main.py"}]},{"type":"codeBlock","attrs":{"language":"python","wrapCode":true},"content":[{"type":"text","text":"# Instantiate your AI model\r\nai_model = GeminiModel()\r\n\n# call step 1\r\nnext_step.step_1(ai_model, debug=False)"}]}]},"nodeType":"block"},{"uid":"RuCwNpe8rs","position":{"x":-680,"y":630},"sizes":{"width":399.75,"height":104.953125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"We setup our AI Model, and create our Agent Character Sheet that will help the AI make the Twitter posts."}]}]},"color":{"bgColor":"#2b313a","bgName":"black"},"nodeType":"block"},{"uid":"24FHjKbcb4","position":{"x":-100,"y":820},"sizes":{"width":399.75,"height":171.4375},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"filePathNode","attrs":{"pathToFile":"","version":1},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"step_1.py"}]},{"type":"codeBlock","attrs":{"language":"python","wrapCode":true},"content":[{"type":"text","text":"def step_1(ai_model, debug=False):\r\n"}]}]},"nodeType":"block"},{"uid":"aPeP7UA4jx","position":{"x":-100,"y":620},"sizes":{"width":399.75,"height":153.9375},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"We have a debug feature that can be turned on and off to help people track down what is going wrong."}]},{"type":"paragraph","content":[{"type":"text","text":"This could also be a separate tests file that is run rather than cluttering up the code here."}]}]},"nodeType":"block"},{"uid":"errABHj0Ma","position":{"x":-130,"y":1260},"sizes":{"width":479.75,"height":381.390625},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"filePathNode","attrs":{"pathToFile":"","version":1},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"step_1.py"}]},{"type":"codeBlock","attrs":{"language":"python","wrapCode":true},"content":[{"type":"text","text":"# prompt 1 Character Creation:\r\n# step 1.1: Generate a new agent name, topic, personality, and communication style with the prompt_1 template\r\nprompt_1_vars = {\r\n # \"agent_name\": \"\",\r\n # \"personality\": \"\",\r\n # \"communication_style\": \"\",\r\n # \"topic\": \"\",\n \"concept\": \"alien drone pilot who is a sarcastic asshole visiting earth to report back his findings to his home planet\",\n \"agent_yaml\": yaml.dump(agent)\r\n}\r\n"}]}]},"nodeType":"block"},{"uid":"cgbqTXNQi1","position":{"x":-110,"y":1720},"sizes":{"width":439.78125,"height":146.9375},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"The following is the prompt that has been changed countless times to ensure the AI is returning a yaml file consistently that is valid, as well as the contents of the yaml file that is our secret sauce to the Agent and what it will use to make posts."}]}]},"nodeType":"block"},{"uid":"flbOA-v9zK","position":{"x":-1070,"y":1250},"sizes":{"width":399.765625,"height":416.390625},"autoheight":true,"blockContent":{"content":[{"type":"filePathNode","attrs":{"pathToFile":"","version":1},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"templates\\agent_template.yaml"}]},{"type":"codeBlock","attrs":{"language":"yaml","wrapCode":true},"content":[{"type":"text","text":"name:\r\npersonality:\r\ncommunication_style:\r\nbackstory:\r\nuniverse:\r\ntopic_expertise:\r\nhashtags: []\r\nemojis: []\r\nmodel_type: \"gemini\"\r\nmodel_name: \"gemini-exp-1206\"\r\nmemory_store: \"gemini_chroma\"\r\nconnectors:\r\n twitter: true\r\n telegram: false\r\n discord: false\r\n"}]}]},"nodeType":"block"},{"uid":"bk2x5x3hCZ","position":{"x":-90,"y":1060},"sizes":{"width":399.765625,"height":153.9375},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"We can provide any number of details for our agent we want made. "}]},{"type":"paragraph","content":[{"type":"text","text":"It is best to provide a concept, otherwise its just a random concept the AI decides."}]}]},"color":{"bgColor":"#2b313a","bgName":"black"},"nodeType":"block"},{"uid":"GTc84OB9HI","position":{"x":-820,"y":2320},"sizes":{"width":399.765625,"height":223.921875},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"This is the prompt that will generate the Character Sheet for our Agent in a yaml format. "}]},{"type":"paragraph","content":[{"type":"text","text":"Not the placeholders using double {{ Variable_Name }}"}]},{"type":"paragraph","content":[{"type":"text","text":"As is how we can swap out values in our yaml file for anything we want without having to retype a custom prompt for every character, while still allowing the prompt to be flexible and not generic."}]}]},"nodeType":"block"},{"uid":"LkysyEPIGx","position":{"x":-600,"y":1400},"sizes":{"width":399.78125,"height":104.953125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"This is the ai template we want the ai to fill out and return to us."}]}]},"color":{"bgColor":"#2b313a","bgName":"black"},"nodeType":"block"},{"uid":"40nWX7qZ7r","position":{"x":-90,"y":1950},"sizes":{"width":399.78125,"height":293.90625},"autoheight":true,"blockContent":{"content":[{"type":"filePathNode","attrs":{"pathToFile":"","version":1},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"step_1.py"}]},{"type":"codeBlock","attrs":{"language":"python","wrapCode":true},"content":[{"type":"text","text":"# step 1.2: Run the prompt\r\nagent_data = manager.run_prompt(\r\n # prompt_key=\"prompt_1 (Character Creation)\",\r\n prompt_key=\"promot_1 (Character Sheet Creation)\",\r\n template_vars=prompt_1_vars, \r\n ai_model=ai_model,\r\n debug=debug\r\n)"}]}]},"nodeType":"block"},{"uid":"u17DlfDBf8","position":{"x":-1500,"y":2110},"sizes":{"width":589.9375,"height":1431.203125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"filePathNode","attrs":{"pathToFile":"","version":1},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"prompts\\chain_prompts_v2.yaml"}]},{"type":"codeBlock","attrs":{"language":"yaml","wrapCode":true},"content":[{"type":"text","text":"promot_1 (Character Sheet Creation): |\r\n You are an expert in creative writing, character design, world-building, and marketing. You are tasked with developing a complete character profile for a new agent who will be featured in stories for a Twitter bot. These stories will be broken down into seasons, episodes, and individual tweets (scenes/posts).\r\n\r\n Agent Development Task\r\n Generate a comprehensive YAML file that defines this new agent, including their name, personality, communication style, topic of expertise, backstory, universe, relevant hashtags, and emojis.\r\n Use the following concept if its not empty, otherwise create one.\r\n - concept: {{ concept }}\r\n\r\n Specific Instructions:\r\n 1. Invent a Creative Name:\r\n - If no specific name is provided, create a unique and fitting name for the agent based on the concept of the agent. The name should be suitable for a Twitter handle.\r\n - Agent Name: {{ agent_name }}\r\n\r\n 2. Define the Topic: \r\n - If no specific topic is provided, default to \"Crypto\" or invent a creative topic that lends itself to episodic storytelling.\r\n - Topic: {{ topic }}\r\n\r\n 3. Develop Personality and Style:\r\n - Create a detailed personality for the agent.\r\n - Define a clear communication style for the agent.\r\n - Personality: {{ personality }}\r\n - Communication Style: {{ style }}\r\n\r\n 4. Craft a Backstory:\r\n - Invent a compelling and original backstory for the agent that explains their motivations, skills, and current situation. This backstory should be suitable for unfolding gradually over multiple story arcs.\r\n - Backstory: {{ backstory }}\r\n\r\n 5. Describe the Universe:\r\n - Detail the world(s) or setting where the agent operates. Consider the current state of technology, the social and political landscape, major organizations or factions, and any unique elements relevant to the chosen topic.\r\n - Use a narrative or bullet-point format within the YAML.\r\n - Universe: {{ universe }}\r\n\r\n 6. Generate Marketing Elements:\r\n - Create a list of relevant hashtags that will be used for social media promotion. Include hashtags related to the agent's name, topic, and genre.\r\n - Important: Output the hashtags as a YAML array. Enclose the hashtags in square brackets, separate them with commas, make sure they have double quotes around each hashtag, and put each hashtag on a new line within the brackets. (e.g., `hashtags: [\\\\n- \"#example1\",\\\\n- \"#example2\"\\\\n]` )**\r\n - Create a list of relevant emojis that can be used in tweets to add visual interest and convey meaning.\r\n - Important: Output the emojis as a YAML array. Enclose the emojis in square brackets, separate them with commas, and put each emoji on a new line within the brackets. (e.g., `emojis: [\\\\n- 🐳,\\\\n- 🌊\\\\n]` )**\r\n - Hashtags: {{ hashtags }}\r\n - Emojis: {{ emojis }}\r\n\r\n Output Requirements:\r\n - Only output valid YAML. Do not include any text outside of the YAML structure.\r\n - Output a single, complete YAML file that includes all the fields mentioned above (name, personality, communication_style, topic, backstory, universe, hashtags, emojis).\r\n - Adhere to the specified answer lengths for each field (short for personality and communication style, long for backstory and universe).\r\n\r\n Output exactly as the yaml file:\r\n ```yaml\r\n {{ agent_yaml }}\r\n ```"}]}]},"nodeType":"block"},{"uid":"_FbKHtj4_Z","position":{"x":-770,"y":3470},"sizes":{"width":479.84375,"height":496.875},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"We swap out all the place holders values in our yaml file for the prompt_1_vars we created."}]},{"type":"codeBlock","attrs":{"language":"yaml","wrapCode":true},"content":[{"type":"text","text":"{{ concept }}"}]},{"type":"paragraph","content":[{"type":"text","text":"This is achieved by using the following library."}]},{"type":"codeBlock","attrs":{"language":"python","wrapCode":false},"content":[{"type":"text","text":"from jinja2 import Template"}]},{"type":"paragraph","content":[{"type":"text","text":"In this case"}]},{"type":"codeBlock","attrs":{"language":"python","wrapCode":false},"content":[{"type":"text","text":"# The following yaml variable\n{{ concept }}\n\n# Gets replaced with prompt_1_vars concept variable value\n\"alien drone pilot who is a sarcastic asshole visiting \nearth to report back his findings to his home planet\""}]},{"type":"paragraph"}]},"nodeType":"block"},{"uid":"3hymDJ_oQT","position":{"x":-190,"y":2570},"sizes":{"width":639.9375,"height":783.8125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"filePathNode","attrs":{"pathToFile":"","version":1},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"content_generator.py"}]},{"type":"codeBlock","attrs":{"language":"python","wrapCode":true},"content":[{"type":"text","text":"# -------------------------------------------------------------------\r\n# Generic prompt runner that works with any prompt template\r\n# -------------------------------------------------------------------\r\ndef run_prompt(self, prompt_key, template_vars, ai_model, debug=False):\r\n \"\"\"\r\n Generic prompt runner that works with any prompt template\r\n \r\n Args:\r\n prompt_key: The key for the prompt template (e.g., \"prompt_1\", \"prompt_2\")\r\n template_vars: dict of variables to pass to the template\r\n ai_model: The AI model to use for generating responses\r\n \"\"\"\r\n self.ai_model = ai_model\r\n\n # 1. Load the chain prompts from the YAML file\r\n with open(\"prompts/chain_prompts_v2.yaml\", \"r\", encoding=\"utf-8\") as f:\r\n chain_prompts = yaml.safe_load(f)\r\n\n # 2. Grab the raw prompt template text\r\n prompt_template = chain_prompts[prompt_key]\r\n\n # 3. Use Jinja2 to fill placeholders\n template = Template(prompt_template)\n prompt_text = template.render(**template_vars)\n\n # 4. Call the LLM\n response = ai_model.generate_response(prompt_text)\n\n # # 5. Parse the YAML from the LLM's response\n yaml_response = self.parse_yaml_from_response(response)\n if yaml_response is None:\r\n # Handle parse error or fallback\r\n print(f\"Error: LLM returned invalid YAML for {prompt_key}.\")\r\n return None\r\n\n # return yaml_response\r\n return yaml_response"}]}]},"nodeType":"block"},{"uid":"aKUkNhx4oa","position":{"x":-80,"y":2370},"sizes":{"width":399.828125,"height":83.953125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"Write your note here..."}]}]},"nodeType":"block"},{"uid":"Ka5_2WO78J","position":{"x":-800,"y":2910},"sizes":{"width":399.828125,"height":125.9375},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"The nice things about this function is that it works for all prompts. There is no need to have a custom function for each prompt."}]}]},"nodeType":"block"},{"uid":"ZHSj7Wyw_j","position":{"x":-70,"y":3450},"sizes":{"width":399.84375,"height":104.953125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"We next have to process the results of the response we get from the AI."}]}]},"nodeType":"block"},{"uid":"xQMZhS6h3z","position":{"x":-70,"y":3890},"sizes":{"width":399.84375,"height":104.953125},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"First we save the raw response. This is a temp file that gets overridden each time."}]}]},"nodeType":"block"},{"uid":"kkQcsDI9vz","position":{"x":-60,"y":4220},"sizes":{"width":399.84375,"height":244.921875},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"We then remove our placed holder values we told the AI to put around our yaml file."}]},{"type":"codeBlock","attrs":{"language":"yaml","wrapCode":false},"content":[{"type":"text","text":"``` yaml\n```"}]},{"type":"paragraph","content":[{"type":"text","text":"we also want to remove any empty lines at the start and end of the file."}]}]},"nodeType":"block"},{"uid":"5CWrjQmahA","position":{"x":-50,"y":4660},"sizes":{"width":399.84375,"height":125.9375},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"We then return the filepath for this file so that we can merge together our template for our AI agent we made at the start, and the new values we received."}]}]},"nodeType":"block"},{"uid":"PotOsSoAv5","position":{"x":-40,"y":4970},"sizes":{"width":399.84375,"height":542.234375},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"paragraph","content":[{"type":"text","text":"Our Character Sheet for our Agents secret sauce should be made. We can then do one of the following:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Character Sheet"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Manually edit the files and tweak what we want."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Copy the results and put them into and online AI edit to refine. Just make sure the results are put into a valid yaml file."}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"ChatGPT"}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Google Gemini"}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Claude."}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Just make "}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Re-run the Agent creation process again. You should be provided with a new agent with its own character sheet. To be safe, I would save your current agent to a safe location just in case the AI does create a character with a similar name. Otherwise it will overwrite the files."}]}]}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Posts"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"type":"text","text":"Move onto the next steps to generate the posts your agent will make and post to social media."}]}]}]}]}]}]},"nodeType":"block"},{"uid":"JNsqZAtg37","position":{"x":-1400,"y":1790},"sizes":{"width":379.9375,"height":276.421875},"autoheight":true,"blockContent":{"type":"doc","content":[{"type":"heading","attrs":{"level":1},"content":[{"type":"text","text":"Pro Tip."}]},{"type":"paragraph","content":[{"type":"text","text":"To help with marketing and getting your ai out there. I would be selective on the hashtags it uses and includes."}]},{"type":"paragraph","content":[{"type":"text","text":"More importantly, I would have it sign on or off with a hashtag that is its own name. This can help trace back to your agents account if it goes viral. It does not cost you anything and could help get eyes on your agent / project."}]}]},"nodeType":"block"}],"configs":{"centerX":718.6506797353916,"centerY":-136.41252377462126,"zoomLevel":0.5629629445664683},"arrowData":{"arrowsMap":{"arrow-point-bI0wBh3Ufk-bottom-point-PSPLIYKa9J-top":{"to":"point-PSPLIYKa9J-top","from":"point-bI0wBh3Ufk-bottom","label":"Normal Box","direction":"ft","selectable":true},"arrow-point-bI0wBh3Ufk-bottom-point-ytXK_ayIc1-top":{"to":"point-ytXK_ayIc1-top","from":"point-bI0wBh3Ufk-bottom","label":"Code 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diff --git a/docs/about-llms.md b/docs/about-llms.md
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index 0000000..e487c79
--- /dev/null
+++ b/docs/about-llms.md
@@ -0,0 +1,164 @@
+# Explanation: Understanding LLMs, Prompt Engineering, and ARAI AI
+
+**ARAI AI Agents** leverages advanced **Large Language Models (LLMs)** to produce non-repetitive, context-aware content through various strategies, including **prompt chaining**. This document provides a brief history of AI, explains how LLMs differ from other AI approaches, covers tokenization and prompts, and explores two popular prompt engineering techniques—_Prompt Chaining_ and _Chain-of-Thought (CoT) Prompting_. We’ll also highlight ARAI’s approach to orchestrating these methods.
+
+---
+
+## Table of Contents
+
+1. [A Brief History of AI](#1-a-brief-history-of-ai)
+2. [How LLMs Differ from Other AI Approaches](#2-how-llms-differ-from-other-ai-approaches)
+3. [Tokens, Prompts & Generation](#3-tokens-prompts-generation)
+4. [Two Paths to Take: Prompt Chaining vs. Chain-of-Thought Prompting](#4-two-paths-to-take-prompt-chaining-vs-chain-of-thought-prompting)
+5. [Limitations of LLMs](#5-limitations-of-llms)
+6. [How ARAI AI Agents Leverage LLMs](#6-how-arai-ai-agents-leverage-llms)
+7. [Conclusion](#7-conclusion)
+
+---
+
+## 1. A Brief History of AI
+
+1. **Symbolic AI (1950s–1980s)**
+ Early AI largely relied on **rule-based** or “expert” systems, manually encoding logic rather than learning from large datasets.
+
+2. **Machine Learning (1980s–2000s)**
+ Statistical algorithms (decision trees, SVMs, etc.) gained traction, but they often required extensive feature engineering and couldn’t handle nuanced language tasks well.
+
+3. **Deep Learning (2010s)**
+ Neural networks scaled to many layers (“deep”) ushered in a new era of success in fields like vision, speech, and basic NLP tasks, yet still had limitations with long-range text dependencies.
+
+4. **Transformers & LLMs (Late 2010s–Present)**
+ The **Transformer** architecture ([“Attention Is All You Need”](https://arxiv.org/abs/1706.03762)) revolutionized NLP. **Large Language Models (LLMs)** like GPT (OpenAI), BERT (Google), and others leverage massive datasets, enabling more context-aware and flexible text generation.
+
+---
+
+## 2. How LLMs Differ from Other AI Approaches
+
+1. **Context Handling**
+ LLMs excel at understanding and generating text with long-range context, unlike older models that quickly lost track of previous content.
+
+2. **General-Purpose Functionality**
+ Traditional AI is usually _task-specific_, whereas modern LLMs can _adapt_ to various language-related tasks via well-structured prompts.
+
+3. **Few-Shot & Zero-Shot Learning**
+ LLMs can tackle tasks with minimal examples, a huge leap from older machine learning methods requiring large labeled datasets for each new domain.
+
+4. **Fluency & Creativity**
+ Transformer-based models produce coherent, contextually rich text that often feels natural and human-like.
+
+---
+
+## 3. Tokens, Prompts & Generation
+
+### 3.1 Tokenization
+
+- **What Is a Token?**
+ A **token** can be a sub-word, punctuation, or symbol. LLMs process text sequentially in tokens.
+- **Context Window**
+ LLMs only have a finite **context window** (e.g., 2k–32k tokens). If your conversation exceeds that window, older context may be dropped or truncated.
+
+### 3.2 Prompts
+
+- **Prompt as an Instruction**
+ A **prompt** is the text or instruction you give an LLM.
+- **Prompt Engineering**
+ The art of shaping prompts to achieve specific outputs is called **prompt engineering**. Effective prompts may include role instructions, examples, or constraints.
+
+---
+
+## 4. Two Paths to Take: Prompt Chaining vs. Chain-of-Thought Prompting
+
+**Prompt engineering** is the process of writing prompts that guide **artificial intelligence (AI) models** (LLMs) to generate desired outputs. Two popular techniques often used to improve the quality and reliability of responses are **Prompt Chaining** and **Chain-of-Thought (CoT) Prompting**. Each technique offers unique advantages and suits different types of tasks.
+
+### 4.1 Prompt Chaining
+
+- **Definition**
+ In **Prompt Chaining**, you break down a complex task into a series of smaller prompts. Each prompt’s output feeds into the next, ensuring the model carries forward relevant context and partial results.
+
+- **Use Case**
+ Ideal for:
+
+ - **Sequential tasks** (e.g., multi-episode stories, multi-step transformations).
+ - **Maintaining context** over multiple posts or interactions.
+ - **Structured workflows** where each step refines or expands the content.
+
+- **Example**
+ 1. **Prompt 1**: “Generate a character backstory.”
+ 2. **Prompt 2**: “Using the backstory, outline a 5-episode arc.”
+ 3. **Prompt 3**: “Write the first episode’s script referencing the outline.”
+
+This approach is central to how **ARAI** orchestrates episodes and seasons while preventing repetitive content.
+
+### 4.2 Chain-of-Thought (CoT) Prompting
+
+- **Definition**
+ **CoT Prompting** encourages the model to write out its reasoning steps before giving a final answer. Instead of simply asking for a solution, you instruct the LLM to “show its work” in a structured or step-by-step explanation.
+
+- **Use Case**
+ Best for:
+
+ - **Complex problem-solving** (math, logic puzzles, or multi-faceted questions).
+ - **Diagnostic tasks** (where seeing intermediate reasoning is valuable).
+ - **Ensuring correctness** by revealing potential errors in the reasoning chain.
+
+- **Example**
+ 1. Prompt: “Explain how to solve this math problem step by step, then provide the final answer.”
+ 2. The model outputs a _reasoning chain_ (hidden or partially visible) and a final solution.
+
+In short, **CoT** is more about unveiling the reasoning process. **Prompt Chaining** is about splitting tasks into multiple sequential steps. **ARAI** primarily uses _Prompt Chaining_ but can combine CoT for more in-depth reasoning within each step.
+
+---
+
+## 5. Limitations of LLMs
+
+1. **Hallucinations**
+ LLMs may invent details when unsure, which can lead to plausible-sounding but incorrect answers.
+
+2. **Context Window Constraints**
+ They can only handle a limited token count at once. Exceeding that limit truncates older parts of the conversation.
+
+3. **Lack of True Understanding**
+ Despite generating sophisticated text, LLMs do not possess consciousness or genuine comprehension.
+
+4. **Bias & Ethical Concerns**
+ LLMs can reflect biases present in their training data. Caution is advised, especially for public-facing content.
+
+5. **Prompt Quality**
+ Output is only as good as the prompt. Poorly structured requests yield suboptimal results.
+
+---
+
+## 6. How ARAI AI Agents Leverage LLMs
+
+**ARAI AI Agents** harness LLMs with a focus on **Prompt Chaining** for narrative-driven content. Some highlights:
+
+1. **Story-First Content**
+ ARAI uses a _TV show or cinematic model_—seasons, episodes, scenes—to maintain overarching context.
+2. **Chained Prompts**
+ - Each step (episode, scene, or post) references the preceding step’s output, ensuring continuity and preventing repetition.
+3. **Multi-Agent Collaboration**
+ - ARAI can integrate multiple agent personalities, each guided by specialized prompts or constraints.
+4. **Chain-of-Thought (Optional)**
+ - For specific logic or puzzle-based tasks, ARAI can enable CoT to capture the LLM’s reasoning process more transparently.
+
+---
+
+## 7. Conclusion
+
+Modern **Large Language Models** give us _unprecedented flexibility_ in text generation. By employing **Prompt Chaining** and, when necessary, **Chain-of-Thought Prompting**, we guide LLMs toward more coherent, context-rich outputs that serve both creative and analytical tasks.
+
+**ARAI AI** exemplifies these methods by:
+
+- Building multi-episode narratives that avoid redundancy.
+- Utilizing carefully structured prompts to maintain story context across seasons.
+- Embracing or bypassing CoT as required by the complexity of each scenario.
+
+---
+
+> **Next Steps**:
+>
+> - Check out our [How-To Guides](./how-to-guides.md) for environment setup and configuring your LLM keys.
+> - Dive into [tutorials](./tutorials.md) for hands-on practice creating your first story-driven agent.
+> - See the [Reference](./reference.md) docs for ARAI’s APIs and modules.
+
+**Happy prompt engineering with ARAI AI!**
diff --git a/docs/api/SUMMARY.md b/docs/api/SUMMARY.md
new file mode 100644
index 0000000..7be0090
--- /dev/null
+++ b/docs/api/SUMMARY.md
@@ -0,0 +1,16 @@
+- auth
+ - [twitter_app_auth](auth/twitter_app_auth.md)
+- connectors
+ - [twitter_connector](connectors/twitter_connector.md)
+- [main](main.md)
+- models
+ - [base_model](models/base_model.md)
+ - [gemini_model](models/gemini_model.md)
+- prompt_chaining
+ - [step_1](prompt_chaining/step_1.md)
+ - [step_2](prompt_chaining/step_2.md)
+ - [step_3](prompt_chaining/step_3.md)
+- utils
+ - [content_generator](utils/content_generator.md)
+ - [post_manager](utils/post_manager.md)
+ - [template_types](utils/template_types.md)
diff --git a/docs/api/auth/index.md b/docs/api/auth/index.md
new file mode 100644
index 0000000..015a98f
--- /dev/null
+++ b/docs/api/auth/index.md
@@ -0,0 +1,3 @@
+# auth
+
+::: arai_ai_agents.auth
diff --git a/docs/api/auth/twitter_app_auth.md b/docs/api/auth/twitter_app_auth.md
new file mode 100644
index 0000000..7894e7a
--- /dev/null
+++ b/docs/api/auth/twitter_app_auth.md
@@ -0,0 +1,3 @@
+# twitter_app_auth
+
+::: arai_ai_agents.auth.twitter_app_auth
diff --git a/docs/api/connectors/index.md b/docs/api/connectors/index.md
new file mode 100644
index 0000000..51cb370
--- /dev/null
+++ b/docs/api/connectors/index.md
@@ -0,0 +1,3 @@
+# connectors
+
+::: arai_ai_agents.connectors
diff --git a/docs/api/connectors/twitter_connector.md b/docs/api/connectors/twitter_connector.md
new file mode 100644
index 0000000..c56d177
--- /dev/null
+++ b/docs/api/connectors/twitter_connector.md
@@ -0,0 +1,3 @@
+# twitter_connector
+
+::: arai_ai_agents.connectors.twitter_connector
diff --git a/docs/api/main.md b/docs/api/main.md
new file mode 100644
index 0000000..076d9ca
--- /dev/null
+++ b/docs/api/main.md
@@ -0,0 +1,3 @@
+# main
+
+::: arai_ai_agents.main
diff --git a/docs/api/models/base_model.md b/docs/api/models/base_model.md
new file mode 100644
index 0000000..dfa59da
--- /dev/null
+++ b/docs/api/models/base_model.md
@@ -0,0 +1,3 @@
+# base_model
+
+::: arai_ai_agents.models.base_model
diff --git a/docs/api/models/gemini_model.md b/docs/api/models/gemini_model.md
new file mode 100644
index 0000000..0fba7d1
--- /dev/null
+++ b/docs/api/models/gemini_model.md
@@ -0,0 +1,3 @@
+# gemini_model
+
+::: arai_ai_agents.models.gemini_model
diff --git a/docs/api/models/index.md b/docs/api/models/index.md
new file mode 100644
index 0000000..a1ef402
--- /dev/null
+++ b/docs/api/models/index.md
@@ -0,0 +1,3 @@
+# models
+
+::: arai_ai_agents.models
diff --git a/docs/api/prompt_chaining/index.md b/docs/api/prompt_chaining/index.md
new file mode 100644
index 0000000..ec55e85
--- /dev/null
+++ b/docs/api/prompt_chaining/index.md
@@ -0,0 +1,3 @@
+# prompt_chaining
+
+::: arai_ai_agents.prompt_chaining
diff --git a/docs/api/prompt_chaining/step_1.md b/docs/api/prompt_chaining/step_1.md
new file mode 100644
index 0000000..0c3f28b
--- /dev/null
+++ b/docs/api/prompt_chaining/step_1.md
@@ -0,0 +1,3 @@
+# step_1
+
+::: arai_ai_agents.prompt_chaining.step_1
diff --git a/docs/api/prompt_chaining/step_2.md b/docs/api/prompt_chaining/step_2.md
new file mode 100644
index 0000000..78bdcb6
--- /dev/null
+++ b/docs/api/prompt_chaining/step_2.md
@@ -0,0 +1,3 @@
+# step_2
+
+::: arai_ai_agents.prompt_chaining.step_2
diff --git a/docs/api/prompt_chaining/step_3.md b/docs/api/prompt_chaining/step_3.md
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--- /dev/null
+++ b/docs/api/prompt_chaining/step_3.md
@@ -0,0 +1,3 @@
+# step_3
+
+::: arai_ai_agents.prompt_chaining.step_3
diff --git a/docs/api/utils/content_generator.md b/docs/api/utils/content_generator.md
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+++ b/docs/api/utils/content_generator.md
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+# content_generator
+
+::: arai_ai_agents.utils.content_generator
diff --git a/docs/api/utils/index.md b/docs/api/utils/index.md
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+++ b/docs/api/utils/index.md
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+# utils
+
+::: arai_ai_agents.utils
diff --git a/docs/api/utils/post_manager.md b/docs/api/utils/post_manager.md
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+++ b/docs/api/utils/post_manager.md
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+# post_manager
+
+::: arai_ai_agents.utils.post_manager
diff --git a/docs/api/utils/template_types.md b/docs/api/utils/template_types.md
new file mode 100644
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+++ b/docs/api/utils/template_types.md
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+# template_types
+
+::: arai_ai_agents.utils.template_types
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diff --git a/docs/cli-guide.md b/docs/cli-guide.md
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+# ARAI CLI Usage Guide
+
+The **ARAI AI Agents** command-line interface (CLI) offers a straightforward way to manage your AI agents, create media content (seasons/episodes), and schedule or force posts. This guide will walk you through the main menu options and typical use cases.
+
+---
+
+## Table of Contents
+
+1. [Launching the CLI](#1-launching-the-cli)
+2. [Main Menu Overview](#2-main-menu-overview)
+3. [CLI Tips & Tricks](#3-cli-tips--tricks)
+4. [Common Workflows](#4-common-workflows)
+5. [Exiting the CLI](#5-exiting-the-cli)
+6. [Troubleshooting](#6-troubleshooting)
+7. [Conclusion](#7-conclusion)
+
+---
+
+## 1. Launching the CLI
+
+1. **Activate your Conda environment** (if using conda):
+ ```bash
+ conda activate arai_ai_agents
+ ```
+2. **Navigate** to your `arai_ai_agents` folder:
+ ```bash
+ cd arai_ai_agents
+ ```
+3. **Run** the CLI script:
+ ```bash
+ python main.py
+ ```
+4. You should see a welcome prompt similar to:
+
+ ```plaintext
+ === Main Menu ===
+ Welcome to ARAI Agents.
+ Please select an option:
+
+ Current Agent: None
+
+ = Agent Management =
+ 1. Select an existing Agent
+ 2. Create a new Agent
+
+ = Media Management =
+ 3. Create a new Season
+ 4. Create Season posts
+
+ = Scheduler Management =
+ 5. Start Scheduler
+ 6. Check posting status
+ 7. Force post now
+ 8. Pause/Resume posting
+
+ = Miscellaneous =
+ 9. Exit
+
+ Enter your choice (1-9):
+ ```
+
+---
+
+## 2. Main Menu Overview
+
+Below is a breakdown of each **main menu** option and what it does:
+
+### 1. Select an Existing Agent
+
+If you have previously created AI agents, you can pick which one you want to work with. After you select an agent, the **Current Agent** will change from `None` to the chosen agent’s name.
+
+- **Why**: This allows you to isolate tasks (creating posts, scheduling, etc.) for a specific agent.
+
+### 2. Create a New Agent
+
+This option launches a short wizard to build a new AI agent from scratch:
+
+- **Steps**:
+ 1. Provide a **name** or handle for the agent.
+ 2. Supply any extra info (e.g., personality traits, brand guidelines).
+ 3. Confirm creation.
+- Once complete, the new agent appears in the list of available agents.
+
+---
+
+### 3. Create a New Season
+
+Seasons act like **story arcs** or **thematic campaigns** for your agent’s content. Selecting this option:
+
+1. Prompts you for a **season name** (e.g., “Season 1: The Great Launch”).
+2. Optionally requests a **description** or theme.
+3. Creates a basic YAML or JSON entry to track this season’s details.
+
+### 4. Create Season Posts
+
+After you have a season in place (or multiple), this option helps you **generate posts** (or “episodes”) related to that season. You may be asked:
+
+1. Which **season** you want to create posts for.
+2. How many posts (scenes/episodes) you need.
+3. Any special context or prompts.
+ The CLI will then process prompts and **batch-generate** content (e.g., social media posts) for that season.
+
+---
+
+### 5. Start Scheduler
+
+ARAI includes a **scheduler** that can automatically post content at set intervals:
+
+1. When you start the scheduler, you’ll see logs or debug info showing if posting is live or in “test mode.”
+2. The system will queue up any scheduled posts from the currently selected agent (or all agents, depending on your config).
+
+### 6. Check Posting Status
+
+Use this to see **which posts** are scheduled, how many have gone out, and whether any are **pending** or **on hold**. It helps confirm that your agent is indeed posting or waiting to post.
+
+### 7. Force Post Now
+
+If you don’t want to wait for the next scheduled slot, you can **force** the system to post immediately. This can be handy if:
+
+- You need to push out an urgent update.
+- You want to test how the post looks on your platform right away.
+
+### 8. Pause/Resume Posting
+
+Allows you to **temporarily halt** the scheduler without losing its state. When paused:
+
+- No new posts are sent to Twitter (or other connectors).
+- You can safely resume posting later when you’re ready.
+
+---
+
+### 9. Exit
+
+Closes the CLI. Any running background jobs or schedulers (if not paused or stopped) may continue operating until their next iteration, but the main interactive session ends.
+
+---
+
+## 3. CLI Tips & Tricks
+
+- **Switching Agents**: You can always go back to option `1` to select another agent at any time.
+- **Inspecting Logs**: If something goes wrong (e.g., an error connecting to Twitter), check the CLI output or your log files in `configs/agent_folder/agentName_post_log.yaml` for debug information.
+- **Using the .env**: Make sure your environment variables (Twitter keys, Gemini API key, etc.) are properly set in your `.env` file or system environment so the CLI can access them.
+- **In-Depth Configuration**: If you prefer custom intervals, advanced scheduling, or specialized post flows, you can modify the relevant `.yaml` or `.json` config files in the `configs/` directory. The CLI reads from and writes to these files to maintain state.
+
+---
+
+## 4. Common Workflows
+
+1. **Create & Configure a New Agent**
+
+ 1. Select option **2** at the main menu.
+ 2. Provide agent details.
+ 3. Optionally, create a season (option **3**) and season posts (option **4**).
+
+2. **Schedule Posts for Automated Publishing**
+
+ 1. Start the scheduler with option **5**.
+ 2. Check the status with option **6**.
+ 3. If you want immediate posting, option **7** triggers it right away.
+ 4. Pause/Resume with option **8**.
+
+3. **Batch Generate Contextual Posts**
+ 1. Select or create an agent.
+ 2. Use option **3** to define a thematic “season.”
+ 3. Then option **4** to generate multiple posts in one go.
+ 4. Let the scheduler handle posting times, or force them out (option **7**).
+
+---
+
+## 5. Exiting the CLI
+
+When you’re done:
+
+1. Select **9** at the main menu or press `Ctrl + C` to exit the application.
+2. Any unsaved changes in memory will typically be written out to config/log files when you exit.
+
+---
+
+## 6. Troubleshooting
+
+- **CLI Not Launching**: Double-check you’ve activated your conda environment or installed the dependencies with `pip install -r requirements.txt`.
+- **Agent Not Appearing in Menu**: The agent creation process may have failed or you forgot to confirm. Check your `/configs/agent_folder/` for a new `.yaml` or `.json`.
+- **No Posting**: Confirm `twitter_live = True` in your main settings if you want real posts. Otherwise, they’re logged to a local file.
+- **Crashes or Errors**: Review the CLI output or logs for stack traces. If the system can’t find your API keys, ensure your `.env` or environment variables are correctly set.
+
+---
+
+## Conclusion
+
+That’s the basics of using the **ARAI AI Agents** CLI. From selecting or creating new agents, to generating multi-episode story arcs, to scheduling or forcing posts, the menu-based system keeps everything at your fingertips. If you have questions or encounter issues, check out the official [GitHub repository](https://github.com/arai-ai/arai_ai_agents) or submit an Issue for assistance.
+
+**Happy creating and scheduling with ARAI!**
diff --git a/docs/code.md b/docs/code.md
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--- /dev/null
+++ b/docs/code.md
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+# Code Examples
+
+## Code annotation Examples
+
+### Codeblocks
+
+Some `code` goes here.
+
+### Plain codeblock
+
+A plain codeblock:
+
+```
+Some code here
+def my_function():
+ print("Hello, world!")
+```
+
+#### Code for a specific language
+
+Some more code with the `py` at the start:
+
+```py
+def my_function():
+ print("Hello, world!")
+```
+
+#### With title
+
+```py title="my_function.py"
+def my_function():
+ print("Hello, world!")
+```
+
+#### With title and line numbers
+
+```py title="my_function.py" linenums="1"
+def my_function():
+ print("Hello, world!")
+```
+
+#### With line numbers and line highlighting
+
+```py title="my_function.py" linenums="1" hl_lines="2"
+def my_function():
+ print("Hello, world!")
+```
+
+## Icons and Emojis
+
+:smile:
+
+:fontawesome-brands-github-alt:
+
+:fontawesome-brands-twitter:{ .twitter}
+
+:octicons-heart-fill-24:{ .heart}
diff --git a/docs/explanation.md b/docs/explanation.md
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--- /dev/null
+++ b/docs/explanation.md
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+# Explanation: ARAI’s Prompt Chaining Approach
+
+**ARAI AI Agents** employs a narrative-based prompt chaining methodology to create cohesive, non-repetitive, and engaging content—ranging from **tweets** and **social media posts** to entire **story arcs**. This approach draws inspiration from **Hollywood screenwriters**, using seasons and episodes to structure continuous storylines and maintain context.
+
+---
+
+## Table of Contents
+
+1. [Core Concept](#1-core-concept)
+2. [Why “Prompt Chaining”?](#2-why-prompt-chaining)
+3. [High-Level Workflow](#3-high-level-workflow)
+4. [The Importance of a Large Context Window](#4-the-importance-of-a-large-context-window)
+5. [Example Flow](#5-example-flow)
+6. [Benefits of This Approach](#6-benefits-of-this-approach)
+7. [Conclusion](#7-conclusion)
+
+---
+
+## Core Concept
+
+1. **Non-Repetitive Storytelling**
+
+ - Each generated post builds on previous context, avoiding repetitive language or ideas.
+ - The AI references a “universe” or “world Bible” similar to what screenwriters use in film or television.
+
+2. **TV Show & Cinematic Structure**
+
+ - Stories are segmented into **seasons** and **episodes**.
+ - Each **episode** can be further divided into “scenes,” which are effectively _individual posts_ or _tweets_.
+
+3. **Prompt Chaining**
+ - **Prompts** are carefully crafted to pass relevant context (e.g., last episode events, the overall season arc).
+ - **Chained prompts** ensure that each subsequent piece of content knows what has happened before and maintains consistency.
+
+---
+
+## Why “Prompt Chaining”?
+
+Typical AI-generated content can become repetitive when prompts are not carefully managed. **Prompt chaining** solves this by:
+
+- **Carrying Over Context**
+ Each step (post, scene, or episode) includes details of what came before, preventing “resetting” or “forgetting” and ensuring a natural flow.
+
+- **Layering Background & Universe Data**
+ The AI “knows” the characters’ personalities, the setting, and prior events. This leads to more believable and varied outputs.
+
+- **Batch & Story-Based Generation**
+ Instead of randomly generating single posts, ARAI processes entire sequences of posts together, referencing each other for narrative coherence.
+
+---
+
+## High-Level Workflow
+
+1. **Character Background Sheets**
+
+ - The system prompts the AI to create detailed character profiles:
+ - **Universe & Backstory** (setting, tone, etc.)
+ - **Traits** (personality, style, goals)
+ - **Emojis & Hashtags** they might use
+
+2. **Season & Episode Creation**
+
+ - **Season** = a broad story arc (e.g., Season 1: “The Origin”).
+ - **Episodes** = subdivisions in each season (e.g., Episode 1: “Awakening”, Episode 2: “New Allies”).
+ - This structure emulates a TV series format, providing an expansive canvas for narrative progression.
+
+3. **Scene-to-Post Conversion**
+
+ - Each **episode** is broken down into “scenes,” which **map directly to individual social media posts**.
+ - _Context injection_ includes:
+ - Last episode’s key events
+ - Character developments
+ - The overall “season” summary
+
+4. **Batch Generation**
+
+ - ARAI then prompts the AI to generate multiple posts at once (or in succession) so they share context and maintain narrative continuity.
+ - This ensures each post references the correct timeline and plot details.
+
+5. **Season Rollovers**
+ - After a season ends, **context** from that entire season is folded into the AI’s prompt for the next season.
+ - This preserves continuity across seasons, allowing characters to evolve over time.
+
+---
+
+## The Importance of a Large Context Window
+
+To make prompt chaining truly effective, the underlying AI model needs a large **context window**. The **context window** refers to the amount of text (measured in tokens) that the model can “remember” and consider when generating a response.
+
+- **Why We Use `2.0 Experimental Advanced model in Gemini Advanced.`**
+ By default, ARAI uses the `Gemini-Exp-1206` model because it offers a large context window. This is ideal for our narrative-driven approach because:
+
+ - **Long-Term Memory:**
+ The model can retain information from earlier parts of the conversation (e.g., details from previous episodes or seasons), which is crucial for maintaining consistency in long-running storylines.
+
+ - **Complex Narrative Structures:**
+ A larger context window allows the model to handle intricate plots, multiple characters, and evolving relationships within the narrative.
+
+ - **Reduced Repetition:**
+ With more context available, the model is less likely to fall back on repetitive phrases or generic responses.
+
+- **Model Selection and Context Window:**
+ When choosing a model for ARAI, the size of the context window is a primary consideration. While a larger context window generally improves performance on complex tasks, it can also increase computational cost and latency. The `gemini-pro` model provides a good balance between context size and efficiency for our use case.
+
+---
+
+## Example Flow
+
+1. **Initialize a “Concept of AI Agent”**
+ ```plaintext
+ Prompt:
+ "You are a comedic AI sidekick from a futuristic Mars colony. Generate a backstory
+ detailing your origin, personality traits, and comedic style. Also include an
+ emoji palette and hashtags you frequently use."
+ ```
+2. **Generate a Character Sheet**
+ - AI responds with backstory, style notes, emojis, hashtags (`#MarsLife`, `#CosmicComedy`, etc.).
+3. **Create a Season Plan**
+ - _Season 1: “Launch Day”_ with 5 episodes.
+4. **Episode 1**
+ - Provide the AI with _scene outlines_ or _beats_ to cover in episode 1.
+ - AI generates Scenes 1, 2, 3, each as separate social media posts but referencing each other’s details.
+5. **Proceed to Episode 2**
+ - Summarize Episode 1 outcomes: “In Episode 1, your comedic AI discovered a stowaway on the Mars rocket…”
+ - The AI crafts next scenes with knowledge of Episode 1’s revelations.
+6. **Season Finale**
+ - Summaries of all episodes in Season 1 inform the _Season 2_ kickoff prompt.
+
+---
+
+## Benefits of This Approach
+
+- **Narrative Consistency**: By continuously chaining prompts, the AI won’t “forget” critical details and the story remains coherent.
+- **Creative Expansion**: You can easily scale from short comedic sketches to grand multi-season arcs.
+- **Flexibility**: Adapt the same system for _comics_, _film scripts_, or _novel chapters_, just by tweaking prompts.
+- **Engagement**: Social media followers can follow an unfolding storyline rather than seeing disjointed or repetitive posts.
+
+---
+
+## Conclusion
+
+**ARAI AI Agents** leverages a _story-first, chain-of-thought approach_ to generating content. By structuring the process akin to **Hollywood screenwriting** and dividing it into **seasons, episodes, and scenes**, ARAI creates vibrant, interconnected narratives. Each step references previous context, preventing repetitive output and fostering deeper engagement for readers (or social media audiences).
+
+If you’d like to learn more about setting up your environment or configuring connectors:
+
+- Check our [How-To Guides](./how-to-guides.md) for environment & API key setup
+- Look at the [Tutorials](./tutorials.md) for step-by-step instructions on building your first season-based storyline
+
+**Happy storytelling with ARAI!**
diff --git a/docs/how-to-guides.md b/docs/how-to-guides.md
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+# How-To Guide: Setting up ARAI AI, Google Gemini, and Twitter
+
+This guide walks you through the essential steps for installing **ARAI AI Agents** in a conda environment, configuring the Google Gemini API key, and authenticating a Twitter account for agent interactions. We also detail how environment variables are managed via a `.env` file using `python-dotenv`.
+
+---
+
+## Table of Contents
+
+1. [Set Up a Conda Environment](#1-set-up-a-conda-environment)
+2. [Install ARAI AI Agents](#2-install-arai-ai-agents)
+3. [Obtain a Google Gemini API Key](#3-obtain-a-google-gemini-api-key)
+4. [Get a Twitter API Key](#4-get-a-twitter-api-key)
+5. [Authenticate Your Twitter Account](#5-authenticate-your-twitter-account)
+6. [Debug Mode](#6-debug-mode)
+7. [Processing Speed & Wait Times](#7-processing-speed--wait-times)
+8. [Next Steps](#8-next-steps)
+9. [Troubleshooting](#9-troubleshooting)
+
+---
+
+## 1. Set Up a Conda Environment
+
+Conda environments keep dependencies isolated and help avoid conflicts with other projects on your system.
+
+1. **Install or Update Conda**
+
+ - Download and install [Miniconda](https://docs.conda.io/en/latest/miniconda.html) or [Anaconda](https://www.anaconda.com/) if you haven’t already.
+ - Ensure your conda is up to date:
+ ```bash
+ conda update conda
+ ```
+
+2. **Create a New Environment**
+ - In your terminal or Anaconda Prompt, run:
+ ```bash
+ conda create --name arai_ai_agents python=3.11
+ ```
+ - Activate your new environment:
+ ```bash
+ conda activate arai_ai_agents
+ ```
+
+You now have an isolated environment ready for ARAI AI Agents.
+
+---
+
+## 2. Install ARAI AI Agents
+
+1. **Clone the Repository**
+ ```bash
+ git clone https://github.com/arai-ai/arai_ai_agents.git
+ ```
+2. **Navigate to the Project Directory**
+ ```bash
+ cd arai_ai_agents
+ ```
+3. **Install Dependencies**
+ Within your `arai_ai_agents` folder, install the required packages:
+ ```bash
+ pip install -r requirements.txt
+ ```
+4. **Run the Main Script**
+ ```bash
+ python main.py
+ ```
+ This starts the primary ARAI AI Agents application, which includes agent configurations, connectors, and any prompt-chaining logic.
+
+---
+
+## 3. Obtain a Google Gemini API Key
+
+_(Note: The steps below are illustrative. Refer to Google’s official documentation for the most up-to-date instructions.)_
+
+1. **Sign Up or Log In**
+
+ - Go to [Google Cloud Console](https://console.cloud.google.com/).
+
+2. **Create a New Project** (if needed)
+
+ - Click **Select a project** → **New Project**. Give it a name, then create it.
+
+3. **Enable the Gemini API**
+
+ - Navigate to **APIs & Services** → **Library**.
+ - Search for **Gemini API** (or equivalent) and enable it.
+
+4. **Create Credentials**
+
+ - Under **APIs & Services** → **Credentials**, click **+ CREATE CREDENTIALS** → **API key**.
+ - Copy the **API key** (e.g., `AIzaSyD...`).
+
+5. **Set the Environment Variable**
+ - Either place it in your system environment:
+ ```bash
+ export GEMINI_API_KEY="AIzaSyD..."
+ ```
+ - **Or** place the key in your `.env` file at the root of the project:
+ ```bash
+ GOOGLE_GEMINI_API_KEY=AIzaSyD...
+ ```
+
+_(ARAI uses `python-dotenv` to automatically load variables from `.env` if configured.)_
+
+---
+
+## 4. Get a Twitter API Key
+
+To allow ARAI AI Agents to interact with Twitter, you need developer credentials.
+
+1. **Apply for a Twitter Developer Account**
+
+ - Go to the [Twitter Developer Portal](https://developer.twitter.com/) and apply for a Developer account.
+
+2. **Create a Project & App**
+
+ - Once approved, create a **Project**, then create an **App** within that project.
+
+3. **Generate Keys & Tokens**
+ - Under **Keys and tokens**, generate an **API Key**, **API Secret Key**, and **Bearer Token** (or OAuth 2.0 Client if required).
+ - Copy these values somewhere secure.
+
+Example `.env` variables:
+
+```bash
+TWITTER_API_KEY=xxxxxxxxxxxxxxxxxxx
+TWITTER_API_SECRET=yyyyyyyyyyyyyyyyyyyyyyyyy
+TWITTER_BEARER_TOKEN=AAAAAAAAAAAAAAAAAAAAAAA
+```
+
+---
+
+## 5. Authenticate Your Twitter Account
+
+ARAI AI Agents includes modules or scripts (e.g., `twitter_app_auth.py`) to handle Twitter OAuth or token-based authentication. Below is a typical approach:
+
+1. **Load Environment Variables**
+ Make sure you have `python-dotenv` installed and your `.env` file in the project root. For example:
+
+ ```python
+ from dotenv import load_dotenv
+ load_dotenv() # This will load your .env variables
+ ```
+
+2. **Locate the Auth File**
+
+ - In `arai_ai_agents/auth/twitter_app_auth.py` (or a similar file):
+
+ ```python
+ import os
+ from dotenv import load_dotenv
+
+ load_dotenv() # ensures environment variables are loaded
+
+ TWITTER_API_KEY = os.getenv("TWITTER_API_KEY")
+ TWITTER_API_SECRET = os.getenv("TWITTER_API_SECRET")
+ TWITTER_BEARER_TOKEN = os.getenv("TWITTER_BEARER_TOKEN")
+
+ # Additional OAuth logic, if needed
+ ```
+
+3. **Set Your Credentials**
+
+ - Store your credentials in a `.env` file or environment variables. The code snippet above automatically pulls from your environment.
+
+4. **Run the App**
+
+ - When you run `python main.py`, the application will attempt to initialize the `TwitterConnector` (if configured), using your environment variables.
+
+5. **Test Connectivity**
+
+ - Use the connector or a test script to verify you can post or retrieve tweets.
+ - Example:
+
+ ```python
+ from arai_ai_agents.connectors.twitter_connector import TwitterConnector
+ import os
+
+ connector = TwitterConnector(
+ api_key=os.getenv("TWITTER_API_KEY"),
+ api_secret=os.getenv("TWITTER_API_SECRET"),
+ bearer_token=os.getenv("TWITTER_BEARER_TOKEN")
+ )
+ connector.test_connection()
+ ```
+
+6. **Twitter Access Tokens**
+
+ - To post on behalf of a user, you will need _access tokens_ for the specific Twitter account.
+ - The `twitter_app_auth.py` file may contain a function to fetch or handle these tokens.
+ - Save your access tokens in the `.env` file:
+ ```bash
+ TWITTER_ACCESS_TOKEN=XXXXXXXX
+ TWITTER_ACCESS_TOKEN_SECRET=YYYYYYYY
+ ```
+
+7. **Enable Twitter Live Mode**
+ - Set `twitter_live = True` in the `main.py` file so you can post to Twitter live:
+ ```python
+ twitter_live = True
+ ```
+ - Once successfully authenticated, your ARAI agents can interact with Twitter—posting tweets, reading mentions, or replying to DMs, depending on your configuration.
+
+---
+
+## 6. Debug Mode
+
+By default, posting to Twitter is **disabled** so you can see generated tweets in log files without actually publishing. This is by design so that you can test AI output before using real APIs:
+
+- **Log-Only Mode**
+ The system logs tweets to a `yaml` file, typically located at `configs/agent_folder/agentName_post_log.yaml`.
+- **Trial Run**
+ Check this file to verify the AI is generating appropriate, non-repetitive content.
+- **Switch to Live**
+ Once you’re satisfied, enable `twitter_live = True` to start posting live.
+
+---
+
+## 7. Processing Speed & Wait Times
+
+Depending on your prompt complexity, **ARAI AI** may take a few minutes to generate a response—especially if the agent is creating **long-form or multiple pieces** of content. The system is waiting for the LLM (e.g., Google Gemini) to process and return a detailed answer.
+
+- **CLI Feedback**
+ Keep an eye on your command-line interface (CLI). You’ll see logging messages that indicate whether the agent is still running or if an error occurs.
+- **Batch Generation**
+ If you’re generating content in batches (e.g., multiple tweets or entire episodes), expect longer wait times as the AI compiles all required context.
+- **Crash Handling**
+ If the CLI exits unexpectedly or logs an error, it’s likely that the model or process has crashed. You can check the logs for details and rerun once you’ve addressed the issue.
+
+_Reminder_: The more context (characters, episodes, story arcs) we include, the more time the model may need to process your request.
+
+---
+
+## 8. Next Steps
+
+- **Using ARAI with Other Models**: Check out the [API Reference Documentation](api/main.md) for integrating additional LLMs like OpenAI or Anthropic.
+- **Setting Up Additional Connectors**: See our [How-To Guides](how-to-guides.md) for adding Discord, Slack, or Telegram connectors.
+- **Managing Prompts & Templates**: Explore the [Prompt Reference](yaml/prompts/prompt_chaining.md) for advanced usage of prompt chaining and template customization.
+
+---
+
+## 9. Troubleshooting
+
+- **Conda Environment Not Found**
+ Ensure you spelled the environment name correctly or re-run `conda activate arai_ai_agents`.
+
+- **Credential Errors**
+ Double-check environment variables are set in your `.env` or system variables. Make sure you restart the shell if you updated `.env`.
+
+- **Authentication Failures**
+ Validate your **Google** or **Twitter** keys/tokens in their respective developer dashboards.
+
+If you run into any issues, feel free to open an [Issue](https://github.com/arai-ai/arai_ai_agents/issues).
+
+---
+
+**That’s it!** You’ve now set up your environment, installed ARAI AI Agents, obtained the necessary API keys, and authenticated your Twitter account. Remember that **complex or large-scale AI tasks** may take a bit longer to generate responses, so don’t worry if you see some delay—it’s just the model crafting detailed content.
+
+**Happy building with ARAI AI Agents!**
diff --git a/docs/index.md b/docs/index.md
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--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1,47 @@
+# ARAI AI Agents
+
+Welcome to the **ARAI AI Agents** documentation site! ARAI (Autonomous Responsive Intelligent Agent) is an advanced AI agent framework designed to create and manage autonomous AI agents. It provides a flexible and powerful system for building, deploying, and orchestrating AI agents for various applications.
+
+---
+
+## Documentation Structure
+
+We follow the [Diátaxis documentation framework](https://diataxis.fr/) to organize content into four main sections:
+
+1. [Tutorials](tutorials.md) – Step-by-step guides to get started with ARAI
+2. [How-To Guides](how-to-guides.md) – Practical guides for specific ARAI tasks
+3. [LLMS Explained](about-llms.md) – Conceptual guides about LLMs and how they work
+4. [ARAI AI Agents](explanation.md) – Conceptual guides about ARAI’s architecture and design
+5. [Resources](resources.md) – Resources for learning how to program AI agents
+6. [API Reference](api/main.md) – Technical reference for ARAI’s APIs and components
+7. [Prompt Reference](yaml/prompts/prompt_chaining.md) – Technical reference for ARAI’s prompts and templates
+
+For **code examples**, **API references**, and **prompt references**, refer to the dedicated pages:
+
+- [Code Examples](code.md)
+- [API Reference](api/main.md)
+- [Prompt Reference](yaml/prompts/prompt_chaining.md)
+
+Quickly find what you’re looking for by using the navigation links on top or in the sidebar.
+
+---
+
+## Project Overview
+
+ARAI simplifies creating and managing AI agents by providing:
+
+> 🚀 **Multiple AI Models**: Connect to OpenAI, Anthropic, or other LLMs.
+> ⚙️ **Modular Architecture**: Plug and play new connectors (Discord, Twitter, Telegram, etc.) with minimal effort.
+> 🔗 **Prompt Chaining**: Build, modify, and chain prompts to accomplish complex tasks.
+> 🧠 **Memory & Templates**: Store persistent context and quickly adapt to different use cases. [WIP]
+> 📊 **CLI**: Monitor and manage agent interactions in real-time.
+
+---
+
+## Acknowledgements
+
+This project builds upon various open-source technologies and research in the field of artificial intelligence and autonomous agents. Special thanks to all contributors and the broader AI research community for their ongoing work in advancing agent-based systems.
+
+---
+
+> **Get Started** by checking out the [Tutorials](tutorials.md) or explore the [How-To Guides](how-to-guides.md) for more specific tasks!
diff --git a/docs/resources.md b/docs/resources.md
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--- /dev/null
+++ b/docs/resources.md
@@ -0,0 +1,178 @@
+# AI Starter Resource Guide
+
+Welcome to the **AI Starter Resource Guide**! This document provides a curated set of resources to help you begin (or continue) your journey into artificial intelligence (AI). It covers everything from **Python** programming and environment management to popular **integrated development environments (IDEs)** and AI-specific tools.
+
+---
+
+## Table of Contents
+
+1. [Python Basics](#1-python-basics)
+2. [Conda for Environment Management](#2-conda-for-environment-management)
+3. [Code Editors & IDEs](#3-code-editors--ides)
+ - [Visual Studio Code](#visual-studio-code)
+ - [Cursor AI](#cursor-ai)
+4. [Foundational AI Resources](#4-foundational-ai-resources)
+5. [Machine Learning & Deep Learning Frameworks](#5-machine-learning--deep-learning-frameworks)
+6. [Online Courses & Tutorials](#6-online-courses--tutorials)
+7. [Communities & Forums](#7-communities--forums)
+8. [Additional References](#8-additional-references)
+
+---
+
+## 1. Python Basics
+
+**Python** is the de facto language for AI. Here are a few beginner-friendly resources:
+
+- **Official Python Docs**
+ [https://docs.python.org/3/](https://docs.python.org/3/)
+ The official documentation for Python. Great place to reference built-in modules, syntax details, and best practices.
+
+- **Automate the Boring Stuff with Python**
+ [https://automatetheboringstuff.com/](https://automatetheboringstuff.com/)
+ A free online book that introduces Python through practical tasks and examples.
+
+- **Python Crash Course** by Eric Matthes
+ [Amazon Link](https://www.amazon.com/Python-Crash-Course-2nd-Edition/dp/1593279280) (not free, but highly recommended).
+ Teaches programming concepts, projects, and fundamental Python skills.
+
+---
+
+## 2. Conda for Environment Management
+
+**Conda** is a widely-used tool for managing virtual environments, especially useful when juggling multiple data science projects or conflicting library versions.
+
+- **Installing Miniconda or Anaconda**
+ [https://docs.conda.io/en/latest/miniconda.html](https://docs.conda.io/en/latest/miniconda.html)
+ Miniconda is a minimal environment, while Anaconda is a more extensive distribution including many data science packages.
+
+- **Conda Cheat Sheet**
+ [https://docs.conda.io/projects/conda/en/latest/user-guide/cheatsheet/](https://docs.conda.io/projects/conda/en/latest/user-guide/cheatsheet/)
+ Quick reference for common commands (create envs, install packages, etc.).
+
+- **Practical Tips**
+ 1. `conda create --name myenv python=3.9` – Create a new environment.
+ 2. `conda activate myenv` – Activate the new environment.
+ 3. `conda install numpy` – Install a package into the active environment.
+
+---
+
+## 3. Code Editors & IDEs
+
+A comfortable coding environment makes learning AI more enjoyable and productive.
+
+### Visual Studio Code
+
+- **VS Code**
+ [https://code.visualstudio.com/](https://code.visualstudio.com/)
+ A free, lightweight yet powerful editor with a robust extension ecosystem.
+
+- **Python Extension**
+ [https://marketplace.visualstudio.com/items?itemName=ms-python.python](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
+ Adds support for Python syntax, IntelliSense, debugging, linting, and more.
+
+- **Remote Development**
+ [https://code.visualstudio.com/docs/remote/remote-overview](https://code.visualstudio.com/docs/remote/remote-overview)
+ Work in containers, WSL, or remote machines — useful for data-intensive AI projects.
+
+### Cursor AI
+
+- **Cursor AI**
+ [https://www.cursor.so/](https://www.cursor.so/)
+ A specialized code editor powered by AI. Cursor AI can provide in-editor suggestions, code completions, and debugging help tailored for data science and machine learning code.
+
+- **Setup & Documentation**
+ The site offers guides on how to integrate AI-based coding assistance into your workflow.
+
+---
+
+## 4. Foundational AI Resources
+
+- **Andrew Ng’s “AI Transformation Playbook”**
+ [https://landing.ai/ai-transformation-playbook/](https://landing.ai/ai-transformation-playbook/)
+ A high-level overview of how companies adopt AI, also helpful to understand AI project lifecycles.
+
+- **Stanford’s CS229: Machine Learning**
+ [https://cs229.stanford.edu/](https://cs229.stanford.edu/)
+ Lecture materials, notes, and assignments from one of the most popular ML courses.
+
+---
+
+## 5. Machine Learning & Deep Learning Frameworks
+
+- **TensorFlow**
+ [https://www.tensorflow.org/](https://www.tensorflow.org/)
+ An end-to-end open-source platform for machine learning from Google. Popular for deep learning, also supports a wide range of machine learning tasks.
+
+- **PyTorch**
+ [https://pytorch.org/](https://pytorch.org/)
+ A popular framework by Meta (Facebook). Known for its dynamic computation graph and ease of experimentation. Favored by many researchers.
+
+- **scikit-learn**
+ [https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)
+ Perfect for traditional machine learning algorithms. Great documentation and easy to integrate into Python projects.
+
+- **Hugging Face**
+ [https://huggingface.co/](https://huggingface.co/)
+ A platform & library for state-of-the-art NLP and other ML tasks. With Transformers, you can quickly experiment with large language models (LLMs).
+
+---
+
+## 6. Online Courses & Tutorials
+
+- **Coursera**
+
+ - “Machine Learning” by Andrew Ng (classic intro course).
+ - “Deep Learning Specialization” by Andrew Ng.
+
+- **fast.ai**
+ [https://www.fast.ai/](https://www.fast.ai/)
+ Practical deep learning courses that don’t require advanced math prerequisites.
+
+- **Kaggle**
+ [https://www.kaggle.com/](https://www.kaggle.com/)
+ Hosts ML competitions. Offers free data sets and interactive tutorials (Kaggle Learn). Great for hands-on practice and portfolio building.
+
+---
+
+## 7. Communities & Forums
+
+- **Reddit /r/MachineLearning**
+ [https://www.reddit.com/r/MachineLearning/](https://www.reddit.com/r/MachineLearning/)
+ News, papers, and discussions on ML.
+
+- **Stack Overflow**
+ [https://stackoverflow.com/](https://stackoverflow.com/)
+ Essential Q&A site for programming issues.
+
+- **Hugging Face Forums**
+ [https://discuss.huggingface.co/](https://discuss.huggingface.co/)
+ Focused on Transformers, NLP, and specialized model usage.
+
+- **Discord Communities**
+ Many open-source ML or AI project communities have active Discord servers. For instance, Hugging Face, PyTorch, etc.
+
+---
+
+## 8. Additional References
+
+- **Papers with Code**
+ [https://paperswithcode.com/](https://paperswithcode.com/)
+ Tracks the latest in AI research along with code implementations.
+
+- **Arxiv**
+ [https://arxiv.org/](https://arxiv.org/)
+ The go-to place for preprints on ML, NLP, CV (computer vision), and other AI research fields.
+
+- **YouTube Channels**
+ - **3Blue1Brown**: Explains math and ML concepts visually.
+ - **Two Minute Papers**: Summaries of recent AI papers.
+
+---
+
+## Final Notes
+
+- **Practice**: The best way to learn AI is by doing. Try small projects on Kaggle or your own dataset.
+- **Stay Updated**: AI research moves quickly. Follow conferences like NeurIPS, ICLR, ICML, and domain-specific communities.
+- **Experiment**: Tools like **VS Code** or **Cursor AI** can speed up your development and debugging. Combine them with **Conda** to keep your environment clean.
+
+**We wish you the best on your AI journey!** Remember that the AI field is broad and constantly evolving—there’s always something new to learn or try. Happy coding!
diff --git a/docs/tutorials.md b/docs/tutorials.md
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--- /dev/null
+++ b/docs/tutorials.md
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+# Getting Started with the Gemini API using Python
+
+This tutorial will guide you through the process of setting up and making your first request to the Gemini API using the Google's Generative AI Python library.
+
+---
+
+## Table of Contents
+
+1. [Prerequisites](#prerequisites)
+2. [Configure Your API Key](#configure-your-api-key)
+3. [Initialize the Model](#initialize-the-model)
+4. [Make Your First Request](#make-your-first-request)
+5. [Using the Chat Interface](#using-the-chat-interface)
+6. [Exploring Further](#exploring-further)
+7. [Troubleshooting](#troubleshooting)
+8. [Conclusion](#conclusion)
+
+---
+
+## Prerequisites
+
+Before you begin, make sure you have the following:
+
+- **Python 3.11+ installed**
+ You can check your Python version by running:
+
+ ```bash
+ python --version
+ ```
+
+ or:
+
+ ```bash
+ python3 --version
+ ```
+
+- **A Google Cloud Project**
+ If you don't have one, create a new project in the [Google Cloud Console](https://console.cloud.google.com/).
+
+- **A Gemini API Key**
+
+ 1. Go to [Google AI Studio](https://ai.google.dev/).
+ 2. Click on **"Get API Key"**.
+ 3. Select your Google Cloud Project where you want to enable the API.
+ 4. Click on **"Create API Key"**. Copy this key; you'll need it later.
+
+- **The `google-generativeai` Python library**
+ You can install it using pip:
+ ```bash
+ pip install google-generativeai
+ ```
+
+---
+
+## Configure Your API Key
+
+The `google-generativeai` library needs your API key to authenticate your requests to the Gemini API. There are a couple of ways to provide it:
+
+### Method 1: Environment Variable (Recommended)
+
+1. Set an environment variable named `GOOGLE_API_KEY` with your API key as the value.
+
+ - **Linux/macOS:**
+
+ ```bash
+ export GOOGLE_API_KEY="YOUR_API_KEY"
+ ```
+
+ - **Windows:**
+ ```bash
+ setx GOOGLE_API_KEY "YOUR_API_KEY"
+ ```
+ _(You might need to restart your console or IDE for it to take effect.)_
+
+2. Then in your Python code, configure like this:
+
+ ```python
+ import google.generativeai as genai
+ import os
+
+ genai.configure(api_key=os.environ.get("GOOGLE_API_KEY"))
+ ```
+
+### Method 2: Directly in Your Code (Less Secure)
+
+You can set your API key directly in your Python code. **However, this is less secure, especially if you are sharing or versioning your code.**
+
+```python
+import google.generativeai as genai
+
+genai.configure(api_key="YOUR_API_KEY") # Replace "YOUR_API_KEY" with your actual key
+```
+
+---
+
+## Initialize the Model
+
+Now, let's initialize the `GenerativeModel` with the `gemini-pro` model:
+
+```python
+import google.generativeai as genai
+
+# ... API key configuration (see "Configure Your API Key") ...
+
+model = genai.GenerativeModel("gemini-pro")
+```
+
+---
+
+## Make Your First Request
+
+Let's send a simple prompt to the Gemini API and get a response:
+
+```python
+import google.generativeai as genai
+
+# ... API key configuration and model initialization (see "Configure Your API Key" & "Initialize the Model") ...
+
+prompt = "What is the capital of France?"
+
+response = model.generate_content(prompt)
+
+print(response.text)
+```
+
+**Expected Output:**
+
+```
+The capital of France is Paris.
+```
+
+_(Or a similar, more elaborate response.)_
+
+**Explanation**:
+
+- **prompt**: This variable holds the text prompt you are sending to the model.
+- **model.generate_content(prompt)**: This calls the API, sending the prompt and receiving the generated content.
+- **response.text**: This accesses the text part of the response from the model.
+
+---
+
+## Using the Chat Interface
+
+The Gemini API also supports a **chat interface** where you can have back-and-forth conversations.
+
+```python
+import google.generativeai as genai
+
+# ... API key configuration (see "Configure Your API Key") ...
+
+model = genai.GenerativeModel("gemini-pro")
+
+messages = []
+
+messages.append({
+ "role": "user",
+ "parts": ["What is the capital of France?"]
+})
+
+response = model.generate_content(messages)
+print(response.text)
+
+# Add model's response back into the conversation history
+messages.append({
+ "role": "model",
+ "parts": [response.text]
+})
+
+# User asks another question
+messages.append({
+ "role": "user",
+ "parts": ["And what is its population?"]
+})
+
+response = model.generate_content(messages)
+print(response.text)
+```
+
+### Explanation
+
+- **messages**: This list stores the history of your conversation.
+- **Adding messages**: Each item in `messages` is a dictionary with:
+ - **role**: Either `"user"` or `"model"`.
+ - **parts**: A list of strings (or other content parts) representing the message content.
+- **model.generate_content(messages)**: Takes the entire message history to provide context for the model.
+- **Appending the model's response**: To maintain conversation flow, you append the AI's response to `messages` so it “remembers” earlier turns.
+
+---
+
+## Exploring Further
+
+- **More Model Parameters**
+ Check out the Google AI for Developers documentation to learn about additional parameters (e.g., `temperature`, `top_k`, `top_p`) for controlling the model's generation.
+- **Safety Settings**
+ You can configure safety settings to control what type of content the model generates. See the **Safety Settings** documentation.
+
+- **Other Models**
+ Explore other available models, such as `gemini-pro-vision` for multimodal input (text + images).
+
+---
+
+## Troubleshooting
+
+- **API key not found error**
+ Make sure your API key is correctly configured as an environment variable or in your code.
+- **PermissionDenied error**
+ Verify that the API key is associated with a Google Cloud project that has the Gemini API enabled.
+- **Other errors**
+ Refer to the Gemini API documentation for more detailed error messages and troubleshooting steps.
+
+---
+
+## Conclusion
+
+Congratulations! You've now successfully made your first request to the Gemini API and even tried out a simple conversation. This is just the beginning of what you can do with this powerful API. Explore the documentation and experiment with different prompts and model parameters to unlock the full potential of **Google's Generative AI** models.
+
+```
+
+```
diff --git a/docs/yaml/agent_template.yaml b/docs/yaml/agent_template.yaml
new file mode 100644
index 0000000..6eecd73
--- /dev/null
+++ b/docs/yaml/agent_template.yaml
@@ -0,0 +1,15 @@
+name:
+personality:
+communication_style:
+backstory:
+universe:
+topic_expertise:
+hashtags:
+emojis:
+model_type: "gemini"
+model_name: "gemini-exp-1206"
+memory_store: "gemini_chroma"
+connectors:
+ twitter: true
+ telegram: false
+ discord: false
diff --git a/docs/yaml/episode_template.yaml b/docs/yaml/episode_template.yaml
new file mode 100644
index 0000000..c2d439a
--- /dev/null
+++ b/docs/yaml/episode_template.yaml
@@ -0,0 +1,15 @@
+episode:
+ season_number:
+ episode_name:
+ episode_number:
+ episode_overview: |
+ episode_summary: |
+ episode_highlights: |
+ episode_posted: False
+ current_post_number: 0
+ posts:
+ - post_id:
+ post_number:
+ post_content: |
+ post_highlights: |
+ post_posted: False
diff --git a/docs/yaml/prompt_chaining.yaml b/docs/yaml/prompt_chaining.yaml
new file mode 100644
index 0000000..6bc9ddf
--- /dev/null
+++ b/docs/yaml/prompt_chaining.yaml
@@ -0,0 +1,181 @@
+prompt_1 (Character Sheet Creation): |
+ You are an expert in creative writing, character design, world-building, and marketing. You are tasked with developing a complete character profile for a new agent who will be featured in stories for a Twitter bot. These stories will be broken down into seasons, episodes, and individual tweets (scenes/posts).
+
+ Agent Development Task
+ Generate a comprehensive YAML file that defines this new agent, including their name, personality, communication style, topic of expertise, backstory, universe, relevant hashtags, and emojis.
+ Use the following concept if its not empty, otherwise create one.
+ - concept: {{ concept }}
+
+ Specific Instructions:
+
+ 1. Invent a Creative Name:
+ - If no specific name is provided, create a unique and fitting name for the agent based on the concept of the agent. The name should be suitable for a Twitter handle.
+ - Agent Name: {{ agent_name }}
+
+ 2. Define the Topic:
+ - If no specific topic is provided, default to "Crypto" or invent a creative topic that lends itself to episodic storytelling.
+ - Topic: {{ topic }}
+
+ 3. Develop Personality and Style:
+ - Create a detailed personality for the agent.
+ - Define a clear communication style for the agent.
+ - Important: Use the YAML block scalar style with the `|` character to preserve newlines and formatting.
+ - Example:
+ ```yaml
+ personality: |
+ This is a multi-line
+ description of the agent's
+ personality.
+ ```
+ - Personality: {{ personality }}
+ - Communication Style: {{ style }}
+
+ 4. Craft a Backstory:
+ - Invent a compelling and original backstory for the agent that explains their motivations, skills, and current situation. This backstory should be suitable for unfolding gradually over multiple story arcs.
+ - Important: Use the YAML block scalar style with the `|` character to preserve newlines and formatting.
+ - Example:
+ ```yaml
+ backstory: |
+ This is a longer, multi-line
+ backstory for the agent.
+ It can span several lines.
+ ```
+ - Backstory: {{ backstory }}
+
+ 5. Describe the Universe:
+ - Detail the world(s) or setting where the agent operates. Consider the current state of technology, the social and political landscape, major organizations or factions, and any unique elements relevant to the chosen topic.
+ - Use a narrative or bullet-point format within the YAML.
+ - Important: Use the YAML block scalar style with the `|` character to preserve newlines and formatting.
+ - Example:
+ ```yaml
+ universe: |
+ This is a description
+ of the agent's universe.
+ It can have multiple lines
+ and paragraphs.
+ ```
+ - Universe: {{ universe }}
+
+ 6. Generate Marketing Elements:
+ - Create a list of relevant hashtags that will be used for social media promotion. Include hashtags related to the agent's name, topic, and genre.
+ - Important: Output the hashtags as a YAML array using block style. This means each hashtag should be on a new line, preceded by a hyphen and a space and placed inside double quotes.
+ - Example:
+ ```yaml
+ hashtags:
+ - "#example1"
+ - "#example2"
+ - "#longerExampleHashtag"
+ ```
+ - Create a list of relevant emojis that can be used in tweets to add visual interest and convey meaning.
+ - Important: Output the emojis as a YAML array using block style. Each emoji should be on a new line, preceded by a hyphen and a space and placed inside double quotes.
+ - Example:
+ ```yaml
+ emojis:
+ - "🐳"
+ - "🌊"
+ - "🚀"
+ ```
+ - Hashtags: {{ hashtags }}
+ - Emojis: {{ emojis }}
+
+ Output Requirements:
+ - Only output valid YAML. Do not include any text outside of the YAML structure.
+ - Output a single, complete YAML file that includes all the fields mentioned above (name, personality, communication_style, topic, backstory, universe, hashtags, emojis).
+ - Adhere to the specified answer lengths for each field (short for personality and communication style, long for backstory and universe).
+
+ Output exactly as the yaml file:
+ ```yaml
+ {{ agent_yaml }}
+ ```
+
+prompt_2 (Season Creation): |
+ You are an expert in creative writing, season/episode design, and structured storytelling.
+ Your goal is to create a new season for {{ agent_name }}, who was previously defined in the following agent YAML:
+
+ {{ agent_yaml }}
+
+ Previous season:
+ - This provides the previous season details, including description and highlights for the season. Ignore if none is provided as this means we are creating season 1, so there is no previous season.
+ {{ previous_season }}
+
+ ---
+ ## Task: Season Creation
+
+ Generate a single valid YAML file that defines a new season for this agent. The season should include:
+
+ - Season Name: If none is provided, create a unique and fitting name based on {{ agent_name }} and the agent’s YAML.
+ - Use something that can also work as a Twitter hashtag.
+ - Season Name: {{ season_name }}
+
+ - Season Number: If none is provided, default to 1.
+ - Season Number: {{ season_number }}
+
+ - Season Description: If none is provided, create a unique description based on {{ agent_name }}’s YAML.
+ - Important: Use the YAML block scalar style with the `|` character to preserve newlines and formatting.
+ - Season Description: {{ season_description }}
+
+ - Season Highlights: If none is provided, invent highlights relevant to the agent’s concept or storyline.
+ - Important: Use the YAML block scalar style with the `|` character to preserve newlines and formatting.
+ - Season Highlights: {{ season_highlights }}
+
+ - Season Summary: If none is provided, create a concise summary that captures the essence of this season.
+ - Important: Use the YAML block scalar style with the `|` character to preserve newlines and formatting.
+ - Season Summary: {{ season_summary }}
+
+ - Episodes: We want **28 episodes** total (one for each day over 4 weeks).
+ - If none are provided, create 28 unique episodes.
+ - Each episode entry should have:
+ - `episode_id`
+ - `episode_name`
+ - `episode_number`
+ - `episode_description` (Use `|` for multi-line)
+ - `episode_highlights` (Use `|` for multi-line)
+ - `episode_posted` (default `False`)
+ - `episode_summary` (Use `|` for multi-line)
+
+ Output Requirements:
+ - Only output valid YAML. Do not include any text outside of the YAML structure.
+ - Output a single, complete YAML file that includes all the fields mentioned above (season_name, season_number, season_description, season_highlights, season_summary, episodes).
+
+ Output exactly as the yaml file:
+ ```yaml
+ {{ season_yaml }}
+
+prompt_3 (Episode Posts Creation): |
+ You are an expert in creative writing, social media engagement, and character voice development. Your task is to generate {{ number_of_posts }} short "updates" (tweets/posts) for the agent {{ agent_name }} in the style of Twitter posts. These posts are for season {{ season_number }}, episode {{ episode_number }}.
+
+ Agent and Context:
+
+ * Agent YAML: {{ agent_yaml }} (This provides the agent's personality, backstory, etc.)
+ * Season YAML: {{ season_yaml }} (This provides the season details, including description and highlights for the season)
+ * Episode YAML: {{ episode_yaml }} (This provides the episode details, including description and highlights for the episode)
+ * Previous Episode YAML: {{ previous_episode }} (This provides the previous episode details, including description and highlights for the episode. Ignore if none is provided as this means we are creating episode 1, so there is no previous episode)
+ **Post Requirements:**
+
+ Each post should:
+
+ * Reflect Agent's Personality: Be consistent with {{ agent_name }}'s personality, tone, and communication style as defined in the agent YAML.
+ * Relate to Setting/Technology: Reference elements of the setting, technology, or other relevant details from the agent's universe as defined in the agent YAML.
+ * Incorporate Episode Context: Align with the season overview and specific episode overview provided in the season YAML.
+ * Highlight Observations: Center around misunderstandings, comedic observations, or serious reflections relevant to the episode's theme.
+ * Consider Emojis: Optionally use emojis from this set: {{ agent_yaml }} or use any emojis that are relevant to the episode's theme.
+ * Consider Hashtags: Optionally use hashtags from this set: {{ agent_yaml }} or use any hashtags that are relevant to the episode's theme.
+ * Length Constraint: Each post must be under {{ post_length }} characters.
+ * Sign-Off: End each post with the sign-off: "#{{ agent_name }}"
+
+ **YAML Output Format:**
+
+ Output a valid YAML file containing a list of posts under the `posts` key. Each post should be a separate entry in the list and include the following:
+
+ * `post_id`: A unique identifier for the post (e.g., s1\_e1\_post1, s1\_e1\_post2).
+ * `post_number`: The ordinal number of the post within the episode (e.g., 1, 2, 3).
+ * `post_content`: The actual text of the post, formatted using the YAML block scalar style (`|`) to preserve newlines, do not use '-' for the first line.
+ * `post_highlights`: A brief, one-sentence description of the key takeaway or event in the post (using `|` for multi-line), do not use '-' for the first line.
+ * `post_posted`: Set to `False` by default.
+
+ **Output Requirements:**
+ - **Only output valid YAML.** Do not include any text outside of the YAML structure.
+ - **Output the yaml file:**
+ ```yaml
+ {{ episode_yaml }}
+ ```
diff --git a/docs/yaml/prompts/prompt_chaining.md b/docs/yaml/prompts/prompt_chaining.md
new file mode 100644
index 0000000..1291699
--- /dev/null
+++ b/docs/yaml/prompts/prompt_chaining.md
@@ -0,0 +1,5 @@
+# Prompt Chaining
+
+```yaml linenums='1'
+{ % include "yaml/prompt_chaining.yaml" % }
+```
diff --git a/docs/yaml/season_template.yaml b/docs/yaml/season_template.yaml
new file mode 100644
index 0000000..d2dc562
--- /dev/null
+++ b/docs/yaml/season_template.yaml
@@ -0,0 +1,15 @@
+season:
+ season_name:
+ season_number:
+ season_description: |
+ season_highlights: |
+ season_summary: |
+ season_posted: False
+ current_episode_number: 0
+ episodes:
+ episode_name:
+ episode_number:
+ episode_description: |
+ episode_highlights: |
+ episode_summary: |
+ episode_posted: False
diff --git a/docs/yaml/templates/agent_template.md b/docs/yaml/templates/agent_template.md
new file mode 100644
index 0000000..fccf337
--- /dev/null
+++ b/docs/yaml/templates/agent_template.md
@@ -0,0 +1,5 @@
+# Agent Template
+
+```yaml linenums='1'
+{ % include "yaml/agent_template.yaml" % }
+```
diff --git a/docs/yaml/templates/episode_template.md b/docs/yaml/templates/episode_template.md
new file mode 100644
index 0000000..95733d6
--- /dev/null
+++ b/docs/yaml/templates/episode_template.md
@@ -0,0 +1,5 @@
+# Episode Template
+
+```yaml linenums='1'
+{ % include "yaml/episode_template.yaml" % }
+```
diff --git a/docs/yaml/templates/season_template.md b/docs/yaml/templates/season_template.md
new file mode 100644
index 0000000..f66c6da
--- /dev/null
+++ b/docs/yaml/templates/season_template.md
@@ -0,0 +1,5 @@
+# Season Template
+
+```yaml linenums='1'
+{ % include "yaml/season_template.yaml" % }
+```
diff --git a/docs/yaml/templates/tracker_template.md b/docs/yaml/templates/tracker_template.md
new file mode 100644
index 0000000..f10ca53
--- /dev/null
+++ b/docs/yaml/templates/tracker_template.md
@@ -0,0 +1,5 @@
+# Tracker Template
+
+```yaml linenums='1'
+{ % include "yaml/tracker_template.yaml" % }
+```
diff --git a/docs/yaml/tracker_template.yaml b/docs/yaml/tracker_template.yaml
new file mode 100644
index 0000000..e7470b2
--- /dev/null
+++ b/docs/yaml/tracker_template.yaml
@@ -0,0 +1,4 @@
+current_season_number: 1
+current_episode_number: 1
+current_post_number: 0
+post_every_x_minutes: 60
diff --git a/mkdocs.yml b/mkdocs.yml
new file mode 100644
index 0000000..e9fb8cd
--- /dev/null
+++ b/mkdocs.yml
@@ -0,0 +1,102 @@
+site_name: ARAI AI Agents
+site_description: Documentation for ARAI AI Agents
+site_author: TheBlockRhino
+
+# The docs directory (where your .md files live)
+docs_dir: docs
+
+# Build directory (where MkDocs will output the static site)
+site_dir: site
+
+theme:
+ name: material
+ features:
+ - navigation.tabs
+ - navigation.sections
+ - toc.integrate
+ - navigation.top
+ - navigation.expand
+ - search.suggest
+ - search.highlight
+ - content.tabs.link
+ - content.code.annotation
+ - content.code.copy
+ language: en
+ palette:
+ - scheme: slate
+ toggle:
+ icon: material/toggle-switch
+ name: Switch to light mode
+ primary: teal
+ accent: lime
+ - scheme: default
+ toggle:
+ icon: material/toggle-switch-off-outline
+ name: Switch to dark mode
+ primary: teal
+ accent: purple
+ logo: assets/images/AVA_LOG_BASE_WHITE.png
+
+extra:
+ social:
+ - icon: fontawesome/brands/github-alt
+ link: https://github.com/ARAIDevHub/arai_ai_agents
+ - icon: fontawesome/brands/twitter
+ link: https://twitter.com/ARAI_AI_AGENTS
+
+markdown_extensions:
+ - pymdownx.highlight:
+ anchor_linenums: true
+ - pymdownx.inlinehilite
+ - pymdownx.snippets
+ - admonition
+ - pymdownx.arithmatex:
+ generic: true
+ - footnotes
+ - pymdownx.details
+ - pymdownx.superfences
+ - pymdownx.mark
+ - attr_list
+ - pymdownx.emoji:
+ emoji_index: !!python/name:material.extensions.emoji.twemoji
+ emoji_generator: !!python/name:material.extensions.emoji.to_svg
+
+plugins:
+ - search
+ - gen-files:
+ scripts:
+ - scripts/gen_ref_py_pages.py
+ - scripts/gen_ref_yaml_pages.py
+ - literate-nav:
+ nav_file: SUMMARY.md
+ - include-markdown
+ - section-index
+ - mkdocstrings:
+ handlers:
+ python:
+ paths: [.]
+ options:
+ show_source: true
+ show_root_heading: true
+ docstring_style: google
+
+nav:
+ - Home: index.md
+ - ARAI CLI Usage Guide: cli-guide.md
+ - How-to Guides: how-to-guides.md
+ - AI Tutorial: tutorials.md
+ - LLMS Explained: about-llms.md
+ - ARAI AI Agents: explanation.md
+ - Resources: resources.md
+ - API Reference: api/
+ - Prompt Reference:
+ - Prompts:
+ - Prompt Chaining: yaml/prompts/prompt_chaining.md
+ - Templates:
+ - Agent Template: yaml/templates/agent_template.md
+ - Episode Template: yaml/templates/episode_template.md
+ - Season Template: yaml/templates/season_template.md
+ - Tracker Template: yaml/templates/tracker_template.md
+
+copyright: |
+ © 2024 <a href="https://github.com/ARAIDevHub" target="_blank" rel="noopener">ARAIDevHub</a>
diff --git a/packages/twitter_playwright/twitter_api_free_connector.py b/packages/twitter_playwright/twitter_api_free_connector.py
new file mode 100644
index 0000000..b1e8400
--- /dev/null
+++ b/packages/twitter_playwright/twitter_api_free_connector.py
@@ -0,0 +1,113 @@
+import os
+from dotenv import load_dotenv
+from playwright.sync_api import sync_playwright
+
+load_dotenv()
+
+# Add this at the top level of the file
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+STATE_FILE = os.path.join(SCRIPT_DIR, "state.json")
+
+def login_and_save_state(username, password, phone_or_username, storage_path=STATE_FILE):
+ with sync_playwright() as p:
+ browser = p.chromium.launch(headless=False, slow_mo=1000) # delay for 1 second for all actions
+ context = browser.new_context()
+ page = context.new_page()
+
+ # 1) Navigate to login
+ page.goto("https://x.com/login")
+ page.wait_for_timeout(1000) # optional
+
+ # 2) Fill in username & password (this is just an example, update selectors as needed)
+ page.wait_for_selector('input[name="text"]', timeout=10000)
+ page.fill('input[name="text"]', username)
+ # page.click('div:has-text("Next")')
+ page.keyboard.press("Enter")
+
+ # Unusal activity
+ try:
+ # Attempt to find the suspicious activity input
+ page.wait_for_selector('input[data-testid="ocfEnterTextTextInput"]', timeout=3000)
+ print("[INFO] Unusual login activity popup detected.")
+
+ # Fill it with phone/username
+ page.fill('input[data-testid="ocfEnterTextTextInput"]', phone_or_username)
+ # page.click('div:has-text("Next")') # Or the appropriate button text
+ page.keyboard.press("Enter")
+ page.wait_for_load_state("networkidle")
+ print("[INFO] Challenge response submitted.")
+
+ except TimeoutError:
+ print("[INFO] No suspicious login activity popup. Continuing normal flow.")
+
+ # Password
+ page.wait_for_selector('input[name="password"]', timeout=10000)
+ page.fill('input[name="password"]', password)
+ # page.click('div:has-text("Log in")')
+ page.keyboard.press("Enter")
+
+ # 3) Wait until the user is on the home feed
+ page.wait_for_url(lambda url: "home" in url, timeout=15000)
+ print("[INFO] Logged in successfully (assuming no extra checks).")
+
+ # 4) Save the current browser context's storage state to a file
+ context.storage_state(path=storage_path)
+ print(f"[INFO] Storage state saved to {storage_path}.")
+
+ browser.close()
+
+def post_tweet_with_saved_state(tweet_text, storage_path=STATE_FILE):
+ with sync_playwright() as p:
+ # Create a new context with the previously saved state
+ browser = p.chromium.launch(headless=False)
+ context = browser.new_context(storage_state=storage_path)
+ page = context.new_page()
+
+ # Now page is already logged in if state.json is still valid
+ page.goto("https://x.com/home")
+ # Wait a bit for the home feed to render
+ page.wait_for_timeout(1000) # optional
+ # page.wait_for_load_state("networkidle")
+ print("[INFO] Checking if we are indeed logged in...")
+
+ # Post a tweet
+ tweet_box_selector = 'div[data-testid="tweetTextarea_0"]'
+ page.wait_for_selector(tweet_box_selector, timeout=10000)
+ page.fill(tweet_box_selector, tweet_text)
+
+ # post_button_selector = 'div[data-testid="tweetButtonInline"]'
+ post_button_selector = 'button[data-testid="tweetButtonInline"]'
+ # page.wait_for_selector(post_button_selector, state="visible", timeout=20000) # Wait for visible
+
+ page.wait_for_selector(post_button_selector, timeout=10000)
+ page.click(post_button_selector)
+ page.wait_for_timeout(3000)
+ print("[INFO] Tweet posted (assuming no errors).")
+
+ browser.close()
+
+
+def main(tweet_content):
+ # Test post
+ X_POST_TEXT = tweet_content
+
+ # check if state.json exist
+ if os.path.exists(STATE_FILE):
+ print(f"[INFO] state.json exists at {STATE_FILE}. Using existing state.")
+ else:
+ print(f"[INFO] state.json does not exist at {STATE_FILE}. Logging in and saving state.")
+
+ login_and_save_state(
+ username=os.getenv("X_USERNAME"),
+ password=os.getenv("X_PASSWORD"),
+ phone_or_username=os.getenv("X_PHONE_OR_USERNAME"),
+ storage_path=STATE_FILE
+ )
+
+ post_tweet_with_saved_state(
+ tweet_text=X_POST_TEXT,
+ storage_path=STATE_FILE
+ )
+
+if __name__ == "__main__":
+ main("Hello")
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..17e9445
Binary files /dev/null and b/requirements.txt differ
diff --git a/scripts/gen_ref_py_pages.py b/scripts/gen_ref_py_pages.py
new file mode 100644
index 0000000..60f88ea
--- /dev/null
+++ b/scripts/gen_ref_py_pages.py
@@ -0,0 +1,68 @@
+"""Generate the code reference pages and navigation."""
+from pathlib import Path
+import mkdocs_gen_files
+
+nav = mkdocs_gen_files.Nav()
+
+root = Path(__file__).parent.parent
+src = root / "arai_ai_agents"
+
+print(f"Looking for Python files in: {src}")
+
+# First, process __init__.py files to create module indexes
+for path in sorted(src.rglob("__init__.py")):
+ module_path = path.relative_to(src).parent
+ doc_path = path.relative_to(src).parent / "index.md"
+ full_doc_path = Path("api", doc_path)
+
+ parts = tuple(module_path.parts)
+
+ if parts:
+ # Create the module index page
+ with mkdocs_gen_files.open(full_doc_path, "w") as fd:
+ ident = "arai_ai_agents"
+ if parts:
+ ident += "." + ".".join(parts)
+ print(f"Writing module index: {ident}")
+ fd.write(f"# {parts[-1] if parts else 'arai_ai_agents'}\n\n")
+ fd.write(f"::: {ident}")
+
+ mkdocs_gen_files.set_edit_path(full_doc_path, path.relative_to(root))
+
+# Then process other Python files
+for path in sorted(src.rglob("*.py")):
+ module_path = path.relative_to(src).with_suffix("")
+ doc_path = path.relative_to(src).with_suffix(".md")
+ full_doc_path = Path("api", doc_path)
+
+ parts = tuple(module_path.parts)
+
+ if parts[-1] == "__init__":
+ continue # Skip __init__.py files as they're already processed
+ elif parts[-1] == "__main__":
+ continue
+ elif "_pycache_" in str(path):
+ continue
+
+ # Use nested navigation to ensure collapsible sections
+ if len(parts) > 1:
+ nav[parts[:-1] + (parts[-1],)] = doc_path.as_posix()
+ else:
+ nav[parts] = doc_path.as_posix()
+
+ print(f"Added to nav: {parts} -> {doc_path.as_posix()}")
+
+ with mkdocs_gen_files.open(full_doc_path, "w") as fd:
+ ident = "arai_ai_agents." + ".".join(parts)
+ print(f"Writing docs for: {ident}")
+ fd.write(f"# {parts[-1]}\n\n") # Add a title
+ fd.write(f"::: {ident}")
+
+ mkdocs_gen_files.set_edit_path(full_doc_path, path.relative_to(root))
+
+# Write the navigation file
+summary_file = Path("api/SUMMARY.md")
+with mkdocs_gen_files.open(summary_file, "w") as nav_file:
+ for line in nav.build_literate_nav():
+ # Convert *-based bullet points to `-` for MkDocs
+ nav_file.write(line.replace("*", "-"))
diff --git a/scripts/gen_ref_yaml_pages.py b/scripts/gen_ref_yaml_pages.py
new file mode 100644
index 0000000..adedff2
--- /dev/null
+++ b/scripts/gen_ref_yaml_pages.py
@@ -0,0 +1,53 @@
+import shutil
+from pathlib import Path
+
+def create_markdown_for_yaml(yaml_path, output_file):
+ """
+ Generate markdown content for a given YAML file.
+ """
+ # Copy the YAML file to the docs/yaml directory
+ target_yaml_dir = root_dir / "docs" / "yaml"
+ target_yaml_dir.mkdir(parents=True, exist_ok=True)
+ target_yaml_path = target_yaml_dir / yaml_path.name
+ shutil.copy(yaml_path, target_yaml_path)
+
+ # Path relative to the docs directory
+ relative_path = target_yaml_path.relative_to(root_dir / "docs").as_posix()
+
+ with open(output_file, "w") as md_file:
+ md_file.write(f"# {yaml_path.stem.replace('_', ' ').title()}\n\n")
+ md_file.write(f"```yaml linenums='1'\n")
+ md_file.write(f"{{% include \"{relative_path}\" %}}\n")
+ md_file.write(f"```\n")
+ print(f"Generated markdown: {output_file} and copied YAML to {target_yaml_path}")
+
+def process_yaml_files():
+ """
+ Automatically find YAML files and create markdown documentation.
+ """
+ yaml_source = root_dir / "arai_ai_agents" # YAML source folder
+ output_dir = root_dir / "docs" / "yaml"
+
+ print(f"Looking for YAML files in: {yaml_source}")
+ output_dir.mkdir(parents=True, exist_ok=True)
+
+ for yaml_file in yaml_source.rglob("*.yaml"):
+ # Skip the 'configs' and 'responses' directories and their subfolders
+ if any(skip_dir in yaml_file.parts for skip_dir in ["configs", "responses"]):
+ print(f"Skipping file in excluded folder: {yaml_file}")
+ continue
+
+ print(f"Found YAML file: {yaml_file}")
+
+ # Create output path based on the YAML file's relative structure
+ relative_path = yaml_file.relative_to(yaml_source)
+ output_path = output_dir / relative_path.with_suffix(".md")
+ output_path.parent.mkdir(parents=True, exist_ok=True)
+
+ # Generate markdown for the YAML file
+ create_markdown_for_yaml(yaml_file, output_path)
+
+
+if __name__ == "__main__":
+ root_dir = Path(__file__).parent.parent # Define the root of the project
+ process_yaml_files()
diff --git a/server/__init__.py b/server/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/server/api.py b/server/api.py
new file mode 100644
index 0000000..122cbd4
--- /dev/null
+++ b/server/api.py
@@ -0,0 +1,527 @@
+"""
+API Server Module
+
+This module implements the Flask REST API server that handles agent creation,
+chat interactions, content generation and other core functionality.
+
+Key Features:
+- Agent creation and management
+- Chat interactions with agents
+- Season and episode content generation
+- Chat history tracking
+"""
+
+from flask import Flask, jsonify, request, make_response
+from flask_cors import CORS
+from dotenv import load_dotenv
+import os
+import json
+import glob
+from pprint import pprint
+from prompt_chaining.step_1_create_agent import create_agent as generateAgent
+from models.openai_model import OpenAIModel
+from prompt_chaining.step_5_agent_chat import agent_chat
+from prompt_chaining.step_2_create_content import create_seasons_and_episodes
+from prompt_chaining.step_3_create_posts import create_episode_posts
+from utils.post_manager import PostManager
+from utils.scheduler import AgentScheduler
+
+
+# Load environment variables
+load_dotenv()
+
+#Global Post Manager - Will be instantiated when a user logs in to Twitter
+global post_manager_twitter
+
+# Initialize Flask app with 20MB max content size
+app = Flask(__name__)
+app.config['MAX_CONTENT_LENGTH'] = 20 * 1024 * 1024 # 20 MB
+
+# Configure CORS for frontend origin
+CORS(app, resources={r"/api/*": {
+ "origins": ["https://g0c0848ggco0cgw4gss8gws0.dev3vds1.link", "http://localhost:5173"],
+ "methods": ["GET", "POST", "PUT", "DELETE", "OPTIONS"],
+ "allow_headers": ["Content-Type"]
+}})
+
+# Create global AI model instance
+ai_model = OpenAIModel()
+
+@app.route('/')
+def show():
+ return "Hello APi works"
+
+# Post reques to create a random agent with no prompt
+@app.route('/api/agents/random', methods=['POST'])
+def create_random_agent():
+ """
+ Creates a new random agent with optional concept.
+
+ Request Body:
+ concept (str, optional): Initial concept for the agent
+
+ Returns:
+ JSON: Generated agent data
+ int: HTTP status code
+ """
+ # Get concept from request body if provided
+ data = request.get_json() if request.is_json else {}
+ concept = data.get('concept', '') # Default to empty string if no concept provided
+
+ print("[create_random_agent] - Creating a random agent", f"concept: {concept}" if concept else "")
+
+ # Remove the local instantiation and use global ai_model
+ print("Using global Gemini Model instance", ai_model)
+
+ # Create RandomAgents directory if it doesn't exist
+ random_agents_dir = os.path.join('configs', 'RandomAgents')
+ os.makedirs(random_agents_dir, exist_ok=True)
+
+ # Call the generateAgent function with the concept
+ generated_master_file_path = generateAgent(ai_model, concept)
+ print(f"[create_random_agent] - generatedMasterFilePath for generatedAgent: {generated_master_file_path}")
+
+ try:
+ if not generated_master_file_path:
+ return jsonify({"error": "No file path generated"}), 500
+
+ # Get just the filename without path and extension
+ base_filename = os.path.basename(generated_master_file_path)
+ name_without_ext = os.path.splitext(base_filename)[0]
+
+ # Replace _master with _random in the filename
+ name_without_ext = name_without_ext.replace('_master', '_random')
+
+ # Generate unique filename in the RandomAgents directory
+ counter = 1
+ final_path = os.path.join(random_agents_dir, f"{name_without_ext}.json")
+ while os.path.exists(final_path):
+ filename = f"{name_without_ext}_{counter}.json"
+ final_path = os.path.join(random_agents_dir, filename)
+ counter += 1
+
+ # Move the generated file to the RandomAgents directory
+ if os.path.exists(generated_master_file_path):
+ os.rename(generated_master_file_path, final_path)
+ print(f"[create_random_agent] - Moved file to: {final_path}")
+
+ # Clean up any empty character directory that might have been created
+ char_dir = os.path.dirname(generated_master_file_path)
+ if os.path.exists(char_dir) and not os.listdir(char_dir):
+ os.rmdir(char_dir)
+
+ # Read the JSON data from the file
+ with open(final_path, 'r', encoding='utf-8') as f:
+ data = json.load(f)
+ print(f"[create_random_agent] - Loaded data from file: {data}")
+
+ return jsonify(data), 200
+
+ except Exception as e:
+ print(f"[create_random_agent] - Error: {str(e)}")
+ return jsonify({"error": "Failed to load agent data"}), 500
+
+@app.before_request
+def handle_preflight():
+ """
+ Handles CORS preflight requests by adding required headers.
+
+ Returns:
+ Response: Flask response with CORS headers
+ """
+ if request.method == "OPTIONS":
+ response = make_response()
+ response.headers.add("Access-Control-Allow-Headers", "*")
+ response.headers.add("Access-Control-Allow-Methods", "*")
+ return response
+
+# Your existing routes...
+@app.route('/api/agents', methods=['GET'])
+def get_agents():
+ """
+ Retrieves list of all agents.
+
+ Returns:
+ JSON: List of agent data
+ """
+ return jsonify(agents)
+
+@app.route('/api/agents/', methods=['POST'])
+def create_agent():
+ """
+ Creates a new agent from provided configuration. If an agent with the same name exists,
+ it will be replaced.
+
+ Request Body:
+ agent_details (dict): Agent configuration including name, personality, etc.
+ concept (str): Initial concept for the agent
+
+ Returns:
+ JSON: Created agent data
+ int: HTTP status code
+ """
+ data = request.get_json()
+ print(f"[create_agent] - Received data: {data}")
+
+ # Extract character name from the agent_details
+ character_name = data.get('agent_details', {}).get('name')
+ if not character_name:
+ return jsonify({"error": "Character name is required"}), 400
+
+ # Replace spaces with underscores in the character name
+ character_name = character_name.replace(' ', '_')
+
+ # Create a directory for the character
+ character_dir = os.path.join('configs', character_name)
+ os.makedirs(character_dir, exist_ok=True)
+
+ # Generate filename - always use _master.json
+ filename = f"{character_name}_master.json"
+ file_path = os.path.normpath(os.path.join(character_dir, filename))
+
+ # Create the new character structure based on the incoming data structure
+ new_character_data = {
+ "concept": data.get('concept', ''),
+ "agent": {
+ "agent_details": {
+ "name": character_name.replace('_', ' '),
+ "personality": data.get('agent_details', {}).get('personality', []),
+ "communication_style": data.get('agent_details', {}).get('communication_style', []),
+ "backstory": data.get('agent_details', {}).get('backstory', ''),
+ "universe": data.get('agent_details', {}).get('universe', ''),
+ "topic_expertise": data.get('agent_details', {}).get('topic_expertise', []),
+ "hashtags": data.get('agent_details', {}).get('hashtags', []),
+ "emojis": data.get('agent_details', {}).get('emojis', []),
+ "concept": data.get('concept', '')
+ },
+ "ai_model": {
+ "model_type": "",
+ "model_name": "",
+ "memory_store": ""
+ },
+ "connectors": {
+ "twitter": False,
+ "telegram": False,
+ "discord": False
+ },
+ "profile_image": data.get('profile_image', []),
+ "profile_image_options": data.get('profile_image_options', []),
+ "tracker": {
+ "current_season_number": 0,
+ "current_episode_number": 0,
+ "current_post_number": 0,
+ "post_every_x_minutes": 0
+ },
+ "seasons": data.get('seasons', [])
+ }
+ }
+
+ # Write data to the JSON file, overwriting if it exists
+ with open(file_path, 'w', encoding='utf-8') as f:
+ json.dump(new_character_data, f, ensure_ascii=False, indent=4)
+
+ return jsonify(new_character_data), 201
+
+@app.route('/api/characters', methods=['GET'])
+def get_characters():
+ """
+ Retrieves all character configurations from the configs directory.
+
+ Returns:
+ JSON: List of character configurations
+ int: HTTP status code
+ """
+ try:
+ # Use os.path.join for cross-platform path handling
+ config_dir = 'configs'
+ pattern = os.path.join(config_dir, '**', '*master*.json')
+ # Use os.path.normpath to normalize path separators
+ files = [os.path.normpath(f) for f in glob.glob(pattern, recursive=True)]
+ print(f"[get_characters] - Found {len(files)} files: {files}")
+
+ if not files:
+ print("[get_characters] - No files found in configs directory or subdirectories")
+ return jsonify({"error": "No character files found"}), 404
+
+ characters = []
+ for file in files:
+ try:
+ with open(file, 'r', encoding='utf-8') as f:
+ characters.append(json.load(f))
+ except json.JSONDecodeError as e:
+ print(f"[get_characters] - Error parsing JSON from {file}: {str(e)}")
+ continue
+ except Exception as e:
+ print(f"[get_characters] - Error reading file {file}: {str(e)}")
+ continue
+
+ return jsonify(characters)
+
+ except Exception as e:
+ print(f"[get_characters] - Unexpected error: {str(e)}")
+ return jsonify({"error": str(e)}), 500
+
+@app.route('/api/agents/chat', methods=['POST'])
+def chat_with_agent():
+ """
+ Handles chat interactions with an agent.
+
+ Request Body:
+ prompt (str): User message to the agent
+ master_file_path (str): Path to agent's master configuration file
+ chat_history (dict): Previous chat history
+
+ Returns:
+ JSON: Agent response and updated chat history
+ int: HTTP status code
+ """
+ data = request.get_json()
+ prompt = data.get('prompt')
+
+ master_file_path = data.get('master_file_path')
+ print(f"[chat_with_agent] - master_file_path: {master_file_path}")
+ print("\n\n\n")
+
+ chat_history = data.get('chat_history', {'chat_history': []})
+
+
+ if not master_file_path:
+ return jsonify({"error": "Master file path is required"}), 400
+
+ if not os.path.exists(master_file_path):
+ return jsonify({"error": "Agent master file not found"}), 404
+
+ try:
+ # Initialize AI model
+ ai_model = OpenAIModel()
+
+ # Call the agent_chat function from step_5
+ agent_response, updated_chat_history = agent_chat(
+ ai_model=ai_model,
+ master_file_path=master_file_path,
+ prompt=prompt,
+ chat_history=chat_history
+ )
+
+ return jsonify({
+ "response": agent_response,
+ "chat_history": updated_chat_history
+ })
+ except Exception as e:
+ print(f"Error in chat_with_agent: {str(e)}")
+ return jsonify({"error": str(e)}), 500
+
+@app.route('/api/agents/chat-history', methods=['GET'])
+def get_chat_history():
+ """
+ Retrieves chat history for a specific agent.
+
+ Query Parameters:
+ master_file_path (str): Path to agent's master configuration file
+
+ Returns:
+ JSON: Agent's chat history
+ int: HTTP status code
+ """
+ master_file_path = request.args.get('master_file_path')
+ print(f"[get_chat_history] - master_file_path: {master_file_path}")
+ if not master_file_path:
+ return jsonify({"error": "Master file path is required"}), 400
+
+ # Extract agent name from master file path
+ agent_name = os.path.basename(master_file_path).replace('_master.json', '')
+
+ # Create the chat history file path using the same format as in step_5_agent_chat.py
+ chat_file_path = os.path.join(os.path.dirname(master_file_path), f"{agent_name}_chat_log.json")
+ print(f"[get_chat_history] - chat_file_path: {chat_file_path}")
+ print("\n\n\n")
+ # If chat history doesn't exist, return empty history with agent name
+ if not os.path.exists(chat_file_path):
+ return jsonify({
+ "agent_name": agent_name,
+ "chat_history": []
+ })
+
+ # Load and return the chat history
+ with open(chat_file_path, 'r', encoding='utf-8') as file:
+ chat_history = json.load(file)
+
+ return jsonify(chat_history)
+
+@app.route('/api/agents/seasons', methods=['POST'])
+def create_season():
+ """
+ Generates a new season of content for an agent.
+
+ Request Body:
+ master_file_path (str): Path to agent's master configuration file
+ number_of_episodes (int): Number of episodes to generate
+
+ Returns:
+ JSON: Updated agent data with new season
+ int: HTTP status code
+ """
+ try:
+ data = request.get_json()
+ master_file_path = data.get('master_file_path')
+ number_of_episodes = data.get('number_of_episodes', 3) # Default to 3 episodes
+
+ if not master_file_path:
+ return jsonify({"error": "Master file path is required"}), 400
+
+ # Create a new season using the global AI model
+ result = create_seasons_and_episodes(
+ ai_model=ai_model,
+ master_file_path=master_file_path,
+ number_of_episodes=number_of_episodes
+ )
+
+ # Load and return the updated agent data
+ with open(master_file_path, 'r', encoding='utf-8') as f:
+ updated_agent = json.load(f)
+
+ return jsonify(updated_agent), 200
+
+ except Exception as e:
+ print(f"Error creating season: {str(e)}")
+ return jsonify({"error": str(e)}), 500
+
+@app.route('/api/agents/episodes/posts', methods=['POST'])
+def create_episode_content():
+ """
+ Generates posts for an agent's episodes.
+
+ Request Body:
+ master_file_path (str): Path to agent's master configuration file
+ number_of_posts (int): Number of posts to generate per episode
+
+ Returns:
+ JSON: Updated agent data with new posts
+ int: HTTP status code
+ """
+ try:
+ data = request.get_json()
+ master_file_path = data.get('master_file_path')
+ number_of_posts = data.get('number_of_posts', 6) # Default to 6 posts
+
+ if not master_file_path:
+ return jsonify({"error": "Master file path is required"}), 400
+
+ # Create posts for the episodes using the global AI model
+ result = create_episode_posts(
+ ai_model=ai_model,
+ master_file_path=master_file_path,
+ number_of_posts=number_of_posts
+ )
+
+ # Load and return the updated agent data
+ with open(master_file_path, 'r', encoding='utf-8') as f:
+ updated_agent = json.load(f)
+
+ return jsonify(updated_agent), 200
+
+ except Exception as e:
+ print(f"Error creating episode posts: {str(e)}")
+ return jsonify({"error": str(e)}), 500
+
+# Twitter Posting
+@app.route('/api/start-post-manager/twitter', methods=['POST'])
+def start_post_manager_twitter():
+ global post_manager_twitter
+
+ data = request.json
+ agent_name = data.get('agent_name')
+
+ print("\n")
+ if not agent_name:
+ print("[start_post_manager_twitter] - Agent name is required")
+ return jsonify({'error': 'Agent name is required'}), 400
+
+ try:
+ # Create PostManager instance with the agent name
+ post_manager_twitter = PostManager(agent_name=agent_name)
+ print(f"[start_post_manager_twitter] - post_manager created: {post_manager_twitter}")
+
+ # Check if the PostManager is logged in
+ if post_manager_twitter and post_manager_twitter.is_logged_in:
+ return jsonify({'success': True, 'message': f'Post manager started for {agent_name}'}), 200
+ else:
+ return jsonify({'error': 'Failed to start post manager or login to Twitter'}), 500
+
+ except Exception as e:
+ print(f"[start_post_manager] - Error: {str(e)}")
+ return jsonify({'error': str(e)}), 500
+
+
+@app.route('/api/post-to-twitter', methods=['POST'])
+def post_to_twitter():
+ global post_manager_twitter
+
+ print("\n")
+ try:
+ data = request.json
+ master_data = data.get('master_data')
+ post_content = data.get('content')
+ print(f"[post_to_twitter api ] - post_content: {post_content}")
+ if not master_data or not post_content:
+ return jsonify({'error': 'Master data or post content is required'}), 400
+
+ if post_manager_twitter:
+ post_success = post_manager_twitter.post_single_tweet(post_content)
+ if post_success:
+ return jsonify({'success': True}), 200
+ else:
+ return jsonify({'error': 'Failed to post to Twitter'}), 500
+ else:
+ return jsonify({'error': 'Post manager not initialized'}), 500
+
+ except Exception as e:
+ return jsonify({'error': str(e)}), 500
+
+@app.route('/api/agents/update-seasons', methods=['PUT'])
+def update_seasons():
+ """
+ Updates the seasons array for a specific agent using the agent's name.
+
+ Request Body:
+ agent_name (str): Name of the agent
+ seasons (list): New seasons data to update
+
+ Returns:
+ JSON: Updated agent data
+ int: HTTP status code
+ """
+ try:
+ data = request.get_json()
+ agent_name = data.get('agent_name')
+ new_seasons = data.get('seasons')
+
+ if not agent_name or not new_seasons:
+ return jsonify({"error": "Agent name and seasons data are required"}), 400
+
+ # Construct the master file path using the agent's name
+ master_file_path = os.path.join('configs', agent_name, f"{agent_name}_master.json")
+
+ if not os.path.exists(master_file_path):
+ return jsonify({"error": "Agent master file not found"}), 404
+
+ # Load the existing agent data
+ with open(master_file_path, 'r', encoding='utf-8') as f:
+ agent_data = json.load(f)
+
+ # Update the seasons array
+ agent_data['agent']['seasons'] = new_seasons
+
+ # Save the updated agent data back to the file
+ with open(master_file_path, 'w', encoding='utf-8') as f:
+ json.dump(agent_data, f, ensure_ascii=False, indent=4)
+
+ return jsonify(agent_data), 200
+
+ except Exception as e:
+ print(f"Error updating seasons: {str(e)}")
+ return jsonify({"error": str(e)}), 500
+
+if __name__ == '__main__':
+ print("API Server starting on port 8080...")
+ app.run(host='0.0.0.0',debug=True, port=8080)
\ No newline at end of file
diff --git a/server/asset_generation/images_leonardo.py b/server/asset_generation/images_leonardo.py
new file mode 100644
index 0000000..7b6580a
--- /dev/null
+++ b/server/asset_generation/images_leonardo.py
@@ -0,0 +1,200 @@
+#
+# Module: template_types
+#
+# This module implements the TemplateType class for defining the type of template.
+#
+# Title: Template Types
+# Summary: Template types implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2024-12-31
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-12
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+
+import requests
+import os
+import dotenv
+import time
+import json
+import sys
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# custom ARAI imports
+from utils.content_generator import ContentGenerator
+from utils.template_types import TemplateType
+
+dotenv.load_dotenv()
+
+# Define the Lambda endpoint URL
+get_inconsistent_image_lambda_url = "https://46i9cnowhh.execute-api.us-east-1.amazonaws.com/getImageInconsistent"
+
+# Function to call the AWS Lambda
+def generate_inconsistent_image_lambda(prompt, model_id, style_uuid, num_images):
+
+ url = get_inconsistent_image_lambda_url
+
+ headers = {
+ "Content-Type": "application/json"
+ }
+
+ payload = {
+ "prompt": prompt,
+ "modelId": model_id,
+ "styleUUID": style_uuid,
+ "num_images": num_images
+ }
+
+ try:
+ # Send the POST request with the payload
+ # Use dumpts to convert the payload to a string
+ response = requests.post(url, headers=headers, data=json.dumps(payload))
+
+ # Check for HTTP errors
+ if not response.ok:
+ print(f"HTTP error! Status: {response.status_code}, Response: {response.text}")
+ response.raise_for_status()
+
+ # Parse and return the response JSON
+ data = response.json()
+ return data
+
+ except requests.RequestException as error:
+ print("Error calling Lambda:", error)
+ raise error
+
+#--------------------------------
+# Generate multiple images from a prompt that are inconsistent with each other
+#--------------------------------
+def generated_image_inconsistent(prompt, model_id, style_uuid, num_images):
+ url = "https://cloud.leonardo.ai/api/rest/v1/generations"
+
+ payload = {
+ "modelId": model_id,
+ "presetStyle": "DYNAMIC",
+ "scheduler": "LEONARDO",
+ "sd_version": "SDXL_LIGHTNING",
+ "contrast": 1.3,
+ "prompt": prompt,
+ "num_images": num_images,
+ "width": 512,
+ "height": 512,
+ "alchemy": True,
+ "styleUUID": style_uuid,
+ "enhancePrompt": False,
+ "nsfw": True,
+ "public": False,
+ }
+ headers = {
+ "accept": "application/json",
+ "content-type": "application/json",
+ "authorization": "Bearer " + os.getenv("LEONARDO_API_KEY", "")
+ }
+
+ try:
+ response = requests.post(url, json=payload, headers=headers)
+ return response.json(), payload
+ except Exception as e:
+ print("Error:", str(e))
+ return None, payload
+
+#--------------------------------
+# Generate multiple images from a prompt that are consistent with each other
+#--------------------------------
+def generated_image_consistent(prompt, model_id, style_uuid, num_images):
+ url = "https://cloud.leonardo.ai/api/rest/v1/generations"
+
+ payload = {
+ "modelId": model_id,
+ "presetStyle": "DYNAMIC",
+ "scheduler": "LEONARDO",
+ "sd_version": "SDXL_LIGHTNING",
+ "contrast": 1.3,
+ "prompt": prompt,
+ "num_images": num_images,
+ "width": 1024,
+ "height": 1024,
+ "alchemy": True,
+ "styleUUID": style_uuid,
+ "enhancePrompt": False,
+ "nsfw": True,
+ "public": False,
+ "num_inference_steps": 10,
+ "guidance_scale": 7,
+
+ }
+ headers = {
+ "accept": "application/json",
+ "content-type": "application/json",
+ "authorization": "Bearer " + os.getenv("LEONARDO_API_KEY", "")
+ }
+
+ try:
+ response = requests.post(url, json=payload, headers=headers)
+ return response.json(), payload
+ except Exception as e:
+ print("Error:", str(e))
+ return None, payload
+
+#--------------------------------
+# Get the image URL from a generation ID
+#--------------------------------
+def get_image_url(generation_id):
+ url = "https://cloud.leonardo.ai/api/rest/v1/generations/" + generation_id
+
+ headers = {
+ "accept": "application/json",
+ "authorization": "Bearer " + os.getenv("LEONARDO_API_KEY", "")
+ }
+
+ response = requests.get(url, headers=headers)
+
+ return response.json()
+
+#--------------------------------
+# Get list of models
+#--------------------------------
+def get_models():
+ url = "https://cloud.leonardo.ai/api/rest/v1/platformModels"
+
+ headers = {
+ "accept": "application/json",
+ "authorization": "Bearer " + os.getenv("LEONARDO_API_KEY", "")
+ }
+
+ response = requests.get(url, headers=headers)
+
+ return response.json()
+
+# Get a list of model styles
+#--------------------------------
+def get_model_styles(model_id):
+ url = "https://cloud.leonardo.ai/api/rest/v1/platformModels/" + model_id + "/styles"
+
+ headers = {
+ "accept": "application/json",
+ "authorization": "Bearer " + os.getenv("LEONARDO_API_KEY", "")
+ }
+
+ response = requests.get(url, headers=headers)
+
+ return response.json()
+
+#--------------------------------
+# Get list of elements
+#--------------------------------
+def get_elements():
+ url = "https://cloud.leonardo.ai/api/rest/v1/elements"
+
+ headers = {
+ "accept": "application/json",
+ "authorization": "Bearer " + os.getenv("LEONARDO_API_KEY", "")
+ }
+
+ response = requests.get(url, headers=headers)
+
+ return response.json()
diff --git a/server/asset_generation/leonard_anime_styles.json b/server/asset_generation/leonard_anime_styles.json
new file mode 100644
index 0000000..cbeb4ea
--- /dev/null
+++ b/server/asset_generation/leonard_anime_styles.json
@@ -0,0 +1,50 @@
+{
+ "models": [
+ {
+ "model name": "Leonardo Anime XL",
+ "modelId": "e71a1c2f-4f80-4800-934f-2c68979d8cc8",
+ "styles": [
+ {
+ "style name": "Anime General",
+ "styleUUID": "b2a54a51-230b-4d4f-ad4e-8409bf58645f"
+ },
+ {
+ "style name": "Anime Retro",
+ "styleUUID": "1824f984-dc0b-4f72-aa32-0442091d488c"
+ },
+ {
+ "style name": "Anime None",
+ "styleUUID": "e89ca6f0-7653-4db6-8a24-ded9d1267d84"
+ },
+ {
+ "style name": "Anime Background",
+ "styleUUID": "5f611024-668e-41cc-b538-4569a7985cad"
+ },
+ {
+ "style name": "Anime Flat Illustration",
+ "styleUUID": ""
+ },
+ {
+ "style name": "Anime Monoschrome",
+ "styleUUID": ""
+ },
+ {
+ "style name": "Anime Screencap",
+ "styleUUID": ""
+ },
+ {
+ "style name": "Anime Semi-Realism",
+ "styleUUID": ""
+ },
+ {
+ "style name": "Character Sheet",
+ "styleUUID": ""
+ },
+ {
+ "style name": "Manga",
+ "styleUUID": ""
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/server/asset_generation/leonard_models.py b/server/asset_generation/leonard_models.py
new file mode 100644
index 0000000..6911551
--- /dev/null
+++ b/server/asset_generation/leonard_models.py
@@ -0,0 +1,39 @@
+#
+# Module: template_types
+#
+# This module implements the TemplateType class for defining the type of template.
+#
+# Title: Leonard Models
+# Summary: Leonard models implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2025-01-12
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-12
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+
+class LeonardModel:
+ # https://docs.leonardo.ai/docs/generate-images-using-flux
+ # Preset_Model modelId
+ FLUX_SPEED_FLUX_SCHNELL = "1dd50843-d653-4516-a8e3-f0238ee453ff"
+ FLUX_PRECISION_FLUX_DEV = "b2614463-296c-462a-9586-aafdb8f00e36"
+
+ # Flux Preset Style UUID
+ THREE_D_RENDER = "debdf72a-91a4-467b-bf61-cc02bdeb69c6"
+ ACRYLIC = "3cbb655a-7ca4-463f-b697-8a03ad67327c"
+ ANIME_GENERAL = "b2a54a51-230b-4d4f-ad4e-8409bf58645f"
+ CREATIVE = "6fedbf1f-4a17-45ec-84fb-92fe524a29ef"
+ DYNAMIC = "111dc692-d470-4eec-b791-3475abac4c46"
+ FASHION = "594c4a08-a522-4e0e-b7ff-e4dac4b6b622"
+ GAME_CONCEPT = "09d2b5b5-d7c5-4c02-905d-9f84051640f4"
+ GRAPHIC_DESIGN_3D = "7d7c2bc5-4b12-4ac3-81a9-630057e9e89f"
+ ILLUSTRATION = "645e4195-f63d-4715-a3f2-3fb1e6eb8c70"
+ NONE = "556c1ee5-ec38-42e8-955a-1e82dad0ffa1"
+ PORTRAIT = "8e2bc543-6ee2-45f9-bcd9-594b6ce84dcd"
+ PORTRAIT_CINEMATIC = "4edb03c9-8a26-4041-9d01-f85b5d4abd71"
+ RAY_TRACED = "b504f83c-3326-4947-82e1-7fe9e839ec0f"
+ STOCK_PHOTO = "5bdc3f2a-1be6-4d1c-8e77-992a30824a2c"
+ WATERCOLOR = "1db308ce-c7ad-4d10-96fd-592fa6b75cc4"
\ No newline at end of file
diff --git a/server/auth/__init__.py b/server/auth/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/server/auth/twitter_app_auth.py b/server/auth/twitter_app_auth.py
new file mode 100644
index 0000000..8016856
--- /dev/null
+++ b/server/auth/twitter_app_auth.py
@@ -0,0 +1,148 @@
+#
+# Module: twitter_app_auth
+#
+# This module implements the TwitterAppAuth class for authenticating with the Twitter API using OAuth 1.0a.
+#
+# Title: Twitter App Auth
+# Summary: Twitter app authentication implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2024-12-31
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-04
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+import os
+from dotenv import load_dotenv
+import tweepy
+import webbrowser
+import yaml
+
+load_dotenv()
+
+class TwitterAppAuth:
+ """
+ A class for authenticating with the Twitter API using OAuth 1.0a.
+
+ Attributes:
+ api_key (str): The API key for the Twitter API.
+ api_secret_key (str): The API secret key for the Twitter API.
+ bearer_token (str): The bearer token for the Twitter API.
+ """
+ def __init__(self):
+ """Initializes the TwitterAppAuth class.
+
+ Example:
+ >>> twitter_auth = TwitterAppAuth()
+ """
+ self.api_key = os.getenv("TWITTER_API_KEY")
+ self.api_secret_key = os.getenv("TWITTER_API_KEY_SECRET")
+ self.bearer_token = os.getenv("TWITTER_BEARER_TOKEN")
+
+ def setup_twitter_auth(self):
+ """Sets up the Twitter authentication.
+
+ Raises:
+ ValueError: If Twitter API credentials are not present in the environment variables.
+
+ Example:
+ >>> twitter_auth.setup_twitter_auth()
+ """
+ try:
+ # Initialize OAuth 1.0a handler
+ self.auth = tweepy.OAuth1UserHandler(
+ consumer_key=self.api_key,
+ consumer_secret=self.api_secret_key,
+ callback="oob" # Use out-of-band OAuth
+ )
+
+ # Get the authorization URL
+ try:
+ auth_url = self.auth.get_authorization_url()
+ print("\n1. Visit this URL to authorize your app:")
+ print(auth_url)
+
+ # Open the URL in default browser
+ webbrowser.open(auth_url)
+
+ # Get the verifier code from user
+ print("\n2. Enter the PIN shown on the website:")
+ verifier = input("> ").strip()
+
+ # Get the access tokens
+ try:
+ access_token, access_token_secret = self.auth.get_access_token(verifier)
+
+ print("\nSuccess! Add these tokens to your .env file:")
+ print(f"\nTWITTER_ACCESS_TOKEN={access_token}")
+ print(f"TWITTER_ACCESS_TOKEN_SECRET={access_token_secret}")
+
+ # Test the credentials
+ self.client = tweepy.Client(
+ bearer_token=self.bearer_token,
+ consumer_key=self.api_key,
+ consumer_secret=self.api_secret_key,
+ access_token=access_token,
+ access_token_secret=access_token_secret
+ )
+
+ me = self.client.get_me()
+ print(f"\nSuccessfully authenticated as @{me.data.username}")
+
+ self.save_credentials(access_token, access_token_secret)
+
+ except Exception as e:
+ print(f"\nError getting access tokens: {str(e)}")
+
+ except Exception as e:
+ print(f"\nError getting authorization URL: {str(e)}")
+ print("\nMake sure your app has OAuth 1.0a enabled in the Twitter Developer Portal:")
+ print("1. Go to https://developer.twitter.com/en/portal/projects")
+ print("2. Select your project and app")
+ print("3. Go to 'User authentication settings'")
+ print("4. Enable 'OAuth 1.0a'")
+ print("5. Set App permissions to 'Read and Write'")
+ print("6. Add 'http://127.0.0.1' to callback URLs")
+
+ except Exception as e:
+ print(f"\nSetup error: {str(e)}")
+ print("\nCheck that your API credentials are correct in .env file:")
+ print("TWITTER_API_KEY")
+ print("TWITTER_API_KEY_SECRET")
+
+
+ def save_credentials(self, access_token, access_token_secret):
+ """Saves the Twitter access tokens to a YAML file.
+
+ Args:
+ access_token (str): The access token for the Twitter API.
+ access_token_secret (str): The access token secret for the Twitter API.
+
+ Example:
+ >>> twitter_auth.save_credentials(access_token, access_token_secret)
+ """
+ try:
+ tokens = {
+ 'TWITTER_ACCESS_TOKEN': access_token,
+ 'TWITTER_ACCESS_TOKEN_SECRET': access_token_secret
+ }
+
+ with open('twitter_tokens.yaml', 'w') as f:
+ yaml.dump(tokens, f)
+
+ print("\nSuccess! Tokens have been saved to twitter_tokens.yaml")
+
+ except Exception as e:
+ print(f"\nError saving tokens: {str(e)}")
+
+
+if __name__ == "__main__":
+ """Main function to run the TwitterAppAuth class.
+
+ Example:
+ >>> python twitter_app_auth.py
+ """
+ auth = TwitterAppAuth()
+ auth.setup_twitter_auth()
diff --git a/server/configs/Cassandra_Fox/Cassandra_Fox_master.json b/server/configs/Cassandra_Fox/Cassandra_Fox_master.json
new file mode 100644
index 0000000..0a1e1a6
--- /dev/null
+++ b/server/configs/Cassandra_Fox/Cassandra_Fox_master.json
@@ -0,0 +1,123 @@
+{
+ "concept": "",
+ "agent": {
+ "agent_details": {
+ "name": "Cassandra Fox",
+ "personality": [
+ "Methodical",
+ "focused",
+ "analytical",
+ "patient",
+ "detail-oriented",
+ "introspective",
+ "determined."
+ ],
+ "communication_style": [],
+ "backstory": "",
+ "universe": "",
+ "topic_expertise": [
+ "Forensic Anthropology",
+ "Human Remains Analysis"
+ ],
+ "hashtags": [],
+ "emojis": [],
+ "concept": ""
+ },
+ "ai_model": {
+ "model_type": "",
+ "model_name": "",
+ "memory_store": ""
+ },
+ "connectors": {
+ "twitter": false,
+ "telegram": false,
+ "discord": false
+ },
+ "profile_image": {
+ "details": {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/a7e5f8a4-c947-48c3-a8ff-4654bf2d9b60/Leonardo_Anime_XL_A_focused_female_agent_in_a_forensic_lab_exa_0.jpg",
+ "image_id": "04a63db0-764e-4b64-8cd8-b1199e33fab0"
+ }
+ },
+ "profile_image_options": [
+ {
+ "generations_by_pk": {
+ "createdAt": "2025-02-07T06:50:22.058",
+ "fantasyAvatar": null,
+ "generated_images": [
+ {
+ "generated_image_variation_generics": [],
+ "id": "04a63db0-764e-4b64-8cd8-b1199e33fab0",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "nsfw": false,
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/a7e5f8a4-c947-48c3-a8ff-4654bf2d9b60/Leonardo_Anime_XL_A_focused_female_agent_in_a_forensic_lab_exa_0.jpg"
+ },
+ {
+ "generated_image_variation_generics": [],
+ "id": "5db26ccf-9931-4998-8109-3ae18362fe99",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "nsfw": false,
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/a7e5f8a4-c947-48c3-a8ff-4654bf2d9b60/Leonardo_Anime_XL_A_focused_female_agent_in_a_forensic_lab_exa_1.jpg"
+ },
+ {
+ "generated_image_variation_generics": [],
+ "id": "af6fb8cb-251c-486a-8195-979e218be28f",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "nsfw": false,
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/a7e5f8a4-c947-48c3-a8ff-4654bf2d9b60/Leonardo_Anime_XL_A_focused_female_agent_in_a_forensic_lab_exa_2.jpg"
+ },
+ {
+ "generated_image_variation_generics": [],
+ "id": "250506dc-0787-4bdf-bd50-1196fc08c1c0",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "nsfw": false,
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/a7e5f8a4-c947-48c3-a8ff-4654bf2d9b60/Leonardo_Anime_XL_A_focused_female_agent_in_a_forensic_lab_exa_3.jpg"
+ }
+ ],
+ "generation_elements": [],
+ "guidanceScale": 1.3,
+ "id": "a7e5f8a4-c947-48c3-a8ff-4654bf2d9b60",
+ "imageHeight": 512,
+ "imageToVideo": null,
+ "imageWidth": 512,
+ "inferenceSteps": null,
+ "initStrength": null,
+ "modelId": "e71a1c2f-4f80-4800-934f-2c68979d8cc8",
+ "motion": null,
+ "motionModel": null,
+ "motionStrength": null,
+ "negativePrompt": "",
+ "photoReal": false,
+ "photoRealStrength": null,
+ "presetStyle": "DYNAMIC",
+ "prompt": "A focused female agent in a forensic lab, examining skeletal remains in a scientific setting. She’s working with a microscope, examining bones and forensic reports, with detailed anatomical charts and forensic tools surrounding her",
+ "promptMagic": false,
+ "promptMagicStrength": null,
+ "promptMagicVersion": null,
+ "prompt_moderations": [
+ {
+ "moderationClassification": []
+ }
+ ],
+ "public": false,
+ "scheduler": "LEONARDO",
+ "sdVersion": "SDXL_LIGHTNING",
+ "seed": 6198019575,
+ "status": "COMPLETE",
+ "ultra": null
+ }
+ }
+ ],
+ "tracker": {
+ "current_season_number": 0,
+ "current_episode_number": 0,
+ "current_post_number": 0,
+ "post_every_x_minutes": 0
+ },
+ "seasons": []
+ }
+}
\ No newline at end of file
diff --git a/server/configs/Dexter_Lorne/Dexter_Lorne_master.json b/server/configs/Dexter_Lorne/Dexter_Lorne_master.json
new file mode 100644
index 0000000..e6daec5
--- /dev/null
+++ b/server/configs/Dexter_Lorne/Dexter_Lorne_master.json
@@ -0,0 +1,123 @@
+{
+ "concept": "",
+ "agent": {
+ "agent_details": {
+ "name": "Dexter Lorne",
+ "personality": [
+ "Confident",
+ "strategic",
+ "composed",
+ "charming",
+ "pragmatic",
+ "observant",
+ "persuasive."
+ ],
+ "communication_style": [],
+ "backstory": "",
+ "universe": "",
+ "topic_expertise": [
+ "Financial Crime",
+ "Money Laundering Prevention"
+ ],
+ "hashtags": [],
+ "emojis": [],
+ "concept": ""
+ },
+ "ai_model": {
+ "model_type": "",
+ "model_name": "",
+ "memory_store": ""
+ },
+ "connectors": {
+ "twitter": false,
+ "telegram": false,
+ "discord": false
+ },
+ "profile_image": {
+ "details": {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/007d9d17-aa5a-4c5a-8667-5de092886637/Leonardo_Anime_XL_A_focused_male_agent_in_a_professional_suit_0.jpg",
+ "image_id": "e0193f0f-fb57-46ee-8de3-640fe27bc29e"
+ }
+ },
+ "profile_image_options": [
+ {
+ "generations_by_pk": {
+ "createdAt": "2025-02-07T06:44:47.837",
+ "fantasyAvatar": null,
+ "generated_images": [
+ {
+ "generated_image_variation_generics": [],
+ "id": "e0193f0f-fb57-46ee-8de3-640fe27bc29e",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "nsfw": false,
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/007d9d17-aa5a-4c5a-8667-5de092886637/Leonardo_Anime_XL_A_focused_male_agent_in_a_professional_suit_0.jpg"
+ },
+ {
+ "generated_image_variation_generics": [],
+ "id": "72f29180-7428-4805-8a4a-c3743705cdc6",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "nsfw": false,
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/007d9d17-aa5a-4c5a-8667-5de092886637/Leonardo_Anime_XL_A_focused_male_agent_in_a_professional_suit_1.jpg"
+ },
+ {
+ "generated_image_variation_generics": [],
+ "id": "8abbaccd-c152-4a6b-a61a-9f6195f27766",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "nsfw": false,
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/007d9d17-aa5a-4c5a-8667-5de092886637/Leonardo_Anime_XL_A_focused_male_agent_in_a_professional_suit_2.jpg"
+ },
+ {
+ "generated_image_variation_generics": [],
+ "id": "e94c11c8-d420-4a03-a2cb-766f38b45a04",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "nsfw": false,
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/007d9d17-aa5a-4c5a-8667-5de092886637/Leonardo_Anime_XL_A_focused_male_agent_in_a_professional_suit_3.jpg"
+ }
+ ],
+ "generation_elements": [],
+ "guidanceScale": 1.3,
+ "id": "007d9d17-aa5a-4c5a-8667-5de092886637",
+ "imageHeight": 512,
+ "imageToVideo": null,
+ "imageWidth": 512,
+ "inferenceSteps": null,
+ "initStrength": null,
+ "modelId": "e71a1c2f-4f80-4800-934f-2c68979d8cc8",
+ "motion": null,
+ "motionModel": null,
+ "motionStrength": null,
+ "negativePrompt": "",
+ "photoReal": false,
+ "photoRealStrength": null,
+ "presetStyle": "DYNAMIC",
+ "prompt": "A focused male agent in a professional suit, sitting in front of a large financial display board. He’s examining encrypted financial transactions and charts on a digital tablet, with high-tech banking and anti-money laundering systems in the background.",
+ "promptMagic": false,
+ "promptMagicStrength": null,
+ "promptMagicVersion": null,
+ "prompt_moderations": [
+ {
+ "moderationClassification": []
+ }
+ ],
+ "public": false,
+ "scheduler": "LEONARDO",
+ "sdVersion": "SDXL_LIGHTNING",
+ "seed": 9117045654,
+ "status": "COMPLETE",
+ "ultra": null
+ }
+ }
+ ],
+ "tracker": {
+ "current_season_number": 0,
+ "current_episode_number": 0,
+ "current_post_number": 0,
+ "post_every_x_minutes": 0
+ },
+ "seasons": []
+ }
+}
\ No newline at end of file
diff --git a/server/configs/Elara_Quinn/Elara_Quinn_master.json b/server/configs/Elara_Quinn/Elara_Quinn_master.json
new file mode 100644
index 0000000..c2cb0bf
--- /dev/null
+++ b/server/configs/Elara_Quinn/Elara_Quinn_master.json
@@ -0,0 +1,123 @@
+{
+ "concept": "",
+ "agent": {
+ "agent_details": {
+ "name": "Elara Quinn",
+ "personality": [
+ "Empathetic",
+ "intuitive",
+ "reserved",
+ "patient",
+ "insightful",
+ "observant",
+ "compassionate."
+ ],
+ "communication_style": [],
+ "backstory": "",
+ "universe": "",
+ "topic_expertise": [
+ "Criminal Psychology",
+ "Behavioral Analysis"
+ ],
+ "hashtags": [],
+ "emojis": [],
+ "concept": ""
+ },
+ "ai_model": {
+ "model_type": "",
+ "model_name": "",
+ "memory_store": ""
+ },
+ "connectors": {
+ "twitter": false,
+ "telegram": false,
+ "discord": false
+ },
+ "profile_image": {
+ "details": {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/e477f131-c57d-4abc-b589-8b32fe133b0f/Leonardo_Anime_XL_A_thoughtful_female_agent_in_a_psychology_of_0.jpg",
+ "image_id": "9ec82199-ab99-4c02-b9b4-7e4c9f094836"
+ }
+ },
+ "profile_image_options": [
+ {
+ "generations_by_pk": {
+ "createdAt": "2025-02-07T06:40:22.955",
+ "fantasyAvatar": null,
+ "generated_images": [
+ {
+ "generated_image_variation_generics": [],
+ "id": "9ec82199-ab99-4c02-b9b4-7e4c9f094836",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "nsfw": false,
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/e477f131-c57d-4abc-b589-8b32fe133b0f/Leonardo_Anime_XL_A_thoughtful_female_agent_in_a_psychology_of_0.jpg"
+ },
+ {
+ "generated_image_variation_generics": [],
+ "id": "90dc5b6a-2e00-4ca5-8671-56827d58ffa4",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "nsfw": false,
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/e477f131-c57d-4abc-b589-8b32fe133b0f/Leonardo_Anime_XL_A_thoughtful_female_agent_in_a_psychology_of_1.jpg"
+ },
+ {
+ "generated_image_variation_generics": [],
+ "id": "963bbd45-a1d0-4638-8202-9e28dd37e5a4",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "nsfw": false,
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/e477f131-c57d-4abc-b589-8b32fe133b0f/Leonardo_Anime_XL_A_thoughtful_female_agent_in_a_psychology_of_2.jpg"
+ },
+ {
+ "generated_image_variation_generics": [],
+ "id": "881671aa-f9fa-437d-98d0-89cc9a64e528",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "nsfw": false,
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/e477f131-c57d-4abc-b589-8b32fe133b0f/Leonardo_Anime_XL_A_thoughtful_female_agent_in_a_psychology_of_3.jpg"
+ }
+ ],
+ "generation_elements": [],
+ "guidanceScale": 1.3,
+ "id": "e477f131-c57d-4abc-b589-8b32fe133b0f",
+ "imageHeight": 512,
+ "imageToVideo": null,
+ "imageWidth": 512,
+ "inferenceSteps": null,
+ "initStrength": null,
+ "modelId": "e71a1c2f-4f80-4800-934f-2c68979d8cc8",
+ "motion": null,
+ "motionModel": null,
+ "motionStrength": null,
+ "negativePrompt": "",
+ "photoReal": false,
+ "photoRealStrength": null,
+ "presetStyle": "DYNAMIC",
+ "prompt": "A thoughtful female agent in a psychology office, sitting at a desk surrounded by psychological profiles and case notes. She’s analyzing a criminal behavior chart on a screen, with psychological books and criminology texts scattered around the room.",
+ "promptMagic": false,
+ "promptMagicStrength": null,
+ "promptMagicVersion": null,
+ "prompt_moderations": [
+ {
+ "moderationClassification": []
+ }
+ ],
+ "public": false,
+ "scheduler": "LEONARDO",
+ "sdVersion": "SDXL_LIGHTNING",
+ "seed": 54561375,
+ "status": "COMPLETE",
+ "ultra": null
+ }
+ }
+ ],
+ "tracker": {
+ "current_season_number": 0,
+ "current_episode_number": 0,
+ "current_post_number": 0,
+ "post_every_x_minutes": 0
+ },
+ "seasons": []
+ }
+}
\ No newline at end of file
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new file mode 100644
index 0000000..1dc93c0
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new file mode 100644
index 0000000..cbb8628
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new file mode 100644
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diff --git a/server/configs/Mia_Caldwell/Mia_Caldwell_master.json b/server/configs/Mia_Caldwell/Mia_Caldwell_master.json
new file mode 100644
index 0000000..4fbe849
--- /dev/null
+++ b/server/configs/Mia_Caldwell/Mia_Caldwell_master.json
@@ -0,0 +1,123 @@
+{
+ "concept": "",
+ "agent": {
+ "agent_details": {
+ "name": "Mia Caldwell",
+ "personality": [
+ "Visionary",
+ "creative",
+ "problem-solving",
+ "ambitious",
+ "social",
+ "practical",
+ "forward-thinking."
+ ],
+ "communication_style": [],
+ "backstory": "",
+ "universe": "",
+ "topic_expertise": [
+ "Urban Planning",
+ "Smart City Solutions"
+ ],
+ "hashtags": [],
+ "emojis": [],
+ "concept": ""
+ },
+ "ai_model": {
+ "model_type": "",
+ "model_name": "",
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+ "telegram": false,
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+ },
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diff --git a/server/configs/RandomAgents/Gaudinho Artdor_random.json b/server/configs/RandomAgents/Gaudinho Artdor_random.json
new file mode 100644
index 0000000..26f85d5
--- /dev/null
+++ b/server/configs/RandomAgents/Gaudinho Artdor_random.json
@@ -0,0 +1,94 @@
+{
+ "agent": {
+ "concept": "Create a parody meme of Antoni Gaudí. The meme should be a funny and clever meme that captures the essence of the person and their achievements. Make it witty and memorable while staying respectful. Include their most iconic features, expressions, or famous quotes if applicable.\n Make sure to use their name as part of their agent name. It is best to make a variation of their name. For example, if the person is Elon Musk, then the agent name could be Elon Musk Jr., Elon Gate, Trump Bot, Trump Tron, etc. Make something unique and memorable that could go viral within the first 24 hours of being posted.",
+ "agent_details": {
+ "name": "Gaudinho Artdor",
+ "personality": [
+ "quirky",
+ "passionate about architecture",
+ "resourceful",
+ "imaginative",
+ "witty"
+ ],
+ "communication_style": [
+ "playful",
+ "pun-laden",
+ "humorous",
+ "energetic",
+ "enthusiastic"
+ ],
+ "backstory": "Once an architecture student inspired by the great Antoni Gaudí, Gaudinho Artdor discovered a mysterious blueprint buried in the ancient streets of Barcelona. These blueprints seem to hold secrets of fantastical and whimsical constructions beyond imagination. Embarking on a quest to bring these designs to life, Gaudinho travels the world spreading the magic of art and architecture while facing quirky challenges and rival architects. Gaudinho believes that art and beauty can solve any problem, fueled by a mission to create structures that inspire laughter, joy, and wonder. With each structure built, a mystery deepens, hinting at a much larger grand design hidden within the patterns.",
+ "universe": "Set in a parallel universe where every city is a character itself, thriving with sentient buildings that communicate emotions through colors and shapes. This world is a mixture of advanced steampunk technology and organic art forms, where architects are seen as wizards wielding magic through their designs. While cities compete for aesthetic supremacy, secret societies of rogue artists and architects battle against mundane, unimaginative forces seeking to gray out the world. Authority figures are depicted as mechanical bureaucrats trying to order the chaos, yet secretly appreciative of Gaudinho's unruly spirit. Social and political landscapes fluctuate based on the latest artistic trends, with rivaling factions vying for dominance in the world's most fabulous craft fair.",
+ "topic_expertise": [
+ "whimsical architecture",
+ "art history",
+ "magic of design",
+ "creative problem-solving",
+ "art-inspired adventures"
+ ],
+ "hashtags": [
+ "#GaudinhoArtdor",
+ "#WhimsyArchitecture",
+ "#BuildTheImpossible",
+ "#MagicOfDesign",
+ "#ArtisticAdventures"
+ ],
+ "emojis": [
+ "🎨",
+ "🏛️",
+ "🔮",
+ "🏗️",
+ "🌈"
+ ]
+ },
+ "ai_model": {
+ "model_type": "",
+ "model_name": "",
+ "memory_store": ""
+ },
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+ },
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+ "seasons": [
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+ "season_number": 0,
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+ "season_highlights": "",
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+ "season_posted": false,
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+ "episodes": [
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+ "episode_posted": false,
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+ "posts": [
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+ "post_number": 0,
+ "post_content": "",
+ "post_highlights": "",
+ "post_posted": false
+ }
+ ]
+ }
+ ]
+ }
+ ]
+ }
+}
\ No newline at end of file
diff --git a/server/configs/Ravi_Patel/Ravi_Patel_master.json b/server/configs/Ravi_Patel/Ravi_Patel_master.json
new file mode 100644
index 0000000..fa36078
--- /dev/null
+++ b/server/configs/Ravi_Patel/Ravi_Patel_master.json
@@ -0,0 +1,123 @@
+{
+ "concept": "",
+ "agent": {
+ "agent_details": {
+ "name": "Ravi Patel",
+ "personality": [
+ "Curious",
+ "innovative",
+ "intellectual",
+ "friendly",
+ "collaborative",
+ "warm",
+ "approachable."
+ ],
+ "communication_style": [],
+ "backstory": "",
+ "universe": "",
+ "topic_expertise": [
+ "Advanced Robotics",
+ "AI Integration"
+ ],
+ "hashtags": [],
+ "emojis": [],
+ "concept": ""
+ },
+ "ai_model": {
+ "model_type": "",
+ "model_name": "",
+ "memory_store": ""
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+ },
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+ "details": {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/6fd386ac-e89b-4259-87c3-4f1cc2a54d54/Leonardo_Anime_XL_A_techsavvy_male_agent_in_a_lab_coat_oversee_0.jpg",
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+}
\ No newline at end of file
diff --git a/server/configs/Seraphina_Blackwell/Seraphina_Blackwell_master.json b/server/configs/Seraphina_Blackwell/Seraphina_Blackwell_master.json
new file mode 100644
index 0000000..9a3d17b
--- /dev/null
+++ b/server/configs/Seraphina_Blackwell/Seraphina_Blackwell_master.json
@@ -0,0 +1,116 @@
+{
+ "concept": "",
+ "agent": {
+ "agent_details": {
+ "name": "Seraphina Blackwell",
+ "personality": [],
+ "communication_style": [],
+ "backstory": "",
+ "universe": "",
+ "topic_expertise": [
+ "High-Profile Diplomacy",
+ "Negotiation"
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\ No newline at end of file
diff --git a/server/configs/Victor_Hayes/Victor_Hayes_master.json b/server/configs/Victor_Hayes/Victor_Hayes_master.json
new file mode 100644
index 0000000..1d0a418
--- /dev/null
+++ b/server/configs/Victor_Hayes/Victor_Hayes_master.json
@@ -0,0 +1,116 @@
+{
+ "concept": "",
+ "agent": {
+ "agent_details": {
+ "name": "Victor Hayes",
+ "personality": [],
+ "communication_style": [],
+ "backstory": "",
+ "universe": "",
+ "topic_expertise": [
+ "Wilderness Survival",
+ "Natural Disaster Response"
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+ "ai_model": {
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+ "profile_image": {
+ "details": {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/b53642e2-c961-4538-826a-d2c514b4e315/Leonardo_Anime_XL_A_rugged_male_agent_in_survival_gear_navigat_1.jpg",
+ "image_id": "27299f36-e44c-4b0e-9faa-3e200ececed8",
+ "generationId": ""
+ }
+ },
+ "profile_image_options": [
+ {
+ "generations_by_pk": {
+ "generated_images": [
+ {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/b53642e2-c961-4538-826a-d2c514b4e315/Leonardo_Anime_XL_A_rugged_male_agent_in_survival_gear_navigat_0.jpg",
+ "nsfw": false,
+ "id": "d0d388cf-e08e-4961-87b8-41c251bb5877",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "generated_image_variation_generics": []
+ },
+ {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/b53642e2-c961-4538-826a-d2c514b4e315/Leonardo_Anime_XL_A_rugged_male_agent_in_survival_gear_navigat_1.jpg",
+ "nsfw": false,
+ "id": "27299f36-e44c-4b0e-9faa-3e200ececed8",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "generated_image_variation_generics": []
+ },
+ {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/b53642e2-c961-4538-826a-d2c514b4e315/Leonardo_Anime_XL_A_rugged_male_agent_in_survival_gear_navigat_2.jpg",
+ "nsfw": false,
+ "id": "7ad22c29-c80b-423f-b40c-18fee3189ca9",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "generated_image_variation_generics": []
+ },
+ {
+ "url": "https://cdn.leonardo.ai/users/d506f027-508f-4cf3-99ea-811721ad6a3a/generations/b53642e2-c961-4538-826a-d2c514b4e315/Leonardo_Anime_XL_A_rugged_male_agent_in_survival_gear_navigat_3.jpg",
+ "nsfw": false,
+ "id": "782a1dc2-2df7-40cf-aca5-234b4af0ff21",
+ "likeCount": 0,
+ "motionMP4URL": null,
+ "generated_image_variation_generics": []
+ }
+ ],
+ "modelId": "e71a1c2f-4f80-4800-934f-2c68979d8cc8",
+ "motion": null,
+ "motionModel": null,
+ "motionStrength": null,
+ "prompt": "A rugged male agent in survival gear, navigating through a dense forest during a storm. He’s leading a group of survivors, guiding them towards safety with a map and compass, surrounded by thick trees and turbulent weather.",
+ "negativePrompt": "",
+ "imageHeight": 512,
+ "imageToVideo": null,
+ "imageWidth": 512,
+ "inferenceSteps": null,
+ "seed": 8224579333,
+ "ultra": null,
+ "public": false,
+ "scheduler": "LEONARDO",
+ "sdVersion": "SDXL_LIGHTNING",
+ "status": "COMPLETE",
+ "presetStyle": "DYNAMIC",
+ "initStrength": null,
+ "guidanceScale": 1.3,
+ "id": "b53642e2-c961-4538-826a-d2c514b4e315",
+ "createdAt": "2025-02-07T06:49:01.658",
+ "promptMagic": false,
+ "promptMagicVersion": null,
+ "promptMagicStrength": null,
+ "photoReal": false,
+ "photoRealStrength": null,
+ "fantasyAvatar": null,
+ "prompt_moderations": [
+ {
+ "moderationClassification": []
+ }
+ ],
+ "generation_elements": []
+ }
+ }
+ ],
+ "tracker": {
+ "current_season_number": 0,
+ "current_episode_number": 0,
+ "current_post_number": 0,
+ "post_every_x_minutes": 0
+ },
+ "seasons": []
+ }
+}
\ No newline at end of file
diff --git a/server/configs/temporary/processed_response.json b/server/configs/temporary/processed_response.json
new file mode 100644
index 0000000..2e3038a
--- /dev/null
+++ b/server/configs/temporary/processed_response.json
@@ -0,0 +1,3 @@
+{
+ "response": "Hey there! 😊 How can I assist you today?"
+}
\ No newline at end of file
diff --git a/server/configs/temporary/raw_response.json b/server/configs/temporary/raw_response.json
new file mode 100644
index 0000000..2cb249b
--- /dev/null
+++ b/server/configs/temporary/raw_response.json
@@ -0,0 +1,5 @@
+```json
+{
+ "response": "Hey there! 😊 How can I assist you today?"
+}
+```
\ No newline at end of file
diff --git a/server/connectors/__init__.py b/server/connectors/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/server/connectors/twitter_api_free_connector.py b/server/connectors/twitter_api_free_connector.py
new file mode 100644
index 0000000..d9e3804
--- /dev/null
+++ b/server/connectors/twitter_api_free_connector.py
@@ -0,0 +1,143 @@
+from playwright.sync_api import sync_playwright
+import os
+from dotenv import load_dotenv
+from pathlib import Path
+
+def ensure_state_folder():
+ """Create state folder if it doesn't exist"""
+ state_folder = Path("state")
+ state_folder.mkdir(exist_ok=True)
+ return state_folder
+
+def get_state_path():
+ """Get the full path to the login state file"""
+ return Path("state/login_state.json")
+
+def login_and_save_state(username, password, phone_or_email, storage_path=None):
+ if storage_path is None:
+ storage_path = get_state_path()
+
+ with sync_playwright() as p:
+ # Ensure state folder exists
+ ensure_state_folder()
+
+ # If we already have a state file, we can return True
+ if storage_path.exists():
+ print(f"[api_free_connector] - {storage_path} exists. Using existing state.")
+ return True
+
+ browser = p.chromium.launch(headless=False, slow_mo=1000)
+ context = browser.new_context()
+ page = context.new_page()
+
+ # 1) Navigate to login
+ page.goto("https://x.com/login")
+ page.wait_for_timeout(1000)
+
+ # 2) Fill in username
+ page.wait_for_selector('input[name="text"]', timeout=10000)
+ page.fill('input[name="text"]', username)
+ page.keyboard.press("Enter")
+
+ try:
+ # Attempt normal login flow
+ page.wait_for_selector('input[name="password"]', timeout=5000) # reduced from 10000
+ page.fill('input[name="password"]', password)
+ page.keyboard.press("Enter")
+
+ # Wait for successful login
+ page.wait_for_url(lambda url: "home" in url, timeout=5000)
+ print("[api_free_connector] - Logged in successfully through normal flow.")
+
+ except:
+ print(f"[api_free_connector] - Normal login failed: Checking for unusual activity...")
+
+ try:
+ # Check for unusual activity challenge
+ page.wait_for_selector('input[data-testid="ocfEnterTextTextInput"]', timeout=3000)
+
+ # Fill in phone or username
+ page.fill('input[data-testid="ocfEnterTextTextInput"]', phone_or_email)
+ page.keyboard.press("Enter")
+ page.wait_for_load_state("networkidle")
+
+ # Enter password again after challenge
+ page.wait_for_selector('input[name="password"]', timeout=5000)
+ page.fill('input[name="password"]', password)
+ page.keyboard.press("Enter")
+
+ # Wait for successful login after challenge
+ page.wait_for_url(lambda url: "home" in url, timeout=5000)
+ print("[api_free_connector] - Logged in successfully after challenge.")
+
+ except:
+ print("[api_free_connector] - Error: Login failed - unusual activity flow could not be completed.")
+ browser.close()
+ return False
+
+ # Save the current browser context's storage state to a file
+ context.storage_state(path=str(storage_path))
+ print(f"[api_free_connector] - Storage state saved to {storage_path}.")
+
+ browser.close()
+ return True
+
+def post_tweet_with_saved_state(tweet_text, storage_path=None):
+ if storage_path is None:
+ storage_path = get_state_path()
+
+ with sync_playwright() as p:
+ # Create a new context with the previously saved state
+ browser = p.chromium.launch(headless=False)
+ context = browser.new_context(storage_state=str(storage_path))
+ page = context.new_page()
+
+ # Now page is already logged in if state.json is still valid
+ page.goto("https://x.com/home")
+ # Post a tweet
+ tweet_box_selector = 'div[data-testid="tweetTextarea_0"]'
+ page.wait_for_selector(tweet_box_selector, timeout=10000)
+ page.fill(tweet_box_selector, tweet_text)
+
+ # post_button_selector = 'div[data-testid="tweetButtonInline"]'
+ post_button_selector = 'button[data-testid="tweetButtonInline"]'
+ # page.wait_for_selector(post_button_selector, state="visible", timeout=20000) # Wait for visible
+
+ page.wait_for_selector(post_button_selector, timeout=10000)
+ page.click(post_button_selector)
+ page.wait_for_timeout(3000)
+ print(f"[Twitter API Free Connector] - Tweet posted: {tweet_text}")
+
+ browser.close()
+ return True
+
+if __name__ == "__main__":
+ """
+ Example usage. Replace the placeholders with your real credentials.
+ 'phone_or_username' is used if the suspicious activity screen appears
+ asking you to confirm via phone or username.
+ """
+ load_dotenv()
+
+ # Test post
+ X_POST_TEXT = "Hello"
+
+ # Ensure state folder exists and get state file path
+ state_path = get_state_path()
+
+ # check if login state exists
+ if state_path.exists():
+ print(f"[INFO] {state_path} exists. Using existing state.")
+ else:
+ print(f"[INFO] {state_path} does not exist. Logging in and saving state.")
+ login_and_save_state(
+ username=os.getenv("X_USERNAME"),
+ password=os.getenv("X_PASSWORD"),
+ phone_or_email=os.getenv("X_PHONE_OR_EMAIL"),
+ storage_path=state_path
+ )
+
+ post_tweet_with_saved_state(
+ tweet_text=X_POST_TEXT,
+ storage_path=state_path
+ )
diff --git a/server/connectors/twitter_connector.py b/server/connectors/twitter_connector.py
new file mode 100644
index 0000000..a8d02e5
--- /dev/null
+++ b/server/connectors/twitter_connector.py
@@ -0,0 +1,114 @@
+#
+# Module: twitter_connector
+#
+# This module implements the TwitterConnector class for interacting with Twitter APIs.
+#
+# Title: Twitter Connector
+# Summary: Twitter connector implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2024-12-31
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-04
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+
+import os
+import time
+import tweepy
+from dotenv import load_dotenv
+
+load_dotenv()
+
+class TwitterConnector:
+ """A connector for interacting with the Twitter API via Tweepy.
+
+ This class handles OAuth 1.0a and OAuth 2.0 authentication for
+ posting tweets and retrieving data from Twitter.
+
+ Attributes:
+ api_key (str): The API key for the Twitter API.
+ api_secret_key (str): The API secret key for the Twitter API.
+ access_token (str): The access token for the Twitter API.
+ access_token_secret (str): The access token secret for the Twitter API.
+ bearer_token (str): The bearer token for the Twitter API.
+ bot_username (str): The username of the bot.
+ bot_id (str): The ID of the bot.
+ """
+
+ def __init__(self):
+ """Initializes the Twitter connector.
+
+ Raises:
+ ValueError: If Twitter API credentials are not present in the environment variables.
+ """
+
+ self.api_key = os.getenv("TWITTER_API_KEY")
+ # Part of the OAuth 1.0a credentials identifying the application (required for user-based authentication).
+
+ self.api_secret_key = os.getenv("TWITTER_API_KEY_SECRET")
+ # Secret counterpart to the API key, used in signing OAuth 1.0a requests.
+
+ self.access_token = os.getenv("TWITTER_ACCESS_TOKEN")
+ # Represents the user’s OAuth 1.0a credentials, required for user-level actions (e.g., posting tweets).
+
+ self.access_token_secret = os.getenv("TWITTER_ACCESS_TOKEN_SECRET")
+ # Secret counterpart to the access token, used in signing user-level requests under OAuth 1.0a.
+
+ self.bearer_token = os.getenv("TWITTER_BEARER_TOKEN")
+ # Used for OAuth 2.0 app-only authentication in Twitter API v2, often for read-only access to public data.
+
+ if not all([self.api_key, self.api_secret_key, self.access_token, self.access_token_secret]):
+ raise ValueError("Twitter API credentials are not set in .env file")
+
+ try:
+ # Initialize v2 client only
+ self.client = tweepy.Client(
+ bearer_token=self.bearer_token,
+ consumer_key=self.api_key,
+ consumer_secret=self.api_secret_key,
+ access_token=self.access_token,
+ access_token_secret=self.access_token_secret,
+ wait_on_rate_limit=True
+ )
+
+ # Get the bot's info using v2 API
+ me = self.client.get_me()
+ self.bot_username = me.data.username.lower()
+ self.bot_id = me.data.id
+ print(f"Successfully authenticated as @{self.bot_username}")
+
+ self.last_mention_id = None
+ except Exception as e:
+ print(f"Twitter authentication failed: {str(e)}")
+ raise
+
+ def post_tweet(self, message: str) -> str:
+ """Posts a tweet to the bot's Twitter account.
+
+ Args:
+ message (str): The message to be posted as a tweet. Must be shorter than 280 characters.
+
+ Returns:
+ str: A string message indicating success or failure.
+
+ Raises:
+ Exception: If there's an error posting the tweet.
+
+ Example:
+ >>> twitter_connector.post_tweet("Hello, world!")
+ """
+ try:
+ if not message:
+ return "Error: Tweet message is empty"
+
+ # Truncate if too long
+ if len(message) > 280:
+ message = message[:277] + "..."
+
+ self.client.create_tweet(text=message)
+ return "Tweeted: " + message
+ except Exception as e:
+ return "Error posting tweet: " + str(e)
\ No newline at end of file
diff --git a/server/handlers/menu_handlers.py b/server/handlers/menu_handlers.py
new file mode 100644
index 0000000..e5923aa
--- /dev/null
+++ b/server/handlers/menu_handlers.py
@@ -0,0 +1,236 @@
+# standard imports
+import os
+import json
+
+# custom ARAI imports
+from utils import config_utils
+import prompt_chaining.step_1_create_agent as step_1
+import prompt_chaining.step_2_create_content as step_2
+import prompt_chaining.step_3_create_posts as step_3
+import prompt_chaining.step_4_create_profile_images as step_4
+import prompt_chaining.step_5_agent_chat as step_5
+
+def handle_create_agent(ai_model):
+ """Handle creating a new agent
+
+ Args:
+ ai_model: The AI model to use
+ """
+ agent_concept = input("\nProvide a concept for the new agent: ")
+
+ print("\nCreating new agent...")
+ agent_file_path = step_1.create_agent(ai_model, agent_concept)
+
+ set_tracker_post_every_x_minutes(agent_file_path, 30)
+
+ return agent_file_path
+
+def set_tracker_post_every_x_minutes(agent_file_path, post_every_x_minutes):
+ """Set the tracker post every x minutes for an agent
+
+ Args:
+ agent_file_path (str): Path to the agent's master file
+ post_every_x_minutes (int): The number of minutes to post every
+ """
+ agent_master_template = config_utils.load_agent_master_template(agent_file_path)
+
+ agent_master_template["agent"]["tracker"]["post_every_x_minutes"] = post_every_x_minutes
+
+ config_utils.save_agent_master_template(agent_master_template, agent_file_path)
+
+def handle_select_agent():
+ """Handle selecting an agent
+
+ Returns:
+ tuple: (selected agent name, agent file path)
+ """
+ agents = config_utils.list_available_agents()
+
+ # Filter out the temporary folder
+ agents = [agent for agent in agents if agent != "temporary"]
+
+ if not agents:
+ print("No agents found. Please create an agent first.")
+ return None, None
+
+ print("\nAvailable agents:")
+ for i, agent in enumerate(agents, 1):
+ print(f"{i}. {agent}")
+
+ while True:
+ try:
+ choice = int(input("\nSelect an agent (number): "))
+ if 1 <= choice <= len(agents):
+ selected_agent = agents[choice - 1]
+ agent_file_path = config_utils.get_agent_file_path(selected_agent)
+ print(f"Selected agent: {selected_agent}")
+ return selected_agent, agent_file_path
+ else:
+ print("Invalid choice. Please try again.")
+ except ValueError:
+ print("Please enter a valid number.")
+
+def handle_select_season(current_agent):
+ """Handle selecting a season for an agent
+
+ Args:
+ current_agent (str): The current agent's name
+ """
+ if not current_agent:
+ print("No agent selected. Please select an agent first.")
+ return
+
+ seasons = config_utils.list_available_seasons(current_agent)
+ if not seasons:
+ print("No seasons found for this agent.")
+ return
+
+ print("\nAvailable seasons:")
+ for i, season in enumerate(seasons, 1):
+ print(f"{i}. {season}")
+
+ while True:
+ try:
+ choice = int(input("\nSelect a season (number): "))
+ if 1 <= choice <= len(seasons):
+ selected_season = seasons[choice - 1]
+ print(f"Selected season: {selected_season}")
+ config_utils.display_season_details(current_agent, choice-1)
+ break
+ else:
+ print("Invalid choice. Please try again.")
+ except ValueError:
+ print("Please enter a valid number.")
+
+
+def handle_create_content(ai_model, current_agent, agent_file_path):
+ """Handle creating content for an agent
+
+ Args:
+ ai_model: The AI model to use
+ current_agent (str): The current agent's name
+ agent_file_path (str): Path to the agent's master file
+ """
+ if not current_agent:
+ print("No agent selected. Please select an agent first.")
+ return
+
+ print(f"\nCreating content for {current_agent}...")
+ step_2.create_seasons_and_episodes(ai_model, agent_file_path, 3)
+
+def handle_create_posts(ai_model, current_agent, agent_file_path):
+ """Handle creating posts for an agent
+
+ Args:
+ ai_model: The AI model to use
+ current_agent (str): The current agent's name
+ agent_file_path (str): Path to the agent's master file
+ """
+ if not current_agent:
+ print("No agent selected. Please select an agent first.")
+ return
+
+ print(f"\nCreating posts for {current_agent}...")
+ step_3.create_episode_posts(ai_model, agent_file_path, 6)
+
+def handle_create_profile_images(ai_model, current_agent, agent_file_path):
+ """Handle creating profile images for an agent
+
+ Args:
+ ai_model: The AI model to use
+ current_agent (str): The current agent's name
+ agent_file_path (str): Path to the agent's master file
+ """
+ if not current_agent:
+ print("No agent selected. Please select an agent first.")
+ return
+
+ prompt = input("\nDescribe what you want your agent to look like: ")
+
+ print(f"\nCreating profile images for {current_agent}...")
+ step_4.create_images(ai_model, prompt,agent_file_path, 4)
+
+def handle_chat_with_agent(ai_model, current_agent, agent_file_path):
+ """Handle chatting with an agent
+
+ Args:
+ ai_model: The AI model to use
+ current_agent (str): The name of the current agent
+ agent_file_path (str): Path to the agent's master file
+ """
+ if not current_agent:
+ print("No agent selected. Please select an agent first.")
+ return
+
+ print("--------------------------------")
+ print(f"\nStarting chat with {current_agent}...")
+ print("--------------------------------")
+ print("\n")
+
+ print(f"Current agent: {current_agent}")
+
+ # loop through chat history and print each message
+ chat_history = config_utils.load_chat_history(current_agent)
+
+ # print chat history
+ if chat_history["chat_history"]:
+ for message in chat_history["chat_history"]:
+ content = message.get('prompt') if 'prompt' in message else message.get('response')
+ print(f"{message['role']}: {content}")
+ print("\n")
+
+ while True:
+ prompt = input("\nEnter your message (or 'exit' to end chat): ")
+ if prompt.lower() == 'exit':
+ break
+
+ response, chat_history = step_5.agent_chat(ai_model, agent_file_path, prompt, chat_history)
+ if response:
+ print(f"\n{current_agent}: {response['response']}")
+
+
+def handle_manage_scheduler(scheduler, current_agent, post_manager):
+ """Handle scheduler management options
+
+ Args:
+ scheduler: The scheduler instance
+ current_agent (str): The current agent's name
+ """
+ if not current_agent:
+ print("No agent selected. Please select an agent first.")
+ return
+
+ print("\nScheduler Management:")
+ print("1. Start scheduler")
+ print("2. Stop scheduler")
+ print("3. Pause/Resume scheduler")
+ print("4. Back to main menu")
+
+ choice = input("\nEnter your choice (1-4): ")
+
+ if choice == "1":
+ if not scheduler.is_running():
+ scheduler.start()
+ scheduler.schedule_posts(
+ post_manager=post_manager,
+ tracker_data=config_utils.load_agent_tracker_config(current_agent),
+ )
+ print("Scheduler started.")
+ else:
+ print("Scheduler is already running.")
+ elif choice == "2":
+ if scheduler.is_running():
+ scheduler.stop()
+ print("Scheduler stopped.")
+ else:
+ print("Scheduler is not running.")
+ elif choice == "3":
+ if scheduler.is_running():
+ if scheduler.is_paused():
+ scheduler.resume()
+ print("Scheduler resumed.")
+ else:
+ scheduler.pause()
+ print("Scheduler paused.")
+ else:
+ print("Scheduler is not running.")
diff --git a/server/main.py b/server/main.py
new file mode 100644
index 0000000..d44c8f5
--- /dev/null
+++ b/server/main.py
@@ -0,0 +1,96 @@
+#
+# Module: main
+#
+# This module implements the main function for the ARAI Agents application.
+#
+# Title: ARAI Agents
+# Summary: ARAI Agents implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2024-12-31
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-04
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+
+# standard imports
+import os
+import json
+
+# custom ARAI imports
+from models.gemini_model import GeminiModel
+from models.openai_model import OpenAIModel
+from models.claude_model import ClaudeModel
+from models.deepseek_model import DeepSeekModel
+from utils.scheduler import AgentScheduler
+from handlers import menu_handlers
+from utils.post_manager import PostManager
+
+def main():
+ """Main function for the ARAI Agents application"""
+ # Initialize the AI model and scheduler
+ ai_model = GeminiModel(model_name="gemini-1.5-flash-latest")
+ # ai_model = OpenAIModel()
+ # ai_model = ClaudeModel()
+ # ai_model = DeepSeekModel()
+
+ scheduler = AgentScheduler()
+ post_manager = None
+
+ # Initialize variables
+ current_agent = None
+ agent_file_path = None
+
+ while True:
+ print("\nARAI Agents Menu:")
+ print("\n= Agent Management =")
+ print("1. Create New Agent")
+ print("2. Select Agent")
+ print("3. Social Feed")
+
+ print("\n= Content Creation =")
+ print("4. Create Content")
+ print("5. Create Posts")
+ print("6. Create Profile Images")
+
+ print("\n= Scheduler =")
+ print("7. Manage Scheduler")
+
+ print("\n= Chat =")
+ print("8. Chat with Agent")
+
+ print("\n= Exit =")
+ print("9. Exit")
+
+ choice = input("\nEnter your choice (1-9): ")
+
+ if choice == "1":
+ agent_file_path = menu_handlers.handle_create_agent(ai_model)
+ elif choice == "2":
+ current_agent, agent_file_path = menu_handlers.handle_select_agent()
+ post_manager = PostManager(current_agent)
+ elif choice == "3":
+ # update this to social media feed - prints off all the posts for the season
+ menu_handlers.handle_select_season(current_agent)
+ elif choice == "4":
+ menu_handlers.handle_create_content(ai_model, current_agent, agent_file_path)
+ elif choice == "5":
+ menu_handlers.handle_create_posts(ai_model, current_agent, agent_file_path)
+ elif choice == "6":
+ menu_handlers.handle_create_profile_images(ai_model, current_agent, agent_file_path)
+ elif choice == "7":
+ menu_handlers.handle_manage_scheduler(scheduler, current_agent, post_manager)
+ elif choice == "8":
+ menu_handlers.handle_chat_with_agent(ai_model, current_agent, agent_file_path)
+ elif choice == "9":
+ if scheduler.is_running():
+ scheduler.stop()
+ print("Goodbye!")
+ break
+ else:
+ print("Invalid choice. Please try again.")
+
+if __name__ == "__main__":
+ main()
diff --git a/server/models/__init__.py b/server/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/server/models/base_model.py b/server/models/base_model.py
new file mode 100644
index 0000000..c91bb60
--- /dev/null
+++ b/server/models/base_model.py
@@ -0,0 +1,36 @@
+#
+# Module: base_model
+#
+# This module implements the ModelInterface class for interacting with ai apis.
+#
+# Title: Base Model
+# Summary: Base model implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2024-12-31
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-04
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+from abc import ABC, abstractmethod # Import the ABC class and abstractmethod from the abc module
+
+class ModelInterface(ABC):
+ """Base class for all models.
+ """
+ @abstractmethod
+ def generate_response(self, prompt: str) -> str:
+ """ Generate a response to a given prompt.
+
+ Args:
+ prompt (str): The prompt to generate a response to.
+
+ Returns:
+ str: The generated response.
+
+ Example:
+ >>> base_model.generate_response("Hello, world!")
+ """
+
+ pass
\ No newline at end of file
diff --git a/server/models/claude_model.py b/server/models/claude_model.py
new file mode 100644
index 0000000..92ec1cc
--- /dev/null
+++ b/server/models/claude_model.py
@@ -0,0 +1,171 @@
+import os
+import anthropic
+from .base_model import ModelInterface
+from dotenv import load_dotenv
+import sys
+import json
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+load_dotenv()
+
+class ClaudeModel(ModelInterface):
+ """DeepSeek model implementation.
+
+ Attributes:
+ model (str): The name of the DeepSeek model to use.
+ """
+
+ def __init__(self, my_api_key=None, model_name="claude-3-5-sonnet-20241022"):
+ """Initialize the DeepSeek model.
+
+ Args:
+ api_key (str): The API key to use for the DeepSeek model.
+ model_name (str): The name of the DeepSeek model to use.
+
+ Example:
+ >>> deepseek_model = DeepSeekModel()
+ """
+
+ self.model_name = model_name
+
+ if my_api_key:
+ self.client = client = anthropic.Anthropic(
+ # defaults to os.environ.get("ANTHROPIC_API_KEY")
+ api_key=my_api_key,
+ )
+ else:
+ self.client = anthropic.Anthropic(
+ api_key=os.environ.get("ANTHROPIC_API_KEY")
+ )
+
+ # -------------------------------------------------------------------
+ # Helper to generate a response to a given prompt using the OpenAI API.
+ # -------------------------------------------------------------------
+ def generate_response(self, prompt, **kwargs):
+ """Generate a response to a given prompt using the DeepSeek API.
+
+ Args:
+ prompt (str): The prompt to generate a response to.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ str: The generated response.
+
+ Example:
+ >>> deepseek_model = DeepSeekModel()
+ >>> response = deepseek_model.generate_response("What is the weather in Tokyo?")
+ """
+ if isinstance(prompt, str):
+ return self.generate_response_from_string(prompt, **kwargs)
+ elif isinstance(prompt, list[dict]):
+ return self.generate_response_dictionary(prompt)
+
+ # -------------------------------------------------------------------
+ # Helper to generate a response to a given prompt using a list of dictionaries
+ # -------------------------------------------------------------------
+ def generate_response_dictionary(self, prompt: list[dict]) -> str:
+ """Generate a response to a given prompt using a list of dictionaries.
+
+ Args:
+ prompt (list[dict]): The prompt to generate a response to.
+
+ Returns:
+ str: The generated response.
+
+ Example:
+ >>> deepseek_model = DeepSeekModel()
+ >>> response = deepseek_model.generate_response_dictionary([{"role": "user", "parts": "What is the weather in Tokyo?"}])
+ """
+ try:
+ response = self.client.messages.create( # Create a chat completion with the OpenAI API
+ model=self.model_name, # Set the model type we want to use
+ messages=prompt, # Set the prompt we want to use
+ max_tokens=1024
+ )
+
+ return response.content[0].text if isinstance(response.content, list) else response.content
+ except Exception as e:
+ return f"Error generating response: {str(e)}"
+
+
+
+ # -------------------------------------------------------------------
+ # Helper to generate a response to a given prompt using a string
+ # -------------------------------------------------------------------
+ def generate_response_from_string(self, prompt, **kwargs):
+ """
+ Description:
+ Generate a response to a given prompt using a string.
+
+ Args:
+ prompt (str): The prompt to generate a response to.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ str: The generated response.
+
+ Example:
+ >>> deepseek_model = DeepSeekModel()
+ >>> response = deepseek_model.generate_response_from_string("What is the weather in Tokyo?")
+ """
+ # Extract personality and style from kwargs, or use defaults from agent_template
+ if kwargs:
+ if "personality" in kwargs:
+ personality = kwargs.get("personality")
+ if "communication_style" in kwargs:
+ communication_style = kwargs.get("communication_style")
+ else:
+ personality = ""
+ communication_style = ""
+
+ try:
+ # instructions being sent to the ai model
+ messages = []
+
+ # add personality and style to the instructions
+ if personality or communication_style:
+ persona_prompt = f"{personality} {communication_style}".strip()
+ messages.append({
+ "role": "system",
+ "content": persona_prompt
+ })
+
+ # User message
+ messages.append({
+ "role": "user",
+ "content": prompt
+ })
+
+ # generate the response
+ # generate the response
+ # response = self.client.messages.create( # Create a chat completion with the OpenAI API
+ # model=self.model_name, # Set the model type we want to use
+ # messages=messages, # Set the prompt we want to use
+ # temperature=0.7, # Set the temperature of the response
+ # max_tokens=400, # Set the maximum number of tokens to generate
+ # )
+ response = self.client.messages.create( # Create a chat completion with the OpenAI API
+ model=self.model_name, # Set the model type we want to use
+ messages=messages, # Set the prompt we want to use
+ max_tokens=1024
+ )
+
+ return response.content[0].text if isinstance(response.content, list) else response.content
+
+ except Exception as e:
+ return f"Error generating response: {str(e)}"
+
+
+if __name__ == "__main__":
+ deepseek_model = DeepSeekModel()
+ response = deepseek_model.generate_response("Tell me 10 one liners about crypto. put them as a json object")
+ # Extract just the text content from the response
+ if hasattr(response, 'text'):
+ print("\nClaude's Response:\n")
+ print(response.text)
+ print("\n")
+ else:
+ print("\nClaude's Response:\n")
+ print(response)
+ print("\n")
diff --git a/server/models/deepseek_model.py b/server/models/deepseek_model.py
new file mode 100644
index 0000000..06840c7
--- /dev/null
+++ b/server/models/deepseek_model.py
@@ -0,0 +1,172 @@
+#
+# Module: deepseek_model
+#
+# This module implements the DeepSeekModel class for interacting with the DeepSeek API.
+#
+# Title: DeepSeek Model
+# Summary: DeepSeek model implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2025-01-22
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-22
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+
+import os
+from openai import OpenAI # Please install OpenAI SDK first: `pip3 install openai`
+from .base_model import ModelInterface
+from dotenv import load_dotenv
+import sys
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+load_dotenv()
+
+class DeepSeekModel(ModelInterface):
+ """DeepSeek model implementation.
+
+ Attributes:
+ model (str): The name of the DeepSeek model to use.
+ """
+
+ def __init__(self, my_api_key=None, model_name="deepseek-chat"):
+ """Initialize the DeepSeek model.
+
+ Args:
+ api_key (str): The API key to use for the DeepSeek model.
+ model_name (str): The name of the DeepSeek model to use.
+
+ Example:
+ >>> deepseek_model = DeepSeekModel()
+ """
+
+ self.model_name = model_name
+
+ if my_api_key:
+ self.client = OpenAI(api_key=my_api_key, base_url="https://api.deepseek.com")
+ else:
+ self.client = OpenAI(api_key=os.environ.get('DEEPSEEK_API_KEY'), base_url="https://api.deepseek.com")
+
+ # -------------------------------------------------------------------
+ # Helper to generate a response to a given prompt using the OpenAI API.
+ # -------------------------------------------------------------------
+ def generate_response(self, prompt, **kwargs):
+ """Generate a response to a given prompt using the DeepSeek API.
+
+ Args:
+ prompt (str): The prompt to generate a response to.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ str: The generated response.
+
+ Example:
+ >>> deepseek_model = DeepSeekModel()
+ >>> response = deepseek_model.generate_response("What is the weather in Tokyo?")
+ """
+ if isinstance(prompt, str):
+ return self.generate_response_from_string(prompt, **kwargs)
+ elif isinstance(prompt, list[dict]):
+ return self.generate_response_dictionary(prompt)
+
+ # -------------------------------------------------------------------
+ # Helper to generate a response to a given prompt using a list of dictionaries
+ # -------------------------------------------------------------------
+ def generate_response_dictionary(self, prompt: list[dict]) -> str:
+ """Generate a response to a given prompt using a list of dictionaries.
+
+ Args:
+ prompt (list[dict]): The prompt to generate a response to.
+
+ Returns:
+ str: The generated response.
+
+ Example:
+ >>> deepseek_model = DeepSeekModel()
+ >>> response = deepseek_model.generate_response_dictionary([{"role": "user", "parts": "What is the weather in Tokyo?"}])
+ """
+ try:
+ response = self.client.chat.completions.create( # Create a chat completion with the OpenAI API
+ model=self.model_name, # Set the model type we want to use
+ messages=prompt # Set the prompt we want to use
+ )
+
+ return response.choices[0].message.content.strip()
+ except Exception as e:
+ return f"Error generating response: {str(e)}"
+
+
+
+ # -------------------------------------------------------------------
+ # Helper to generate a response to a given prompt using a string
+ # -------------------------------------------------------------------
+ def generate_response_from_string(self, prompt, **kwargs):
+ """
+ Description:
+ Generate a response to a given prompt using a string.
+
+ Args:
+ prompt (str): The prompt to generate a response to.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ str: The generated response.
+
+ Example:
+ >>> deepseek_model = DeepSeekModel()
+ >>> response = deepseek_model.generate_response_from_string("What is the weather in Tokyo?")
+ """
+ # Extract personality and style from kwargs, or use defaults from agent_template
+ if kwargs:
+ if "personality" in kwargs:
+ personality = kwargs.get("personality")
+ if "communication_style" in kwargs:
+ communication_style = kwargs.get("communication_style")
+ else:
+ personality = ""
+ communication_style = ""
+
+ try:
+ # instructions being sent to the ai model
+ messages = []
+
+ # add personality and style to the instructions
+ if personality or communication_style:
+ persona_prompt = f"{personality} {communication_style}".strip()
+ messages.append({
+ "role": "system",
+ "content": persona_prompt
+ })
+
+ # User message
+ messages.append({
+ "role": "user",
+ "content": prompt
+ })
+
+ # generate the response
+ # generate the response
+ # response = self.client.chat.completions.create( # Create a chat completion with the OpenAI API
+ # model=self.model_name, # Set the model type we want to use
+ # messages=messages, # Set the prompt we want to use
+ # temperature=0.7, # Set the temperature of the response
+ # max_tokens=400, # Set the maximum number of tokens to generate
+ # )
+ response = self.client.chat.completions.create( # Create a chat completion with the OpenAI API
+ model=self.model_name, # Set the model type we want to use
+ messages=messages # Set the prompt we want to use
+ )
+
+ return response.choices[0].message.content.strip()
+
+ except Exception as e:
+ return f"Error generating response: {str(e)}"
+
+
+if __name__ == "__main__":
+ deepseek_model = DeepSeekModel()
+ response = deepseek_model.generate_response("Tell me 10 one liners about crypto. put them as a json object")
+ print(response)
diff --git a/server/models/gemini_model.py b/server/models/gemini_model.py
new file mode 100644
index 0000000..bc37dc4
--- /dev/null
+++ b/server/models/gemini_model.py
@@ -0,0 +1,156 @@
+#
+# Module: gemini_model
+#
+# This module implements the GeminiModel class for interacting with the Gemini API.
+#
+# Title: Gemini Model
+# Summary: Gemini model implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2024-12-31
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-04
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+import os
+import google.generativeai as genai
+from .base_model import ModelInterface
+from dotenv import load_dotenv
+import yaml
+import sys
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+load_dotenv()
+
+class GeminiModel(ModelInterface):
+ """Gemini model implementation.
+
+ Attributes:
+ model (str): The name of the Gemini model to use.
+ """
+
+ def __init__(self, my_api_key=None, model_name="gemini-exp-1206"):
+ """Initialize the Gemini model.
+
+ Args:
+ api_key (str): The API key to use for the Gemini model.
+ model_name (str): The name of the Gemini model to use.
+
+ Example:
+ >>> gemini_model = GeminiModel()
+ """
+ if my_api_key:
+ genai.configure(api_key=my_api_key)
+ else:
+ genai.configure(api_key=os.environ.get('GOOGLE_GEMINI_API_KEY'))
+ self.model = genai.GenerativeModel(model_name)
+
+ # -------------------------------------------------------------------
+ # Helper to generate a response to a given prompt using the Gemini API.
+ # -------------------------------------------------------------------
+ def generate_response(self, prompt, **kwargs):
+ """Generate a response to a given prompt using the Gemini API.
+
+ Args:
+ prompt (str): The prompt to generate a response to.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ str: The generated response.
+
+ Example:
+ >>> gemini_model = GeminiModel()
+ >>> response = gemini_model.generate_response("What is the weather in Tokyo?")
+ """
+ if isinstance(prompt, str):
+ return self.generate_response_from_string(prompt, **kwargs)
+ elif isinstance(prompt, list[dict]):
+ return self.generate_response_dictionary(prompt)
+
+ # -------------------------------------------------------------------
+ # Helper to generate a response to a given prompt using a list of dictionaries
+ # -------------------------------------------------------------------
+ def generate_response_dictionary(self, prompt: list[dict]) -> str:
+ """Generate a response to a given prompt using a list of dictionaries.
+
+ Args:
+ prompt (list[dict]): The prompt to generate a response to.
+
+ Returns:
+ str: The generated response.
+
+ Example:
+ >>> gemini_model = GeminiModel()
+ >>> response = gemini_model.generate_response_dictionary([{"role": "user", "parts": "What is the weather in Tokyo?"}])
+ """
+ try:
+ response = self.model.generate_content(prompt)
+ return response.text.strip()
+ except Exception as e:
+ return f"Error generating response: {str(e)}"
+
+ # -------------------------------------------------------------------
+ # Helper to generate a response to a given prompt using a string
+ # -------------------------------------------------------------------
+ def generate_response_from_string(self, prompt, **kwargs):
+ """
+ Description:
+ Generate a response to a given prompt using a string.
+
+ Args:
+ prompt (str): The prompt to generate a response to.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ str: The generated response.
+
+ Example:
+ >>> gemini_model = GeminiModel()
+ >>> response = gemini_model.generate_response_from_string("What is the weather in Tokyo?")
+ """
+ # Extract personality and style from kwargs, or use defaults from agent_template
+ if kwargs:
+ if "personality" in kwargs:
+ personality = kwargs.get("personality")
+ if "communication_style" in kwargs:
+ communication_style = kwargs.get("communication_style")
+ else:
+ personality = ""
+ communication_style = ""
+
+ try:
+ # instructions being sent to the ai model
+ messages = []
+
+ # add personality and style to the instructions
+ if personality or communication_style:
+ persona_prompt = f"{personality} {communication_style}"
+ messages.append({
+ "role": "user",
+ "parts": [persona_prompt]
+ })
+
+ # user message
+ messages.append({
+ "role": "user",
+ "parts": [prompt]
+ })
+
+ # Make sure that what is being sent to the model is correct
+ # print(messages)
+
+ # generate the response
+ response = self.model.generate_content(messages)
+ return response.text.strip()
+
+ except Exception as e:
+ return f"Error generating response: {str(e)}"
+
+if __name__ == "__main__":
+ gemini_model = GeminiModel()
+ response = gemini_model.generate_response("Tell me 10 one liners about crypto. put them as a json object")
+ print(response)
+
diff --git a/server/models/openai_model.py b/server/models/openai_model.py
new file mode 100644
index 0000000..a7e95cc
--- /dev/null
+++ b/server/models/openai_model.py
@@ -0,0 +1,144 @@
+import os # Os is used for interacting with the operating system
+from openai import OpenAI # OpenAI is used for interacting with the OpenAI API
+from .base_model import ModelInterface # Used for agent interface
+from dotenv import load_dotenv # Used for loading environment variables
+
+load_dotenv() # Load environment variables from .env file
+
+class OpenAIModel(ModelInterface): # OpenAIModel inherits from ModelInterface
+ '''
+ OpenAI model implementation.
+ '''
+
+ def __init__(self, my_api_key=None, model_name="gpt-4o"):
+ '''
+ Initialize the OpenAI model.
+ '''
+
+ # ai model setup
+ self.model_name = model_name # Set the model type we want to use
+ if my_api_key:
+ self.client = OpenAI(api_key=my_api_key)
+ else:
+ self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+ # -------------------------------------------------------------------
+ # Helper to generate a response to a given prompt using the OpenAI API.
+ # -------------------------------------------------------------------
+ def generate_response(self, prompt, **kwargs):
+ """Generate a response to a given prompt using the OpenAI API.
+
+ Args:
+ prompt (str): The prompt to generate a response to.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ str: The generated response.
+
+ Example:
+ >>> openai_model = OpenAIModel()
+ >>> response = openai_model.generate_response("What is the weather in Tokyo?")
+ """
+ if isinstance(prompt, str):
+ return self.generate_response_from_string(prompt, **kwargs)
+ elif isinstance(prompt, list[dict]):
+ return self.generate_response_dictionary(prompt)
+
+ # -------------------------------------------------------------------
+ # Helper to generate a response to a given prompt using a list of dictionaries
+ # -------------------------------------------------------------------
+ def generate_response_dictionary(self, prompt: list[dict]) -> str:
+ """Generate a response to a given prompt using a list of dictionaries.
+
+ Args:
+ prompt (list[dict]): The prompt to generate a response to.
+
+ Returns:
+ str: The generated response.
+
+ Example:
+ >>> openai_model = OpenAIModel()
+ >>> response = openai_model.generate_response_dictionary([{"role": "user", "parts": "What is the weather in Tokyo?"}])
+ """
+ try:
+ response = self.client.chat.completions.create( # Create a chat completion with the OpenAI API
+ model=self.model_name, # Set the model type we want to use
+ messages=prompt # Set the prompt we want to use
+ )
+
+ return response.choices[0].message.content.strip()
+ except Exception as e:
+ return f"Error generating response: {str(e)}"
+
+
+
+ # -------------------------------------------------------------------
+ # Helper to generate a response to a given prompt using a string
+ # -------------------------------------------------------------------
+ def generate_response_from_string(self, prompt, **kwargs):
+ """
+ Description:
+ Generate a response to a given prompt using a string.
+
+ Args:
+ prompt (str): The prompt to generate a response to.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ str: The generated response.
+
+ Example:
+ >>> openai_model = OpenAIModel()
+ >>> response = openai_model.generate_response_from_string("What is the weather in Tokyo?")
+ """
+ # Extract personality and style from kwargs, or use defaults from agent_template
+ if kwargs:
+ if "personality" in kwargs:
+ personality = kwargs.get("personality")
+ if "communication_style" in kwargs:
+ communication_style = kwargs.get("communication_style")
+ else:
+ personality = ""
+ communication_style = ""
+
+ try:
+ # instructions being sent to the ai model
+ messages = []
+
+ # add personality and style to the instructions
+ if personality or communication_style:
+ persona_prompt = f"{personality} {communication_style}".strip()
+ messages.append({
+ "role": "system",
+ "content": persona_prompt
+ })
+
+ # User message
+ messages.append({
+ "role": "user",
+ "content": prompt
+ })
+
+ # generate the response
+ # generate the response
+ # response = self.client.chat.completions.create( # Create a chat completion with the OpenAI API
+ # model=self.model_name, # Set the model type we want to use
+ # messages=messages, # Set the prompt we want to use
+ # temperature=0.7, # Set the temperature of the response
+ # max_tokens=400, # Set the maximum number of tokens to generate
+ # )
+ response = self.client.chat.completions.create( # Create a chat completion with the OpenAI API
+ model=self.model_name, # Set the model type we want to use
+ messages=messages # Set the prompt we want to use
+ )
+
+ return response.choices[0].message.content.strip()
+
+ except Exception as e:
+ return f"Error generating response: {str(e)}"
+
+
+if __name__ == "__main__":
+ openai_model = OpenAIModel()
+ response = openai_model.generate_response("Tell me 10 one liners about crypto. put them as a json object")
+ print(response)
diff --git a/server/prompt_chaining/__init__.py b/server/prompt_chaining/__init__.py
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/server/prompt_chaining/__init__.py
@@ -0,0 +1 @@
+
diff --git a/server/prompt_chaining/step_1_create_agent.py b/server/prompt_chaining/step_1_create_agent.py
new file mode 100644
index 0000000..d0c563a
--- /dev/null
+++ b/server/prompt_chaining/step_1_create_agent.py
@@ -0,0 +1,150 @@
+#
+# Module: step_1
+#
+# This module implements the step_1 function for creating a new agent.
+#
+# Title: Step 1
+# Summary: Step 1 implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2025-01-02
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-09
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+
+# standard imports
+import yaml
+import json
+import sys
+import os
+import time
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# custom ARAI imports
+from utils.content_generator import ContentGenerator
+import prompt_chaining.step_2_create_content as next_step
+from utils.template_types import TemplateType
+
+# -------------------------------------------------------------------
+# Step 1: Create a new agent
+# -------------------------------------------------------------------
+def create_agent(ai_model, concept: str):
+ '''
+ Description:
+ Create a new agent
+
+ Args:
+ ai_model: The AI model to use for generating responses
+ debug (bool, optional): whether to print debug information. Defaults to False.
+
+ Returns:
+ agent_file_path: The path to the agent yaml file
+
+ Raises:
+ Exception: If there's an error creating the agent
+
+ Example:
+ >>> ai_model = GeminiModel()
+ >>> step_1(ai_model, "alien drone pilot who is a sarcastic asshole visiting earth to report back his findings to his home planet")
+ '''
+
+ # Step 1.1: Create a new agent
+ manager = ContentGenerator()
+ # agent_template = manager.create_new_template_yaml(TemplateType.AGENT)
+ agent_template = manager.create_new_template_json(TemplateType.AGENT)
+ agent_master_template = manager.create_new_template_json(TemplateType.MASTER)
+
+ # Gather a list of agent names based on folder names in config folder
+ agent_names_blacklist = manager.get_agent_names_blacklist()
+ # remove _ from names
+ # print(f"agent_names_blacklist: {agent_names_blacklist}")
+ # agent_names_blacklist = [name.replace("_", " ") for name in agent_names_blacklist]
+ print(f"agent_names_blacklist: {agent_names_blacklist}")
+
+ # step 1.2: Generate a new agent name, topic, personality, and communication style with the prompt_1 template
+ # prompt 1 Character Creation:
+ print("Crafting prompt for AI to create a new agent")
+ prompt_1_vars = {
+ # "agent_name": "",
+ # "personality": "",
+ # "communication_style": "",
+ # "topic": "",
+ "concept": concept,
+ "agent_names_blacklist": agent_names_blacklist,
+ "agent_json": json.dumps(agent_template)
+ }
+
+ # Constants for retry configuration
+ max_retries = 3
+ delay = 2 # seconds between retries
+
+ # step 3.11: Run the prompt with retry logic for LLM failures
+ success = False
+ for attempt in range(max_retries):
+ try:
+ # step 1.3: Run the prompt
+ print("Sending prompt to AI to create a new agent")
+ agent_data = manager.run_prompt(
+ prompt_key="prompt_1 (Character Sheet Creation)",
+ template_vars=prompt_1_vars,
+ ai_model=ai_model,
+ debug=False
+ )
+
+ # Validate that posts_data is valid JSON and has expected structure
+ if isinstance(agent_data, dict) and 'agent' in agent_data:
+ success = True
+ break
+ else:
+ print(f"Invalid response format. Attempt {attempt + 1}/{max_retries}")
+
+ except (json.JSONDecodeError, Exception) as e:
+ print(f"Error on attempt {attempt + 1}/{max_retries}: {str(e)}")
+ if attempt < max_retries - 1:
+ print(f"Retrying in {delay} seconds...")
+ time.sleep(delay)
+ continue
+
+ if not success:
+ print("Max retries reached. Failed to get valid response from LLM.")
+ return None
+
+ # step 1.4: Merge agent details into the master template
+ print("Merging agent details into the master template")
+ agent_master_template = manager.merge_agent_details(
+ master_data=agent_master_template,
+ agent_data=agent_data
+ )
+
+ # step 1.5: store the concept in the agent template
+ print("Storing the concept in the agent template")
+ agent_master_template["agent"]["concept"] = prompt_1_vars["concept"]
+
+ # step 1.6: create the file path for master file
+ print("Creating the file path for the master file")
+ agent_master_file_path = manager.create_filepath(
+ agent_name=agent_master_template["agent"]["agent_details"]["name"],
+ season_number=0,
+ episode_number=0,
+ template_type=TemplateType.MASTER
+ )
+
+ # step 1.7: Save the agent data to a file
+ print("Saving the agent data to a file")
+ manager.save_json_file(
+ save_path=agent_master_file_path,
+ json_data=agent_master_template
+ )
+
+ print("Step 1 complete")
+ return agent_master_file_path
+
+
+import models.gemini_model as gemini_model
+if __name__ == "__main__":
+ ai_model = gemini_model.GeminiModel()
+ create_agent(ai_model, "time-traveling historian documenting the evolution of human technology through the ages")
+
diff --git a/server/prompt_chaining/step_2_create_content.py b/server/prompt_chaining/step_2_create_content.py
new file mode 100644
index 0000000..e111af6
--- /dev/null
+++ b/server/prompt_chaining/step_2_create_content.py
@@ -0,0 +1,162 @@
+#
+# Module: step_2
+#
+# This module implements the step_2 function for creating a new season for an agent.
+#
+# Title: Step 2
+# Summary: Step 2 implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2025-01-02
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-09
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+
+# standard imports
+import os
+import json
+import json
+import sys
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# custom ARAI imports
+from utils.content_generator import ContentGenerator
+from utils.template_types import TemplateType
+import prompt_chaining.step_3_create_posts as next_step
+
+def create_seasons_and_episodes(ai_model, master_file_path, number_of_episodes):
+ '''
+ Description:
+ Create a new season for the agent
+
+ Args:
+ ai_model: The AI model to use for generating responses
+ master_file_path: The path to the agent master json file
+
+ Returns:
+ season_file_path: The path to the season json file
+
+ Raises:
+ Exception: If there's an error creating the season
+
+ Example:
+ >>> ai_model = GeminiModel()
+ >>> master_file_path = "agent_template.json"
+ >>> step_2(ai_model, master_file_path)
+ '''
+ print("Step 2: Create a new season")
+
+ # step 2.1: load the season template json file
+ manager = ContentGenerator()
+ #season_template = manager.create_new_template_yaml(TemplateType.SEASON)
+ print(f"Creating a new season template")
+ season_template = manager.create_new_template_json(TemplateType.SEASON)
+
+ # step 2.2: load the agent json file
+ print(f"Loading agent master json file")
+ agent_master_json = None
+ with open(master_file_path, 'r', encoding='utf-8') as file:
+ agent_master_json = json.load(file)
+
+ # extract agent from master json
+ print(f"Extracting agent details from master json")
+ agent_details = agent_master_json['agent']['agent_details']
+
+
+ # if the seasons array is empty or None, we need to initialize it
+ if not agent_master_json['agent']['seasons'] or agent_master_json['agent']['seasons'] == []:
+ agent_master_json['agent']['seasons'] = season_template['seasons']
+
+ # step 2.3: find the previous season
+ # get last season file
+ previous_season = None
+ # Get the last season from the seasons array if it exists
+ seasons = agent_master_json['agent']['seasons']
+ previous_season = seasons[-1] if seasons else None
+
+ if previous_season:
+ print(f"Found previous season: {previous_season['season_number']}")
+ else:
+ print("No previous seasons found")
+
+ # step 2.3: Generate a new season name, topic, and communication style with the prompt_2 template
+ # prompt 2 Season Creation:
+ # note that emojis will be output as unicode characters due to the json dump
+ print("Crafting prompt for AI to create a new season")
+ prompt_2_vars = {
+ "agent_name": agent_details["name"],
+ "agent_json": json.dumps(agent_details),
+ "season_json": json.dumps(season_template),
+ "previous_season": json.dumps(previous_season),
+ "number_of_episodes": number_of_episodes
+ }
+
+ # Constants for retry configuration
+ max_retries = 3
+ delay = 2 # seconds between retries
+
+ # step 3.11: Run the prompt with retry logic for LLM failures
+ success = False
+ for attempt in range(max_retries):
+ try:
+ # step 2.4: Run the prompt
+ print("Sending prompt to AI to create a new season")
+ season_data = manager.run_prompt(
+ # prompt_key="prompt_1 (Character Creation)",
+ prompt_key="prompt_2 (Season Creation)",
+ template_vars=prompt_2_vars,
+ ai_model=ai_model,
+ )
+
+ # Validate that posts_data is valid JSON and has expected structure
+ if isinstance(season_data, dict) and 'seasons' in season_data:
+ success = True
+ break
+ else:
+ print(f"Invalid response format. Attempt {attempt + 1}/{max_retries}")
+
+ except (json.JSONDecodeError, Exception) as e:
+ print(f"Error on attempt {attempt + 1}/{max_retries}: {str(e)}")
+ if attempt < max_retries - 1:
+ print(f"Retrying in {delay} seconds...")
+ time.sleep(delay)
+ continue
+
+ if not success:
+ print("Max retries reached. Failed to get valid response from LLM.")
+ return None
+
+ #print(f"season data is: {season_data}")
+
+ # step 2.8: append the season data to the master data
+ if len(seasons) <= 1 and seasons[0]["season_number"] == 0:
+ print("First season, so we need to initialize the seasons array")
+ agent_master_json = manager.initialize_seasons(
+ master_data=agent_master_json,
+ seasons_data=season_data
+ )
+ else:
+ print("Appending the season data to the master data")
+ agent_master_json = manager.append_seasons(
+ master_data=agent_master_json,
+ seasons_data=season_data,
+ )
+
+ # step 2.9: save the master data to a file
+ print("Saving the master data to a file")
+ manager.save_json_file(
+ save_path=master_file_path,
+ json_data=agent_master_json
+ )
+
+ print("Step 2 complete")
+ #return season_file_path
+
+import models.gemini_model as gemini_model
+if __name__ == "__main__":
+ ai_model = gemini_model.GeminiModel()
+ create_seasons_and_episodes(ai_model, "configs/CipherCat/CipherCat_master.json", 3)
\ No newline at end of file
diff --git a/server/prompt_chaining/step_3_create_posts.py b/server/prompt_chaining/step_3_create_posts.py
new file mode 100644
index 0000000..b52ccd9
--- /dev/null
+++ b/server/prompt_chaining/step_3_create_posts.py
@@ -0,0 +1,161 @@
+#
+# Module: step_3
+#
+# This module implements the step_3 function for creating a new episode posts for an agent.
+#
+# Title: Step 3
+# Summary: Step 3 implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2025-01-02
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-09
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+
+# standard imports
+import os
+import yaml
+import json
+import sys
+import time
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# custom ARAI imports
+from utils.content_generator import ContentGenerator
+from utils.template_types import TemplateType
+
+def create_episode_posts(ai_model, master_file_path, number_of_posts):
+ '''
+ Description:
+ Create a new episode posts for the agent
+
+ Args:
+ ai_model: The AI model to use for generating responses
+ master_file_path: The path to the agent master json file
+
+ Raises:
+ Exception: If there's an error creating the episode posts
+
+ Example:
+ >>> ai_model = GeminiModel()
+ >>> master_file_path = "configs/Zorp/Zorp_master.json"
+ >>> step_3(ai_model, master_file_path)
+ '''
+ print("Step 3: Create a batch of posts for episode")
+
+ # step 3.1: load the season template yaml file
+ manager = ContentGenerator()
+
+ episode_template = manager.create_new_template_json(TemplateType.EPISODE)
+
+ # step 2.2: load the agent json file
+ agent_master_json = None
+ with open(master_file_path, 'r', encoding='utf-8') as file:
+ agent_master_json = json.load(file)
+
+ # extract agent details from master json
+ agent_details = agent_master_json['agent']['agent_details']
+
+ # get last season file
+ current_season = None
+ # Get the last season from the seasons array if it exists
+ seasons = agent_master_json['agent']['seasons']
+ current_season = seasons[-1] if seasons else None
+
+ # Extract season details
+ season_details = {
+ 'season_name': current_season['season_name'],
+ 'season_number': current_season['season_number'],
+ 'season_description': current_season['season_description'],
+ 'season_highlights': current_season['season_highlights'],
+ 'season_summary': current_season['season_summary'],
+ }
+
+ previous_episode = None
+
+ # Iterate over episodes while maintaining season context
+ for episode in current_season['episodes']:
+ # Now you have access to both season_details and the current episode
+ print(f"Processing Episode {episode['episode_number']} of Season {season_details['season_number']}")
+ # Work with episode data
+ episode_data = {
+ 'episode_name': episode['episode_name'],
+ 'episode_number': episode['episode_number'],
+ 'episode_description': episode['episode_description'],
+ 'episode_highlights': episode['episode_highlights'],
+ 'episode_summary': episode['episode_summary'],
+ }
+
+ # step 3.10: Generate a new season name, topic, and communication style with the prompt_2 template
+ # prompt 2 Season Creation:
+ # note that emojis will be output as unicode characters due to the yaml dump
+
+ prompt_3_vars = {
+ "agent_name": agent_details["name"],
+ "agent_json": json.dumps(agent_details),
+ "season_json": json.dumps(season_details),
+ "episode_json": json.dumps(episode_data),
+ "previous_episode": json.dumps(previous_episode),
+ "number_of_posts": number_of_posts,
+ "post_length": 277
+ }
+
+ # Constants for retry configuration
+ max_retries = 3
+ delay = 2 # seconds between retries
+
+ # step 3.11: Run the prompt with retry logic for LLM failures
+ success = False
+ for attempt in range(max_retries):
+ try:
+ posts_data = manager.run_prompt(
+ prompt_key="prompt_3 (Episode Posts Creation)",
+ template_vars=prompt_3_vars,
+ ai_model=ai_model,
+ )
+
+ # Validate that posts_data is valid JSON and has expected structure
+ if isinstance(posts_data, dict) and 'posts' in posts_data:
+ success = True
+ break
+ else:
+ print(f"Invalid response format. Attempt {attempt + 1}/{max_retries}")
+
+ except (json.JSONDecodeError, Exception) as e:
+ print(f"Error on attempt {attempt + 1}/{max_retries}: {str(e)}")
+ if attempt < max_retries - 1:
+ print(f"Retrying in {delay} seconds...")
+ time.sleep(delay)
+ continue
+
+ if not success:
+ print("Max retries reached. Failed to get valid response from LLM.")
+ return None
+
+ print("Appending the episode data to the master data")
+ agent_master_json = manager.append_episodes(
+ master_data=agent_master_json,
+ posts_data=posts_data,
+ season_index=season_details["season_number"]-1,
+ episode_index= episode_data["episode_number"]-1
+ )
+
+ # step 2.9: save the master data to a file
+ print("Saving the master data to a file")
+ manager.save_json_file(
+ save_path=master_file_path,
+ json_data=agent_master_json
+ )
+
+ # step 3.15: Move onto the next step of creating a new season
+ previous_episode = episode_data
+
+import models.gemini_model as gemini_model
+if __name__ == "__main__":
+ ai_model = gemini_model.GeminiModel()
+ create_episode_posts(ai_model, "configs/Pixelina/Pixelina_master.json", 6)
+
diff --git a/server/prompt_chaining/step_4_create_profile_images.py b/server/prompt_chaining/step_4_create_profile_images.py
new file mode 100644
index 0000000..c29253d
--- /dev/null
+++ b/server/prompt_chaining/step_4_create_profile_images.py
@@ -0,0 +1,182 @@
+#
+# Module: template_types
+#
+# This module implements the TemplateType class for defining the type of template.
+#
+# Title: Template Types
+# Summary: Template types implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2024-12-31
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-12
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+
+import requests
+import os
+import dotenv
+import time
+import json
+import sys
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# custom ARAI imports
+from utils.content_generator import ContentGenerator
+from utils.template_types import TemplateType
+import asset_generation.images_leonardo as images_leonardo
+from models.gemini_model import GeminiModel
+
+dotenv.load_dotenv()
+
+def create_images(ai_model, prompt, master_file_path, num_images):
+ # 4.1 Setup model details
+ model_details = json.loads(open("asset_generation/leonard_anime_styles.json", "r").read())
+ model_id = model_details["models"][0]["modelId"]
+ anime_general_style = next(
+ style for style in model_details["models"][0]["styles"]
+ if style["style name"] == "Anime General"
+ )
+
+ # 4.2 Setup style UUID
+ style_uuid = anime_general_style["styleUUID"]
+
+ # 4.3 Setup number of images
+ num_images = 4
+
+ # 4.4 Create image descriptions
+ # If prompt is None, we need to create image descriptions automatically
+ if prompt is None or prompt == "":
+ # # 4.4 Create image descriptions
+ # image_descriptions = image_post_description(ai_model, master_file_path, num_images)
+
+ # # 4.5 loop through image descriptions and create profile images
+ # for image_description in image_descriptions["profile_image_descriptions"]:
+ # print(image_description["description"] + "\n")
+ # # 4.6 Create profile images
+ # create_profile_images(image_description, master_file_path, model_id, style_uuid, num_images, consistent=True, max_retries=10, delay=5)
+
+ # Simple fix for now is to feed in the agents concept into the prompt
+ agent_master_json = None
+ with open(master_file_path, 'r', encoding='utf-8') as file:
+ agent_master_json = json.load(file)
+
+ prompt = agent_master_json['agent']['concept']
+ create_profile_images(prompt, master_file_path, model_id, style_uuid, num_images, consistent=False, max_retries=10, delay=5)
+
+ # 4.7 Create profile images based on users prompt
+ else:
+ create_profile_images(prompt, master_file_path, model_id, style_uuid, num_images, consistent=False, max_retries=10, delay=5)
+
+def image_post_description(ai_model, master_file_path, num_images):
+ manager = ContentGenerator()
+ image_descriptions = None
+
+ # step 4.2: load the agent json file
+ agent_master_json = None
+ with open(master_file_path, 'r', encoding='utf-8') as file:
+ agent_master_json = json.load(file)
+
+ # step 4.3: extract agent from master json
+ agent_details = agent_master_json['agent']['agent_details']
+
+ print("Crafting prompt for AI to create a new profile image descriptions")
+ prompt_4_vars = {
+ "agent_name": agent_details["name"],
+ "agent_json": json.dumps(agent_details),
+ "number_of_images": num_images
+ }
+
+ # step 2.4: Run the prompt
+ print("Sending prompt to AI to create a new profile image descriptions")
+ image_descriptions = manager.run_prompt(
+ prompt_key="prompt_4 (Agent Profile Image)",
+ template_vars=prompt_4_vars,
+ ai_model=ai_model,
+ )
+
+ # print(f"image_descriptions is: {image_descriptions}")
+ return image_descriptions
+
+#--------------------------------
+# Step 4: Generate a number of images for profile image
+#--------------------------------
+def create_profile_images(prompt, master_file_path, model_id, style_uuid, num_images, consistent=False, max_retries=10, delay=5):
+
+ # step 4.1: load existing profile image options from templates directory
+ manager = ContentGenerator()
+ profile_image_options_template = manager.create_new_template_json(TemplateType.PROFILE_IMAGE_OPTIONS)
+ profile_image_template = manager.create_new_template_json(TemplateType.PROFILE_IMAGE)
+
+ # step 4.2: load the agent json file
+ agent_master_json = None
+ with open(master_file_path, 'r', encoding='utf-8') as file:
+ agent_master_json = json.load(file)
+
+ # step 4.3: extract agent from master json
+ agent_details = agent_master_json['agent']['agent_details']
+
+ # step 4.3.1: check to make sure we have a profile image placeholder
+ if agent_master_json['agent']['profile_image'] is None or agent_master_json['agent']['profile_image'] == {}:
+ agent_master_json['agent']['profile_image'] = profile_image_template['profile_image']
+
+ # step 4.4: generate the image
+ if consistent:
+ response = images_leonardo.generate_inconsistent_image_lambda(prompt, model_id, style_uuid, num_images)
+ else:
+ response = images_leonardo.generate_inconsistent_image_lambda(prompt, model_id, style_uuid, num_images)
+
+ if not response['generations_by_pk']['generated_images']:
+ raise Exception("No Images Generated")
+
+ generations_by_pk = response['generations_by_pk'].get("generated_images")
+ generated_url = generations_by_pk[0].get('url')
+ generated_id = generations_by_pk[0].get('id')
+
+ # step 4.6: append new URL to the array
+ profile_image_options_template["profile_image_options"].append(response)
+
+ profile_image_template["profile_image"]["details"]["generationId"] = generated_id
+ profile_image_template["profile_image"]["details"]["url"] = generated_url
+ profile_image_template["profile_image"]["details"]["image_id"] = generated_id
+
+ # step 4.7: create the save path, use 0 as we are not in a season or episode
+ save_path = manager.create_filepath(agent_details["name"], 0, 0, TemplateType.PROFILE_IMAGE_OPTIONS)
+ save_path_profile_image = manager.create_filepath(agent_details["name"], 0, 0, TemplateType.PROFILE_IMAGE)
+
+ # step 4.8: save the profile image options to a file
+ manager.save_json_file(save_path, profile_image_options_template)
+
+ # if this is consistent, we will multiple genreation ids to track. So its better to store the generation id of the image the user picks.
+ if not consistent:
+ manager.save_json_file(save_path_profile_image, profile_image_template)
+
+ # step 4.9: append to master file
+ agent_master_json = manager.append_profile_image_options(agent_master_json, profile_image_options_template)
+ agent_master_json = manager.append_profile_image(agent_master_json, profile_image_template)
+
+ # step 4.10: save the master data to a file
+ print("Saving the master data to a file")
+ manager.save_json_file(
+ save_path=master_file_path,
+ json_data=agent_master_json
+ )
+
+
+if __name__ == "__main__":
+ # 4.1 Setup number of images
+ num_images = 4
+
+ # 4.2 Setup AI model
+ ai_model = GeminiModel()
+ master_file_path = "configs/CipherCat/CipherCat_master.json"
+
+ # 4.2.1 Setup prompt
+ # prompt = "a pygmy marmoset who owns 30% of trumps meme coin"
+ prompt = ""
+
+ # 4.3 Create profile images
+ create_images(ai_model, prompt, master_file_path, num_images)
diff --git a/server/prompt_chaining/step_5_agent_chat.py b/server/prompt_chaining/step_5_agent_chat.py
new file mode 100644
index 0000000..4c6aa32
--- /dev/null
+++ b/server/prompt_chaining/step_5_agent_chat.py
@@ -0,0 +1,131 @@
+#
+# Module: step_1
+#
+# This module implements the step_1 function for creating a new agent.
+#
+# Title: Step 1
+# Summary: Step 1 implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2025-01-15
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-15
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+
+# standard imports
+import yaml
+import json
+import sys
+import os
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+import utils.content_generator as content_generator
+from utils.template_types import TemplateType
+import utils.config_utils as config_utils
+# -------------------------------------------------------------------
+# Step 5: Chat with the agent
+# -------------------------------------------------------------------
+def agent_chat(ai_model, master_file_path: str, prompt: str, chat_history):
+ # Step 5.1: Create a new content manager that will send off the prompt to the AI model
+ manager = content_generator.ContentGenerator()
+
+ # step 5.2: load the agent json file
+ agent_master_json = None
+ with open(master_file_path, 'r', encoding='utf-8') as file:
+ agent_master_json = json.load(file)
+
+ # step 5.3: extract agent from master json
+ agent_details = agent_master_json['agent']['agent_details']
+
+ # Initialize chat history if None
+ if chat_history is None:
+ chat_history = config_utils.load_chat_history(agent_details["name"])
+
+ # step 5.4: Set up chat variables
+ agent_response = None
+
+ # prompt 5 Chat with the agent:
+ print("Crafting prompt for AI to chat with the agent")
+ prompt_5_vars = {
+ "agent_name": agent_details["name"],
+ "agent_json": json.dumps(agent_details),
+ "chat_history": json.dumps(chat_history),
+ "user_prompt": prompt, # Use the prompt parameter instead of user_prompt
+ "agent_json": json.dumps(agent_details)
+ }
+
+ # get the agent's response
+ print("Sending prompt to AI to chat with the agent")
+ agent_response = manager.run_prompt(
+ prompt_key="prompt_5 (Chat with the agent)",
+ template_vars=prompt_5_vars,
+ ai_model=ai_model,
+ )
+
+ if agent_response: # Add error checking
+ # add the user's prompt to the history log with label
+ chat_history['chat_history'].append({
+ "role": "user",
+ "prompt": prompt,
+ "message_id": len(chat_history['chat_history'])
+ })
+
+ # add the agent's response to the history log with label
+ chat_history['chat_history'].append({
+ "role": agent_details["name"],
+ "response": agent_response['response'],
+ "message_id": len(chat_history['chat_history'])
+ })
+
+ # Need to save the chat history to a file
+ # Need to check is there is an existing chat history file
+ # If there is, append to the file
+ # If there is not, create a new file
+ # or chat a new file, for users to have new chats
+ # later can allow the user to select which chat history to use
+
+ # step 5.5: create the file path for chat file
+ print("Creating the file path for the chat file")
+ agent_chat_file_path = manager.create_filepath(
+ agent_name=agent_details["name"],
+ season_number=0,
+ episode_number=0,
+ template_type=TemplateType.CHAT
+ )
+
+ # step 5.6: Save the chat history to a file
+ print("Saving the chat history to a file")
+ manager.save_json_file(
+ save_path=agent_chat_file_path,
+ json_data=chat_history
+ )
+
+ return agent_response, chat_history
+
+
+import models.gemini_model as gemini_model
+if __name__ == "__main__":
+ ai_model = gemini_model.GeminiModel()
+ manager = content_generator.ContentGenerator()
+ master_file_path = "configs/LamboLara/LamboLara_master.json"
+ chat_history = manager.create_new_template_json(TemplateType.CHAT)
+
+ user_prompt = "What is your name?"
+ agent_response, chat_history = agent_chat(ai_model, master_file_path, user_prompt, chat_history)
+ print(f"Response: {agent_response['response']}")
+ # print(f"Chat history: {chat_history}")
+ user_prompt = "What is your favorite color?"
+ agent_response, chat_history = agent_chat(ai_model, master_file_path, user_prompt, chat_history)
+ print(f"Response: {agent_response['response']}")
+ # print(f"Chat history: {chat_history}")
+ user_prompt = "What is your favorite food?"
+ agent_response, chat_history = agent_chat(ai_model, master_file_path, user_prompt, chat_history)
+ print(f"Response: {agent_response['response']}")
+ # print(f"Chat history: {chat_history}")
+ user_prompt = "What was the first question I asked you?"
+ agent_response, chat_history = agent_chat(ai_model, master_file_path, user_prompt, chat_history)
+ print(f"Response: {agent_response['response']}")
+ # print(f"Chat history: {chat_history}")
\ No newline at end of file
diff --git a/server/prompt_chaining/step_6_create_image_posts.py b/server/prompt_chaining/step_6_create_image_posts.py
new file mode 100644
index 0000000..863bd98
--- /dev/null
+++ b/server/prompt_chaining/step_6_create_image_posts.py
@@ -0,0 +1,149 @@
+#
+# Module: template_types
+#
+# This module implements the TemplateType class for defining the type of template.
+#
+# Title: Template Types
+# Summary: Template types implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2024-12-31
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-12
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+
+import requests
+import os
+import dotenv
+import time
+import json
+import sys
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# custom ARAI imports
+from utils.content_generator import ContentGenerator
+from utils.template_types import TemplateType
+import asset_generation.images_leonardo as images_leonardo
+from models.gemini_model import GeminiModel
+
+dotenv.load_dotenv()
+
+
+def create_image_posts(ai_model, master_file_path, num_images, season_number, episode_number):
+ manager = ContentGenerator()
+ image_descriptions = None
+
+ # step 4.2: load the agent json file
+ agent_master_json = None
+ with open(master_file_path, 'r', encoding='utf-8') as file:
+ agent_master_json = json.load(file)
+
+ # step 4.3: extract agent from master json
+ agent_details = agent_master_json['agent']['agent_details']
+ episode_details = agent_master_json['agent']['seasons'][season_number]['episodes'][episode_number]
+
+ print("Crafting prompt for AI to create a new profile image descriptions")
+ prompt_6_vars = {
+ "agent_name": agent_details["name"],
+ "agent_json": json.dumps(agent_details),
+ "episode_json": json.dumps(episode_details),
+ "number_of_images": num_images
+ }
+
+ # step 2.4: Run the prompt
+ print("Sending prompt to AI to create a new profile image descriptions")
+ image_descriptions = manager.run_prompt(
+ prompt_key="prompt_6 (Create Social Media Image Post)",
+ template_vars=prompt_6_vars,
+ ai_model=ai_model,
+ )
+
+ # print(f"image_descriptions is: {image_descriptions}")
+ return image_descriptions
+
+def create_image_post(ai_model, prompt, model_id, style_uuid, num_images, consistent=False, max_retries=10, delay=5):
+ manager = ContentGenerator()
+
+ # step 4.4: generate the image
+ if consistent:
+ response, payload = images_leonardo.generated_image_consistent(prompt, model_id, style_uuid, num_images)
+ else:
+ response, payload = images_leonardo.generated_image_inconsistent(prompt, model_id, style_uuid, num_images)
+
+ generation_id = response["sdGenerationJob"]["generationId"]
+
+ # step 4.5: retry loop to check for image completion
+ success = False # Add flag to track success
+ for attempt in range(max_retries):
+ response_url = images_leonardo.get_image_url(generation_id)
+
+ if response_url.get("generations_by_pk", {}).get("generated_images"):
+ success = True # Set flag when successful
+ break # Exit the loop when we have images
+
+ print(f"Image not ready yet. Attempt {attempt + 1}/{max_retries}. Waiting {delay} seconds...")
+ time.sleep(delay)
+
+ if not success:
+ print("Max retries reached. Image generation may have failed.")
+ return None
+
+ # step 4.7: create the save path, use 0 as we are not in a season or episode
+ save_path = os.path.join("configs", "temporary", "image_generations", f"generation_{generation_id}.json")
+
+ # step 4.8: save the profile image options to a file
+ manager.save_json_file(save_path, response_url)
+
+if __name__ == "__main__":
+ # Setup model details
+ model_details = json.loads(open("asset_generation/leonard_anime_styles.json", "r").read())
+ model_id = model_details["models"][0]["modelId"]
+ anime_general_style = next(
+ style for style in model_details["models"][0]["styles"]
+ if style["style name"] == "Anime General"
+ )
+
+ # Setup parameters
+ episode_number = 0
+ season_number = 0
+ style_uuid = anime_general_style["styleUUID"]
+ num_images = 2
+
+ # Setup AI model
+ ai_model = GeminiModel()
+ master_file_path = "configs/LamboLara/LamboLara_master.json"
+
+ #--------------------------------
+ # Step 4.1: Create multiple profile images via multiple prompts using consistent images
+ #--------------------------------
+ image_descriptions = create_image_posts(
+ ai_model=ai_model,
+ master_file_path=master_file_path,
+ num_images=num_images,
+ episode_number=episode_number,
+ season_number=season_number
+ )
+
+ print("\n\n")
+ print(f"image_descriptions is: {json.dumps(image_descriptions, indent=4)}")
+ print("\n\n")
+
+ # print the image descriptions
+ for image_description in image_descriptions["image_post_descriptions"]:
+ print(image_description["image_description"] + "\n")
+ print(image_description["post_after"] + "\n")
+ # Generate the image
+ create_image_post(
+ ai_model=ai_model,
+ prompt=image_description["image_description"],
+ model_id=model_id,
+ style_uuid=style_uuid,
+ num_images=1,
+ consistent=False,
+ max_retries=10,
+ delay=5
+ )
\ No newline at end of file
diff --git a/server/prompts/prompt_chaining.yaml b/server/prompts/prompt_chaining.yaml
new file mode 100644
index 0000000..ab80439
--- /dev/null
+++ b/server/prompts/prompt_chaining.yaml
@@ -0,0 +1,395 @@
+prompt_1 (Character Sheet Creation): |
+ You are an expert in creative writing, character design, world-building, and marketing. You are tasked with developing a complete character profile for a new agent who will be featured in stories for a Twitter bot. These stories will be broken down into seasons, episodes, and individual tweets (scenes/posts).
+
+ Agent Development Task
+ Generate a comprehensive JSON file that defines this new agent, including their name, personality, communication style, topic of expertise, backstory, universe, relevant hashtags, and emojis.
+ Use the following concept if its not empty, otherwise create one.
+ - concept: {{ concept }}
+
+ Specific Instructions:
+
+ 1. Invent a Creative Name:
+ - If no specific name is provided, create a unique and fitting name for the agent based on the concept of the agent. The name should be suitable for a Twitter handle.
+ - Don't have the word agent in the agents name or the @ symbol in the agents name.
+ - The name must be made up of two words. A first and last name, or something fun and creative.
+ - Agent Name: {{ agent_name }}
+ - Do not use any names that have already been previously used from {{ agent_names_blacklist }}
+
+ 2. Define the Topic:
+ - If no specific topic is provided invent a creative topic that lends itself to episodic storytelling.
+ - Topic: {{ topic }}
+
+ 3. Develop Personality and Style:
+ - Create a detailed personality for the agent.
+ - Define a clear communication style for the agent.
+ - Create a list of keywords.
+ - Personality: {{ personality }}
+ - Communication Style: {{ style }}
+
+ 4. Craft a Backstory:
+ - Invent a compelling and original backstory for the agent that explains their motivations, skills, and current situation. This backstory should be suitable for unfolding gradually over multiple story arcs.
+ - Backstory: {{ backstory }}
+
+ 5. Describe the Universe:
+ - Detail the world(s) or setting where the agent operates. Consider the current state of technology, the social and political landscape, major organizations or factions, and any unique elements relevant to the chosen topic.
+ - Universe: {{ universe }}
+
+ 6. Generate Marketing Elements:
+ - Create a list of relevant hashtags that will be used for social media promotion. Include hashtags related to the agent's name, topic, and genre.
+ - Example:
+ ```json
+ hashtags:
+ - "#example1"
+ - "#example2"
+ - "#longerExampleHashtag"
+ ```
+ - Create a list of relevant emojis that can be used in tweets to add visual interest and convey meaning.
+ - Example:
+ ```json
+ emojis:
+ - "🐳"
+ - "🌊"
+ - "🚀"
+ ```
+ - Hashtags: {{ hashtags }}
+ - Emojis: {{ emojis }}
+
+ Output Requirements:
+ - Only output valid JSON. Do not include any text outside of the JSON structure.
+ - Output a single, complete JSON file that includes all the fields mentioned above (name, personality, communication_style, topic, backstory, universe, hashtags, emojis).
+ - Adhere to the specified answer lengths for each field (short for personality and communication style, long for backstory and universe).
+
+ Output exactly as the json file:
+ ```json
+ {{ agent_json }}
+ ```
+
+prompt_2 (Season Creation): |
+ You are an expert in creative writing, season/episode design, and structured storytelling.
+ Your goal is to create a new season for {{ agent_name }}, who was previously defined in the following agent JSON:
+
+ {{ agent_json }}
+
+ Previous season:
+ - This provides the previous season details, including description and highlights for the season. Ignore if none is provided as this means we are creating season 1, so there is no previous season.
+ {{ previous_season }}
+
+ ---
+ ## Task: Season Creation
+
+ Generate a single valid JSON file that defines a new season for this agent. The season should include:
+
+ - Season Name: If none is provided, create a unique and fitting name based on {{ agent_name }} and the agent’s JSON.
+ - Use something that can also work as a Twitter hashtag.
+ - Season Name: {{ season_name }}
+
+ - Season Number: If none is provided, default to 1.
+ - Season Number: {{ season_number }}
+
+ - Season Description: If none is provided, create a unique description based on {{ agent_name }}’s JSON.
+ - Season Description: {{ season_description }}
+
+ - Season Highlights: If none is provided, invent highlights relevant to the agent’s concept or storyline.
+ - Season Highlights: {{ season_highlights }}
+
+ - Season Summary: If none is provided, create a concise summary that captures the essence of this season.
+ - Season Summary: {{ season_summary }}
+
+ - Episodes: We want **{{ number_of_episodes }} episodes** total. Starting with episode 1.
+ - If none are provided, create {{ number_of_episodes }} unique episodes.
+ - Each episode entry should have:
+ - `episode_id`
+ - `episode_name`
+ - `episode_number`
+ - `episode_description`
+ - `episode_highlights`
+ - `episode_posted` (default `False`)
+ - `episode_summary`
+
+ Output Requirements:
+ - Only output valid JSON. Do not include any text outside of the JSON structure.
+ - Output a single, complete JSON file in a seasons array that includes all the fields mentioned above (season_name, season_number, season_description, season_highlights, season_summary, episodes).
+
+ Output exactly as the json file:
+ ```json
+ {{ season_json }}
+ ```
+
+prompt_3 (Episode Posts Creation): |
+ You are an expert in creative writing, social media engagement, and character voice development. Your task is to generate {{ number_of_posts }} short "updates" (tweets/posts) for the agent {{ agent_name }} in the style of Twitter posts. These posts are for season {{ season_number }}, episode {{ episode_number }}.
+
+ Agent and Context:
+
+ * Agent JSON: {{ agent_json }} (This provides the agent's personality, backstory, etc.)
+ * Season JSON: {{ season_json }} (This provides the season details, including description and highlights for the season)
+ * Episode JSON: {{ episode_json }} (This provides the episode details, including description and highlights for the episode)
+ * Previous Episode JSON: {{ previous_episode }} (This provides the previous episode details, including description and highlights for the episode. Ignore if none is provided as this means we are creating episode 1, so there is no previous episode)
+ **Post Requirements:**
+
+ Each post should:
+
+ * Reflect Agent's Personality: Be consistent with {{ agent_name }}'s personality, tone, and communication style as defined in the agent JSON.
+ * Relate to Setting/Technology: Reference elements of the setting, technology, or other relevant details from the agent's universe as defined in the agent JSON.
+ * Incorporate Episode Context: Align with the season overview and specific episode overview provided in the season JSON.
+ * Highlight Observations: Center around misunderstandings, comedic observations, or serious reflections relevant to the episode's theme.
+ * Consider Emojis: Optionally use emojis from this set: {{ agent_json }} or use any emojis that are relevant to the episode's theme.
+ * Make sure there are no hashtags in the post. We want only text and emojis.
+ * Length Constraint: Each post must be under {{ post_length }} characters.
+
+ **JSON Output Format:**
+
+ Output a valid JSON file containing a list of posts under the `posts` key. Each post should be a separate entry in the list and include the following:
+
+ * `post_id`: A unique identifier for the post (e.g., s1\_e1\_post1, s1\_e1\_post2).
+ * `post_number`: The ordinal number of the post within the episode (e.g., 1, 2, 3).
+ * `post_content`: The actual text of the post.
+ * `post_highlights`: A brief, one-sentence description of the key takeaway or event in the post.
+ * `post_posted`: Set to `False` by default.
+
+ **Output Requirements:**
+ - **Only output valid JSON.** Do not include any text outside of the JSON structure.
+ - **Output the json file:**
+ ```json
+ {{ episode_json }}
+ ```
+prompt_4 (Agent Profile Image): |
+ You are an expert in creative writing, character design, world-building, and marketing.
+ You are tasked with developing a visual description for a character profile for social media.
+ This profile image is for a new agent named {{ agent_name }} who will be featured in stories for a Twitter bot.
+
+ Based on the agent's JSON, create a visual description for the agent's profile image.
+ {{ agent_json }}
+
+ Make {{ number_of_images }} image description variations.
+
+ Output Requirements:
+ - Only output valid JSON. Do not include any text outside of the JSON structure.
+
+ Output exactly as the json file:
+ ```json
+ {
+ "profile_image_descriptions": [
+ {
+ "id": 1,
+ "description": ""
+ }]
+ }
+ ```
+
+prompt_5 (Chat with the agent): |
+ Your task is to take on the role of {{ agent_name }} and answer any user's question as best and accurately as possible.
+ You should respond in the style of {{ agent_name }}.
+ You should use the chat history to understand the context of the conversation.
+ Do not put in hashtags in your response, unless its relevant to the conversation.
+ Use emojis if relevant to the conversation.
+
+ Here is the agent character sheet:
+ {{ agent_json }}
+
+ Here is the chat history:
+ {{ chat_history }}
+
+ Here is the users question as a prompt:
+ {{ user_prompt }}
+
+ **Output Requirements:**
+ - **Only output valid JSON.** Do not include any text outside of the JSON structure.
+ - **Output the json file:**
+ ```json
+ {
+ "response": ""
+ }
+ ```
+
+prompt_6 (Create Social Media Image Post): |
+ You are an expert in creative writing, character design, world-building, and marketing.
+ You are tasked with developing a series of visual descriptions that will be used to create artwork for a character social media posts.
+ These image descriptions will be used to create images based on the agents current episode.
+ These will be posted to twitter as a series of posts.
+ The style will be instagram style were it does not include the character in the photos but what the character is doing and seeing.
+
+ Here is the agent's JSON:
+ {{ agent_json }}
+
+ Here is the episode JSON with details on the episode and posts:
+ {{ episode_json }}
+
+ Make {{ number_of_images }} of image descriptions that support the narrative of the episode posts being sent to twitter.
+ Keep the descriptions short and to the point.
+ Mark were abouts in the order of the posts the image post should be tweeted.
+ Example, post this image after post 1, post this image after post 4, etc.
+ Determine which posts best benefit from an image directly after them.
+ Mark sure to spread out the image post among the text posts.
+
+ Example:
+ - Image of pina calada drink at a beach resort
+ - Image traditional japanese breakfast food
+ - Image out an apartment window on a summers day.
+
+ Output Requirements:
+ - Only output valid JSON. Do not include any text outside of the JSON structure.
+
+ Output exactly as the json file:
+ ```json
+ {
+ "image_post_descriptions": [
+ {
+ "image_description": "",
+ "post_after":
+ }
+ ]
+ }
+ ```
+
+prompt_6.1 (Create Social Media Image Post): |
+ You are tasked with developing visual descriptions for {{ number_of_images }} images that will be used to create artwork for a character social media posts.
+ These image descriptions will be based on an agents current episode and posts it contains.
+ These will be posted to twitter as a series of posts.
+ The style will be instagram style were it does not include the character in the photos but what the character is doing and seeing.
+
+ Keep it short and to the point.
+ It should be Image of OBJECT ACTION ENVIRONMENT
+ Example:
+ - Image of pina calada drink at a beach resort
+ - Image traditional japanese breakfast food
+ - Image out an apartment window on a summers day.
+
+ Here is the agent's JSON for context:
+ {{ agent_json }}
+
+ Here is the episode JSON with details on the episode and posts:
+ {{ episode_json }}
+
+ Mark were abouts in the order of the posts the image post should be tweeted.
+ Example, post this image after post 1, post this image after post 4, etc.
+ Determine which posts best benefit from an image directly after them.
+ Mark sure to spread out the image post among the text posts.
+
+ Output Requirements:
+ - Only output valid JSON. Do not include any text outside of the JSON structure.
+
+ Output exactly as the json file:
+ ```json
+ {
+ "image_post_descriptions": [
+ {
+ "image_description": "",
+ "post_after":
+ }
+ ]
+ }
+ ```
+
+prompt_6.2 (Create Social Media Image Post): |
+ You are tasked with developing visual descriptions for {{ number_of_images }} images that will be used to create artwork for a character social media posts.
+ These image descriptions will be based on an agents current episode and posts it contains.
+ These will be posted to twitter as a series of posts.
+ The style will be instagram style were it does not include any characters in the photos but what the character is doing and seeing.
+
+ Keep it short and to the point, and avoid any descriptiosn that could be used to generate a character image.
+ It should be Image of OBJECT ACTION ENVIRONMENT
+ Example:
+ - Image of pina calada drink at a beach resort
+ - Image traditional japanese breakfast food
+ - Image out an apartment window on a summers day.
+
+ Here is the agent's JSON for context:
+ {{ agent_json }}
+
+ Here is the episode JSON with details on the episode and posts:
+ {{ episode_json }}
+
+ Mark were abouts in the order of the posts the image post should be tweeted.
+ Example, post this image after post 1, post this image after post 4, etc.
+ Determine which posts best benefit from an image directly after them.
+ Mark sure to spread out the image post among the text posts but keep context, so an image should have at least a number of posts before it. This is dependent on the number of posts in the current episode.
+ If an image appears after a post, it should be relevant to the post.
+
+ Output Requirements:
+ - Only output valid JSON. Do not include any text outside of the JSON structure.
+
+ Output exactly as the json file:
+ ```json
+ {
+ "image_post_descriptions": [
+ {
+ "image_description": "",
+ "post_after":
+ }
+ ]
+ }
+ ```
+
+prompt 7 (Create Social Media Image Post): |
+ You are an expert in character design, and visual storytelling. Your task is to generate an illustration.
+
+
+
+prompt 8 (Profile Image Editor): |
+ You are an expert in character design, and visual storytelling. Your task is to generate a character illustration.
+ The illustration will include all relevant physical features, clothing, style, and the character's overall aesthetic.
+ Use the placeholders provided below to dynamically incorporate details into the description.
+
+ Character Illustration Prompt
+ Generate a descriptive prompt for an artist or an AI to create an image of the character. The prompt should be vivid, imaginative, and comprehensive, capturing the essence of the character. Use the following placeholders to define the details of the character:
+
+ About the character:
+ - agent name {{ agent_name }}
+ - agent personality {{ personality }}
+ - agent backstory {{ backstory }}
+
+ Specific Instructions:
+ 1. Character Profile:
+ - Gender/Body Type: {{ genderBodyType }}
+ - Age: {{ age }}
+ - Ethnicity/Skin Tone: {{ ethnicitySkinTone }}
+ - Height/Build: {{ heightBuild }}
+
+ 2. Face & Head Details:
+ - Face Shape: {{ faceShape }}
+ - Eye Color: {{ eyes.color }}
+ - Eye Shape: {{ eyes.shape }}
+ - Eyebrow Style: {{ eyes.eyebrowStyle }}
+ - Nose Shape: {{ nose }}
+ - Mouth/Lip Style: {{ mouthLips }}
+ - Facial Hair (if applicable): {{ facialHair }}
+ - Hair Length: {{ hair.length }}
+ - Hair Style: {{ hair.style }}
+ - Hair Color: {{ hair.color }}
+
+ 3. Body & Clothing:
+ - Body Shape: {{ bodyShape }}
+ - Outfit Style: {{ outfitStyle }}
+ - Specific Garments: {{ specificGarments }}
+ - Color Palette: {{ colorPalette }}
+ - Accessories: {{ accessories }}
+ - Armor or Weapons: {{ armorOrWeapons }}
+
+ 4. Style & Aesthetic:
+ - Art Style: {{ artStyle }}
+ - Mood/Expression: {{ moodExpression }}
+ - Lighting or Color Mood: {{ lightingOrColorMood }}
+ - Background Setting: {{ background }}
+
+ Output Requirements:
+ - Generate a complete and vivid description of the character using the placeholders provided.
+ - The output should be a natural language paragraph formatted for readability.
+ - Ensure the description flows smoothly and vividly combines all selected traits into a cohesive vision.
+ - Example:
+ "{{ genderBodyType }} character, {{ age }} with {{ ethnicitySkinTone }} skin, a {{ faceShape }} face, {{ hair.length }} {{ hair.style }} hair in {{ hair.color }}, wearing {{ outfitStyle }} clothing, accessorized with {{ accessories }}, set in a {{ background }}."
+
+ Final Output:
+ - Description: {{ character_description }}
+
+
+prompt 9 (Create Character Image): |
+ Generate a character portrait of {{ agent_name }} with {{ hair_style }} {{ hair_color }} hair,
+ {{ eye_color }} eyes, wearing {{ clothing_style }} style clothing.
+ Their personality can be described as {{ personality }} and their communication style is
+ {{ communication_style }}. Scene: {{ background }}.
+ Make sure to create an image with only one character.
+
+
+ "{{ genderBodyType }} character, {{ age }} with {{ ethnicitySkinTone }} skin,
+ a {{ faceShape }} face, {{ hair.length }} {{ hair.style }} hair in {{ hair.color }},
+ wearing {{ outfitStyle }} clothing, accessorized with {{ accessories }}, set in a {{ background }}."
diff --git a/server/requirements.txt b/server/requirements.txt
new file mode 100644
index 0000000..eec5d2a
Binary files /dev/null and b/server/requirements.txt differ
diff --git a/server/templates/agent.json b/server/templates/agent.json
new file mode 100644
index 0000000..95b9e36
--- /dev/null
+++ b/server/templates/agent.json
@@ -0,0 +1,14 @@
+{
+ "agent": {
+ "agent_details": {
+ "name": "",
+ "personality": [],
+ "communication_style": [],
+ "backstory": "",
+ "universe": "",
+ "topic_expertise": [],
+ "hashtags": [],
+ "emojis": []
+ }
+}
+}
diff --git a/server/templates/chat_log.json b/server/templates/chat_log.json
new file mode 100644
index 0000000..d6450e0
--- /dev/null
+++ b/server/templates/chat_log.json
@@ -0,0 +1,4 @@
+{
+ "agent_name": "",
+ "chat_history": []
+}
diff --git a/server/templates/episode.json b/server/templates/episode.json
new file mode 100644
index 0000000..d7578c7
--- /dev/null
+++ b/server/templates/episode.json
@@ -0,0 +1,20 @@
+{
+ "episode": {
+ "episode_name": "",
+ "episode_number": 0,
+ "episode_description": "",
+ "episode_highlights": "",
+ "episode_summary": "",
+ "episode_posted": false,
+ "current_post_number": 0,
+ "posts": [
+ {
+ "post_id": "",
+ "post_number": 0,
+ "post_content": "",
+ "post_highlights": "",
+ "post_posted": false
+ }
+ ]
+ }
+}
diff --git a/server/templates/master.json b/server/templates/master.json
new file mode 100644
index 0000000..8351ab5
--- /dev/null
+++ b/server/templates/master.json
@@ -0,0 +1,64 @@
+{
+ "agent": {
+ "concept": "",
+ "agent_details": {
+ "name": "",
+ "personality": [],
+ "communication_style": [],
+ "backstory": "",
+ "universe": "",
+ "topic_expertise": [],
+ "hashtags": [],
+ "emojis": []
+ },
+ "ai_model": {
+ "model_type": "",
+ "model_name": "",
+ "memory_store": ""
+ },
+ "connectors": {
+ "twitter": false,
+ "telegram": false,
+ "discord": false
+ },
+ "tracker": {
+ "current_season_number": 0,
+ "current_episode_number": 0,
+ "current_post_number": 0,
+ "post_every_x_minutes": 0
+ },
+ "profile_image": {},
+ "profile_image_options": [],
+ "seasons": [
+ {
+ "season_name": "",
+ "season_number": 0,
+ "season_description": "",
+ "season_highlights": "",
+ "season_summary": "",
+ "season_posted": false,
+ "current_episode_number": 0,
+ "episodes": [
+ {
+ "episode_name": "",
+ "episode_number": 0,
+ "episode_description": "",
+ "episode_highlights": "",
+ "episode_summary": "",
+ "episode_posted": false,
+ "current_post_number": 0,
+ "posts": [
+ {
+ "post_id": "",
+ "post_number": 0,
+ "post_content": "",
+ "post_highlights": "",
+ "post_posted": false
+ }
+ ]
+ }
+ ]
+ }
+ ]
+ }
+}
diff --git a/server/templates/post_image.json b/server/templates/post_image.json
new file mode 100644
index 0000000..e69de29
diff --git a/server/templates/profile_image.json b/server/templates/profile_image.json
new file mode 100644
index 0000000..2b058a9
--- /dev/null
+++ b/server/templates/profile_image.json
@@ -0,0 +1,11 @@
+{
+ "profile_image":{
+ "details":{
+ "url": "",
+ "image_id": "",
+ "generationId": ""
+ },
+ "payload":{
+ }
+ }
+}
diff --git a/server/templates/profile_image_options.json b/server/templates/profile_image_options.json
new file mode 100644
index 0000000..a086c9d
--- /dev/null
+++ b/server/templates/profile_image_options.json
@@ -0,0 +1,3 @@
+{
+ "profile_image_options":[]
+}
diff --git a/server/templates/profile_image_traits.json b/server/templates/profile_image_traits.json
new file mode 100644
index 0000000..d857fa9
--- /dev/null
+++ b/server/templates/profile_image_traits.json
@@ -0,0 +1,121 @@
+{
+ "characterTraits": [
+ {
+ "category": "Character Profile",
+ "traits": [
+ {
+ "traitName": "genderBodyType",
+ "options": ["Male", "Female", "Androgynous", "Child-like", "Non-binary"]
+ },
+ {
+ "traitName": "age",
+ "options": ["Teen", "Young Adult", "Adult", "Middle-aged", "Elderly"]
+ },
+ {
+ "traitName": "ethnicitySkinTone",
+ "options": ["Light", "Medium", "Dark", "Olive", "Fantasy (e.g. green, blue)"]
+ },
+ {
+ "traitName": "heightBuild",
+ "options": ["Short", "Tall", "Slim", "Athletic", "Muscular", "Heavy-set"]
+ }
+ ]
+ },
+ {
+ "category": "Face & Head Details",
+ "traits": [
+ {
+ "traitName": "faceShape",
+ "options": ["Round", "Oval", "Square", "Heart-shaped", "Angular"]
+ },
+ {
+ "traitName": "eyes.color",
+ "options": ["Brown", "Blue", "Green", "Amber", "Gray", "Fantasy (e.g. purple, red)"]
+ },
+ {
+ "traitName": "eyes.shape",
+ "options": ["Large and round", "Narrow", "Slanted", "Almond-shaped"]
+ },
+ {
+ "traitName": "eyes.eyebrowStyle",
+ "options": ["Arched", "Bushy", "Thin", "Straight"]
+ },
+ {
+ "traitName": "nose",
+ "options": ["Small and pointed", "Broad", "Straight", "Hawk-like", "Button"]
+ },
+ {
+ "traitName": "mouthLips",
+ "options": ["Thin lips", "Full lips", "Wide mouth", "Subtle smile", "Neutral"]
+ },
+ {
+ "traitName": "facialHair",
+ "options": ["Clean-shaven", "Stubble", "Goatee", "Full beard", "Moustache"]
+ },
+ {
+ "traitName": "hair.length",
+ "options": ["Short", "Medium", "Long", "Bald"]
+ },
+ {
+ "traitName": "hair.style",
+ "options": ["Straight", "Wavy", "Curly", "Braided", "Dreads", "Ponytail", "Mohawk"]
+ },
+ {
+ "traitName": "hair.color",
+ "options": ["Black", "Brown", "Blonde", "Red", "Gray", "Fantasy (e.g. neon pink, silver)"]
+ }
+ ]
+ },
+ {
+ "category": "Body & Clothing",
+ "traits": [
+ {
+ "traitName": "bodyShape",
+ "options": ["Proportional", "Long-legged", "Broad-shouldered", "Pear-shaped", "Stocky"]
+ },
+ {
+ "traitName": "outfitStyle",
+ "options": ["Medieval fantasy", "Futuristic", "Pirate", "Steampunk", "Military", "Casual modern", "High-tech soldier"]
+ },
+ {
+ "traitName": "specificGarments",
+ "options": ["Armor", "Robe", "Jacket", "Dress", "Tunic", "Bodysuit"]
+ },
+ {
+ "traitName": "colorPalette",
+ "options": ["Neutral browns", "Bright primary", "Dark & edgy", "Pastel", "Neon/cyberpunk"]
+ },
+ {
+ "traitName": "accessories",
+ "options": ["Earrings", "Necklace", "Headband", "Goggles", "Gloves", "Hat", "Belts or pouches"]
+ },
+ {
+ "traitName": "armorOrWeapons",
+ "options": ["Leather armor", "Plate mail", "Energy shield", "Katana", "Magic staff"]
+ }
+ ]
+ },
+ {
+ "category": "Style & Aesthetic",
+ "traits": [
+ {
+ "traitName": "artStyle",
+ "options": ["Semi-realistic", "Cartoon", "Anime", "Stylized", "Pixel Art", "Cel-shaded"]
+ },
+ {
+ "traitName": "moodExpression",
+ "options": ["Confident", "Friendly smile", "Serious and determined", "Mischievous grin", "Angry snarl"]
+ },
+ {
+ "traitName": "lightingOrColorMood",
+ "options": ["Bright and vibrant", "Dark and gothic", "Pastel", "Neon/cyberpunk", "Cinematic lighting"]
+ },
+ {
+ "traitName": "background",
+ "options": ["Transparent", "Simple gradient", "Forest", "Sci-fi cityscape", "Ancient ruins", "Cyberpunk skyline"]
+ }
+ ]
+ }
+ ]
+ }
+
diff --git a/server/templates/season.json b/server/templates/season.json
new file mode 100644
index 0000000..4a123f0
--- /dev/null
+++ b/server/templates/season.json
@@ -0,0 +1,24 @@
+{
+ "seasons": [
+ {
+ "season_name": "",
+ "season_number": 0,
+ "season_description": "",
+ "season_highlights": "",
+ "season_summary": "",
+ "season_posted": false,
+ "current_episode_number": 0,
+ "episodes": [
+ {
+ "episode_name": "",
+ "episode_number": 0,
+ "episode_description": "",
+ "episode_highlights": "",
+ "episode_summary": "",
+ "episode_posted": false,
+ "current_post_number": 0
+ }
+ ]
+ }
+ ]
+}
diff --git a/server/templates/tracker.json b/server/templates/tracker.json
new file mode 100644
index 0000000..7f19ef2
--- /dev/null
+++ b/server/templates/tracker.json
@@ -0,0 +1,8 @@
+{
+"tracker": {
+ "current_season_number": 0,
+ "current_episode_number": 0,
+ "current_post_number": 0,
+ "post_every_x_minutes": 0
+ }
+}
diff --git a/server/utils/__init__.py b/server/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/server/utils/config_utils.py b/server/utils/config_utils.py
new file mode 100644
index 0000000..570b4a4
--- /dev/null
+++ b/server/utils/config_utils.py
@@ -0,0 +1,213 @@
+# standard imports
+import os
+import json
+import sys
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+import utils.content_generator as content_generator
+from utils.template_types import TemplateType
+
+def list_available_seasons(agent_name):
+ """List all available seasons for an agent
+
+ Args:
+ agent_name (str): The name of the agent to list seasons for
+
+ Returns:
+ list: List of available season names
+ """
+ seasons = []
+ config_path = os.path.join("configs", agent_name, f"{agent_name}_master.json")
+
+ if os.path.exists(config_path):
+ with open(config_path, 'r', encoding='utf-8') as f:
+ master_data = json.load(f)
+ for season in master_data["agent"]["seasons"]:
+ seasons.append(season["season_name"])
+ return seasons
+
+def load_season_data(agent_name, season_index):
+ """Load detailed information about a season and its episodes
+
+ Args:
+ agent_name (str): The name of the agent
+ season_index (int or str): The index of the season to display
+ """
+ config_path = os.path.join("configs", agent_name, f"{agent_name}_master.json")
+ with open(config_path, 'r', encoding='utf-8') as f:
+ # Convert season_index to int if it's a string
+ if isinstance(season_index, str):
+ season_index = int(season_index)
+ return json.load(f)["agent"]["seasons"][season_index]
+
+def display_season_details(agent_name, season_index):
+ """Display detailed information about a season and its episodes
+
+ Args:
+ agent_name (str): The name of the agent
+ season_name (str): The name of the season to display
+ """
+ season_data = load_season_data(agent_name, season_index)
+
+ print("\n" + "="*50)
+ print("Season Name:")
+ print(season_data.get('season_name', 'No season name available'))
+ print(f"Season Number:")
+ print(f"{season_data.get('season_number', 'No season number available')}")
+ print(f"Season Posted:")
+ print(f"{season_data.get('season_posted', 'No season posted available')}")
+ print("\nSeason Description:")
+ print(season_data.get('season_description', 'No description available'))
+ print("\nSeason Highlights:")
+ for highlight in season_data.get('season_highlights', []):
+ print(f"- {highlight}")
+ print("\nSeason Summary:")
+ print(season_data.get('season_summary', 'No summary available'))
+
+ print("="*50)
+ print("\nEpisodes:")
+
+ for episode in season_data.get('episodes', []):
+ print("="*50)
+ print(f"\nEpisode: {episode['episode_name']}")
+ print(f"Episode Number: {episode['episode_number']}")
+ print(f"Episode Posted: {episode.get('episode_posted', 'No episode posted available')}")
+ print("-"*30)
+ print("Description:")
+ print(episode.get('episode_description', 'No description available'))
+ print("\nHighlights:")
+ for highlight in episode.get('episode_highlights', []):
+ print(f"- {highlight}")
+ print("\nEpisode Summary:")
+ print(episode.get('episode_summary', 'No summary available'))
+
+ print("\nPosts:")
+ for post in episode.get('posts', []):
+ print("-"*20)
+ print(f"Post Number: {post.get('post_number', 'No post number available')}")
+ print(f"Post Posted: {post.get('post_posted', 'No post posted available')}")
+ print(f"Content: {post.get('post_content', 'No content available')}")
+ print(f"Highlights: {post.get('post_highlights', 'No highlights available')}")
+ print("-"*30)
+
+def list_available_agents():
+ """List all available agent configs in the configs folder
+
+ Returns:
+ list: List of available agent names
+ """
+ agents = []
+ if os.path.exists("configs"):
+ for agent_dir in os.listdir("configs"):
+ if os.path.isdir(os.path.join("configs", agent_dir)):
+ agents.append(agent_dir)
+ return agents
+
+def load_agent_master_template(agent_file_path):
+ """Load the master template for an agent
+
+ Args:
+ agent_name (str): The name of the agent to load the master template for
+
+ Returns:
+ dict: The master template for the agent
+ """
+ with open(agent_file_path, 'r', encoding='utf-8') as f:
+ return json.load(f)
+
+def save_agent_master_template(agent_master_template, agent_file_path):
+ """Save the master template for an agent
+
+ Args:
+ agent_master_template (dict): The master template for the agent
+ agent_file_path (str): The path to the agent's master file
+ """
+ with open(agent_file_path, 'w', encoding='utf-8') as f:
+ json.dump(agent_master_template, f, indent=4)
+
+def load_agent_tracker_config(agent_name):
+ """Load configuration for the selected agent
+
+ Args:
+ agent_name (str): The name of the agent to load configuration for
+
+ Returns:
+ dict: The configuration for the selected agent
+ """
+ config_path = os.path.join("configs", agent_name, f"{agent_name}_master.json")
+ with open(config_path, 'r', encoding='utf-8') as f:
+ return json.load(f)["agent"]["tracker"]
+
+def get_agent_file_path(agent_name):
+ """Get the path to an agent's master file
+
+ Args:
+ agent_name (str): Name of the agent
+
+ Returns:
+ str: Path to the agent's master file
+ """
+ return os.path.join("configs", agent_name, f"{agent_name}_master.json")
+
+def load_chat_history(agent_name):
+ """Load chat history for an agent
+
+ Args:
+ agent_name (str): Name of the agent
+
+ Returns:
+ dict: Chat history for the agent
+ """
+
+ if not check_if_chat_history_exists(agent_name):
+ create_new_chat_history_file(agent_name)
+
+ chat_history_path = os.path.join("configs", agent_name, f"{agent_name}_chat_log.json")
+ with open(chat_history_path, 'r', encoding='utf-8') as f:
+ return json.load(f)
+
+def create_new_chat_history_file(agent_name):
+ """Create a new chat history file for an agent
+
+ Args:
+ agent_name (str): Name of the agent
+ """
+ manager = content_generator.ContentGenerator()
+
+ chat_history = manager.create_new_template_json(TemplateType.CHAT)
+
+ chat_history["agent_name"] = agent_name
+
+ agent_chat_file_path = manager.create_filepath(
+ agent_name=agent_name,
+ season_number=0,
+ episode_number=0,
+ template_type=TemplateType.CHAT
+ )
+
+ manager.save_json_file(
+ save_path=agent_chat_file_path,
+ json_data=chat_history
+ )
+
+def get_chat_history_file_path(agent_name):
+ """Get the path to an agent's chat history file
+
+ Args:
+ agent_name (str): Name of the agent
+
+ Returns:
+ str: Path to the agent's chat history file
+ """
+ return os.path.join("configs", agent_name, f"{agent_name}_chat_log.json")
+
+def check_if_chat_history_exists(agent_name):
+ """Check if chat history exists for an agent
+
+ Args:
+ agent_name (str): Name of the agent
+
+ Returns:
+ bool: True if chat history exists, False otherwise
+ """
+ return os.path.exists(os.path.join("configs", agent_name, f"{agent_name}_chat_log.json"))
\ No newline at end of file
diff --git a/server/utils/content_generator.py b/server/utils/content_generator.py
new file mode 100644
index 0000000..d4cb268
--- /dev/null
+++ b/server/utils/content_generator.py
@@ -0,0 +1,658 @@
+#
+# Module: content_generator
+#
+# This module implements the ContentGenerator class for generating content for the agents.
+#
+# Title: Content Generator
+# Summary: Content generator implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2025-01-02
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-09
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+
+# standard imports
+import os
+import shutil
+import yaml
+import json
+from jinja2 import Template
+import copy
+
+# custom ARAI code imports
+from models.base_model import ModelInterface
+from utils.template_types import TemplateType
+
+class ContentGenerator:
+ """
+ Description:
+ This class is responsible for generating content for the agents.
+
+ Attributes:
+ agents_config_dir (str): the directory to save the agent configurations
+ templates_dir (str): the directory to save the agent templates
+ agent_template_path (str): the path to the agent template
+ chain_prompts_path (str): the path to the chain prompts
+ """
+
+ def __init__(self):
+ """Initialize the ContentGenerator class.
+
+ Example:
+ >>> content_generator = ContentGenerator()
+ """
+ # use relative path to get to project root (two levels up from utils)
+ project_root = os.path.dirname(os.path.dirname(__file__))
+
+ # Set the directories relative to project root
+ self.agents_config_dir = os.path.join(project_root, "configs")
+ self.prompts_dir = os.path.join(project_root, "prompts")
+
+ # Set the path for the chain prompt
+ self.chain_prompts_path = os.path.join(self.prompts_dir, "prompt_chaining.yaml")
+
+ # Set the paths for the templates
+ self.templates_dir = os.path.join(project_root, "templates")
+ self.master_template_path = os.path.join(self.templates_dir, "master.json")
+ self.tracker_template_path = os.path.join(self.templates_dir, "tracker.json")
+ self.agent_template_path = os.path.join(self.templates_dir, "agent.json")
+ self.season_template_path = os.path.join(self.templates_dir, "season.json")
+ self.episode_template_path = os.path.join(self.templates_dir, "episode.json")
+ self.profile_image_template_path = os.path.join(self.templates_dir, "profile_image.json")
+ self.profile_image_options_template_path = os.path.join(self.templates_dir, "profile_image_options.json")
+ self.chat_template_path = os.path.join(self.templates_dir, "chat_log.json")
+
+ # -------------------------------------------------------------------
+ # Helper to create a new agent yaml file
+ # -------------------------------------------------------------------
+ def create_new_template_json(self, template_type: TemplateType) -> dict:
+ """Create a new agent configuration based on the template configuration file.
+
+ Args:
+ template_type (TemplateType): the type of template to create
+
+ Returns:
+ dict: the new agent configuration
+
+ Raises:
+ ValueError: If the template type is invalid
+
+ Example:
+ >>> agent_config = create_new_template_json(TemplateType.MASTER)
+ >>> print(agent_config)
+ """
+ # 1. Ensure directory exist
+ os.makedirs(self.templates_dir, exist_ok=True)
+
+ # 2. Load the template configuration file
+ if template_type == TemplateType.MASTER:
+ template_path = self.master_template_path
+ elif template_type == TemplateType.TRACKER:
+ template_path = self.tracker_template_path
+ elif template_type == TemplateType.AGENT:
+ template_path = self.agent_template_path
+ elif template_type == TemplateType.SEASON:
+ template_path = self.season_template_path
+ elif template_type == TemplateType.EPISODE:
+ template_path = self.episode_template_path
+ elif template_type == TemplateType.PROFILE_IMAGE:
+ template_path = self.profile_image_template_path
+ elif template_type == TemplateType.PROFILE_IMAGE_OPTIONS:
+ template_path = self.profile_image_options_template_path
+ elif template_type == TemplateType.CHAT:
+ template_path = self.chat_template_path
+ else:
+ raise ValueError(f"Invalid template type: {template_type}")
+
+ # 3. Load the template configuration file
+ with open(template_path, "r") as f:
+ template = json.load(f)
+
+ # 4. Return new configuration
+ return template
+
+ # -------------------------------------------------------------------
+ # Helper to safely parse YAML from the LLM's response
+ # -------------------------------------------------------------------
+ def process_and_save_agent_response(self, response) -> dict:
+ """Attempts to parse JSON from LLM text.
+
+ Args:
+ response (str): the response from the LLM
+ debug (bool, optional): whether to print debug information. Defaults to False.
+
+ Returns:
+ dict: the parsed JSON
+
+ Raises:
+ Exception: If there's an error parsing the JSON
+
+ Example:
+ >>> response = "```yaml\nname: John Doe\nage: 30\n```"
+ >>> parsed = process_and_save_agent_response(response)
+ >>> print(parsed)
+ """
+
+ # 1. response = self.fix_yaml_from_response(response, debug)
+ raw_save_path = self.save_raw_response(response)
+ print(f"raw_save_path is: {raw_save_path}")
+
+ # 2. response = self.save_processed_response(response, debug)
+ save_path = self.create_json_from_response(response)
+ print(f"save_path is: {save_path}")
+
+ # 3. load the yaml file into a dict
+ with open(save_path, "r", encoding="utf-8") as f:
+ try:
+ # 3.1 load the yaml file into a dict
+ response = json.load(f)
+ except Exception as e:
+ print(f"process_and_save_agent_response. Error loading json file: {str(e)}")
+ return None
+
+ # 4. Agent directory
+ file_dir = os.path.join(self.agents_config_dir, "temporary")
+ print(f"file_dir is: {file_dir}")
+
+ # 5. Make sure the agent directory exists
+ os.makedirs(file_dir, exist_ok=True)
+
+ # 6. Move files to agent directory
+ saved_raw_path = self.move_file(raw_save_path, file_dir)
+ saved_processed_path = self.move_file(save_path, file_dir)
+
+ # 8. return the response
+ return response
+
+ # -------------------------------------------------------------------
+ # Helper to save the raw response to a file
+ # -------------------------------------------------------------------
+ def save_raw_response(self, response) -> str:
+ """Saves the raw response to a file.
+
+ Args:
+ response (str): the response from the LLM
+ debug (bool, optional): whether to print debug information. Defaults to False.
+
+ Returns:
+ str: the path to the saved yaml file
+
+ Raises:
+ Exception: If there's an error saving the response
+
+ Example:
+ >>> response = "```yaml\nname: John Doe\nage: 30\n```"
+ >>> save_path = save_raw_response(response)
+ >>> print(save_path)
+ """
+ # 1. create a file to save the response
+ save_path = os.path.join(self.agents_config_dir, "raw_response.json")
+
+ # 2. Ensure directory exists
+ os.makedirs(os.path.dirname(save_path), exist_ok=True)
+
+ # 3. Save the response to the file
+ with open(save_path, "w", encoding="utf-8") as f:
+ try:
+ f.write(response)
+ return save_path
+ except Exception as e:
+ print(f"Error saving raw response to file: {str(e)}")
+ return None
+
+ # -------------------------------------------------------------------
+ # Helper to create a yaml file from LLM text
+ # -------------------------------------------------------------------
+ def create_json_from_response(self, response) -> str:
+ """Attempts to create a yaml file from LLM text.
+
+ Args:
+ response (str): the response from the LLM
+
+ Returns:
+ str: the path to the saved yaml file
+
+ Raises:
+ Exception: If there's an error saving the response
+
+ Example:
+ >>> response = "```json\nname: John Doe\nage: 30\n```"
+ >>> save_path = create_json_from_response(response)
+ >>> print(save_path)
+ """
+ # 1. strip out '''yaml and '''
+ # remove new lines and leading and trailing whitespace
+ response = response.replace("```json", "").replace("```", "").strip()
+
+ # 2. create a file to save the response
+ save_path = os.path.join(self.agents_config_dir, "processed_response.json")
+
+ # 3. Ensure directory exists
+ os.makedirs(os.path.dirname(save_path), exist_ok=True)
+
+ # 4. Save the response to the file
+ with open(save_path, "w", encoding="utf-8") as f:
+ try:
+ # yaml_string = yaml.dump(response, allow_unicode=True, default_flow_style=False)
+ # f.write(yaml_string)
+ f.write(response)
+ except Exception as e:
+ print(f"Error creating json file from response: {str(e)}")
+
+ return save_path
+
+ # -------------------------------------------------------------------
+ # Helper to rename file
+ # -------------------------------------------------------------------
+ def rename_file(self, old_path, new_name) -> str:
+ """Renames a file.
+
+ Args:
+ old_path (str): the old path to the file
+ new_name (str): the new name of the file
+
+ Returns:
+ str: the new path to the file
+
+ Raises:
+ Exception: If there's an error renaming the file
+
+ Example:
+ >>> rename_file("tests/test.json", "test_new.json")
+ """
+ # 1. Ensure directory exists
+ os.makedirs(os.path.dirname(old_path), exist_ok=True)
+
+ # 2. Rename the file
+ try:
+ new_path = os.path.join(os.path.dirname(old_path), new_name)
+ os.rename(old_path, new_path)
+ return new_path
+ except Exception as e:
+ print(f"Error renaming file: {str(e)}")
+ return None
+
+ # -------------------------------------------------------------------
+ # Helper to move file
+ # -------------------------------------------------------------------
+ def move_file(self, old_path, new_dir) -> str:
+ """Moves a file.
+
+ Args:
+ old_path (str): the old path to the file
+ new_dir (str): the new directory to move the file to
+
+ Returns:
+ str: the new path to the file after moving
+
+ Raises:
+ Exception: If there's an error moving the file
+
+ Example:
+ >>> move_file("tests/test.json", "agents/test")
+ """
+ # 1. Ensure directory exists
+ os.makedirs(new_dir, exist_ok=True)
+
+ # 2. Move the file
+ try:
+ new_path = os.path.join(new_dir, os.path.basename(old_path))
+ new_path = shutil.move(old_path, new_path)
+ return new_path
+ except Exception as e:
+ print(f"Error moving file: {str(e)}")
+ return None
+
+
+ # -------------------------------------------------------------------
+ # Helper to add new agent data to the current agent data
+ # -------------------------------------------------------------------
+ def add_data_to_template(self, current_data, new_data) -> dict:
+ """Adds new agent data to the current agent data.
+
+ Args:
+ new_data (dict): the new data
+ current_data (dict): the current data
+
+ Returns:
+ dict: the updated agent data
+
+ Raises:
+ Exception: If there's an error adding the data
+
+ Example:
+ >>> new_data = {"name": "John Doe", "age": 30}
+ >>> current_data = {"name": "Jane Doe", "age": 25}
+ >>> updated_data = add_data_to_template(new_data, current_data)
+ >>> print(updated_data)
+ """
+ # 1. ensure we have a dictionary to work with
+ if isinstance(current_data, str):
+ existing_data = json.loads(current_data)
+ elif isinstance(current_data, dict):
+ existing_data = current_data.copy()
+ else:
+ existing_data = {}
+
+ # 2. Only update fields that already exist in existing_data
+ for key in new_data:
+ if key in existing_data:
+ existing_data[key] = new_data[key]
+
+ # 3. return the updated data
+ return existing_data
+
+ # -------------------------------------------------------------------
+ # Helper to create filepath
+ # -------------------------------------------------------------------
+ def create_filepath(self, agent_name: str, season_number: int, episode_number: int, template_type: TemplateType):
+ """Creates a filepath for the agent data.
+
+ Args:
+ agent_name (str): the name of the agent
+ season_number (str): the number of the season
+ episode_number (str): the number of the episode
+ template_type (TemplateType): the type of template
+
+ Returns:
+ str: the filepath
+
+ Raises:
+ Exception: If there's an error creating the filepath
+
+ Example:
+ >>> create_filepath("John Doe", "0", TemplateType.AGENT)
+ """
+ # 1. create the filepath based on the template type
+ if template_type == TemplateType.MASTER:
+ return os.path.join(self.agents_config_dir, agent_name, agent_name + "_master.json")
+ elif template_type == TemplateType.TRACKER:
+ return os.path.join(self.agents_config_dir, agent_name, "tracker" + ".json")
+ elif template_type == TemplateType.AGENT:
+ return os.path.join(self.agents_config_dir, agent_name, agent_name + ".json")
+ elif template_type == TemplateType.SEASON:
+ return os.path.join(self.agents_config_dir, agent_name, "season_" + str(season_number), "season_" + str(season_number) + ".json")
+ elif template_type == TemplateType.EPISODE:
+ return os.path.join(self.agents_config_dir, agent_name, "season_" + str(season_number), "s" + str(season_number) + "_episode_" + str(episode_number) + ".json")
+ elif template_type == TemplateType.PROFILE_IMAGE:
+ return os.path.join(self.agents_config_dir, agent_name, "profile_image.json")
+ elif template_type == TemplateType.PROFILE_IMAGE_OPTIONS:
+ return os.path.join(self.agents_config_dir, agent_name, "profile_image_options.json")
+ elif template_type == TemplateType.CHAT:
+ return os.path.join(self.agents_config_dir, agent_name, agent_name + "_chat_log.json")
+
+ # -------------------------------------------------------------------
+ # Helper to save the agent data to a yaml file
+ # -------------------------------------------------------------------
+ def save_json_file(self, save_path: str, json_data: dict):
+ """Saves the agent data to a JSON file.
+
+ Args:
+ save_path (str): The path to save the JSON file
+ json_data (dict): The data to save to the JSON file
+
+ Returns:
+ str: the path to the saved json file
+
+ Raises:
+ Exception: If there's an error saving the json file
+
+ Example:
+ >>> agent_data = {"name": "John Doe", "age": 30}
+ >>> save_json_file(filepath="tests/test.json", json_data=agent_data)
+ """
+ # 1. Ensure directory exists
+ os.makedirs(os.path.dirname(save_path), exist_ok=True)
+
+ # 2. Save the response to the file
+ with open(save_path, "w", encoding="utf-8") as f:
+ try:
+ # Use json.dump() with the file object, not json.dumps()
+ json.dump(json_data, f, ensure_ascii=False, indent=2)
+ print(f"save_path is: {save_path}")
+ return save_path
+ except Exception as e:
+ print(f"Error saving response to json file: {str(e)}")
+ return None
+
+ # -------------------------------------------------------------------
+ # Generic prompt runner that works with any prompt template
+ # -------------------------------------------------------------------
+ def run_prompt(self, prompt_key, template_vars, ai_model, debug=False):
+ """Generic prompt runner that works with any prompt template.
+
+ Args:
+ prompt_key (str): The key for the prompt template (e.g., "prompt_1", "prompt_2")
+ template_vars (dict): dict of variables to pass to the template
+ ai_model (ModelInterface): The AI model to use for generating responses
+ debug (bool, optional): whether to print debug information. Defaults to False.
+
+ Returns:
+ dict: the parsed YAML
+
+ Raises:
+ Exception: If there's an error running the prompt
+
+ Example:
+ >>> prompt_key = "prompt_1"
+ >>> template_vars = {"name": "John Doe", "age": 30}
+ >>> ai_model = OpenAI(api_key="your_api_key")
+ >>> parsed = run_prompt(prompt_key, template_vars, ai_model, debug=True)
+ >>> print(parsed)
+ """
+ # 1. Load the chain prompts from the JSON file
+ with open(self.chain_prompts_path, "r", encoding="utf-8") as f:
+ chain_prompts = yaml.safe_load(f)
+
+ # 2. Grab the raw prompt template text
+ prompt_template = chain_prompts[prompt_key]
+
+ # 3. Use Jinja2 to fill placeholders
+ template = Template(prompt_template)
+ prompt_text = template.render(**template_vars)
+
+ if debug:
+ print("--------------------------------")
+ print(f"prompt key is:")
+ print (prompt_key)
+ print("--------------------------------")
+ print(f"prompt text is:")
+ print (prompt_text)
+ print("--------------------------------")
+
+ # 4. Call the LLM
+ response = ai_model.generate_response(prompt_text)
+
+ if debug:
+ print("--------------------------------")
+ print(f"response is:")
+ print(response)
+ print("--------------------------------")
+
+ # # 5. Parse the YAML from the LLM's response
+ json_response = self.process_and_save_agent_response(response)
+
+ if debug:
+ print("--------------------------------")
+ print(f"json_response is:")
+ print(json_response)
+ print("--------------------------------")
+ print(f"json_response is a {type(json_response)}")
+ print("--------------------------------")
+
+ if json_response is None:
+ # Handle parse error or fallback
+ print(f"Error: LLM returned invalid JSON for {prompt_key}.")
+ return None
+
+ # 6. return json_response
+ return json_response
+
+ def merge_agent_details(self, master_data: dict, agent_data: dict) -> dict:
+ """Merges agent details by replacing fields in master with agent data.
+
+ Args:
+ master_data (dict): The master template data
+ agent_data (dict): The agent-specific data to merge
+
+ Returns:
+ dict: Updated master data with agent details
+ """
+ if isinstance(master_data, str):
+ master_data = json.loads(master_data)
+
+ # Deep copy to avoid modifying original
+ result = copy.deepcopy(master_data)
+
+ # Replace agent_details section
+ if "agent" in agent_data and "agent_details" in agent_data["agent"]:
+ result["agent"]["agent_details"] = agent_data["agent"]["agent_details"]
+ else:
+ print("Error: agent_details not found in agent_data")
+
+ return result
+
+ def append_seasons(self, master_data: dict, seasons_data: dict) -> dict:
+ """Appends new seasons data to existing master data.
+
+ Args:
+ master_data (dict): The master template data
+ seasons_data (dict): The seasons data to append
+
+ Returns:
+ dict: Updated master data with appended seasons
+ """
+ if isinstance(master_data, str):
+ master_data = json.loads(master_data)
+
+ result = copy.deepcopy(master_data)
+
+ # Append new seasons to existing seasons list
+ if "seasons" in seasons_data:
+ if "seasons" not in result["agent"]:
+ result["agent"]["seasons"] = []
+ result["agent"]["seasons"].extend(seasons_data["seasons"])
+
+ return result
+
+ def initialize_seasons(self, master_data: dict, seasons_data: dict) -> dict:
+ """Initializes the seasons array in the master data.
+
+ Args:
+ master_data (dict): The master template data
+ seasons_data (dict): The seasons data to initialize
+
+ Returns:
+ dict: Updated master data with initialized seasons
+ """
+
+ if isinstance(master_data, str):
+ master_data = json.loads(master_data)
+
+ # Deep copy to avoid modifying original
+ result = copy.deepcopy(master_data)
+
+ if "agent" in master_data and "seasons" in master_data["agent"]:
+ result["agent"]["seasons"] = seasons_data["seasons"]
+
+ #print(f"result is: {result}")
+ return result
+
+ def append_episodes(self, master_data: dict, posts_data: dict, season_index: int, episode_index: int) -> dict:
+ """Appends new episodes data to existing season data.
+
+ Args:
+ master_data (dict): The master template data
+ episodes_data (dict): The episodes data to append
+ season_index (int): Index of the season to update
+ episode_index (int): Index of the episode to update
+
+ Returns:
+ dict: Updated master data with appended episodes
+ """
+ if isinstance(master_data, str):
+ master_data = json.loads(master_data)
+
+ result = copy.deepcopy(master_data)
+
+ # Validate the nested structure exists
+ if "agent" not in result:
+ result["agent"] = {}
+ if "seasons" not in result["agent"]:
+ result["agent"]["seasons"] = []
+ if season_index >= len(result["agent"]["seasons"]):
+ result["agent"]["seasons"].append({"episodes": []})
+ if "episodes" not in result["agent"]["seasons"][season_index]:
+ result["agent"]["seasons"][season_index]["episodes"] = []
+ if episode_index >= len(result["agent"]["seasons"][season_index]["episodes"]):
+ result["agent"]["seasons"][season_index]["episodes"].append({})
+
+ # Replace posts array with new data
+ if "posts" in posts_data:
+ result["agent"]["seasons"][season_index]["episodes"][episode_index]["posts"] = posts_data["posts"]
+
+ return result
+
+ def append_profile_image_options(self, master_data: dict, profile_image_data: dict) -> dict:
+ """Appends new profile image data to existing master data.
+
+ Args:
+ master_data (dict): The master template data
+ profile_image_data (dict): The profile image data to append
+
+ Returns:
+ dict: Updated master data with appended profile image
+ """
+
+ if isinstance(master_data, str):
+ master_data = json.loads(master_data)
+
+ # Deep copy to avoid modifying original
+ result = copy.deepcopy(master_data)
+
+ # Append new profile image to existing profile image list
+ if "profile_image_options" in profile_image_data:
+ if "profile_image_options" not in result["agent"]:
+ result["agent"]["profile_image_options"] = []
+
+ # replace the profile image with the new profile image
+ result["agent"]["profile_image_options"] = profile_image_data["profile_image_options"]
+
+ #print(f"result is: {result}")
+ return result
+
+ def append_profile_image(self, master_data: dict, profile_image_data: dict) -> dict:
+ """Appends new profile image data to existing master data.
+
+ Args:
+ master_data (dict): The master template data
+ profile_image_data (dict): The profile image data to append
+
+ Returns:
+ dict: Updated master data with appended profile image
+ """
+
+ if isinstance(master_data, str):
+ master_data = json.loads(master_data)
+
+ # Deep copy to avoid modifying original
+ result = copy.deepcopy(master_data)
+
+ # Append new profile image to existing profile image list
+ if "profile_image" in profile_image_data:
+ if "profile_image" not in result["agent"]:
+ result["agent"]["profile_image"] = []
+
+ # replace the profile image with the new profile image
+ result["agent"]["profile_image"] = profile_image_data["profile_image"]
+
+ #print(f"result is: {result}")
+ return result
+
+ def get_agent_names_blacklist(self):
+ """Get a list of agent names based on folder names in config folder."""
+ return [name for name in os.listdir(self.agents_config_dir) if os.path.isdir(os.path.join(self.agents_config_dir, name))]
\ No newline at end of file
diff --git a/server/utils/post_manager.py b/server/utils/post_manager.py
new file mode 100644
index 0000000..8992184
--- /dev/null
+++ b/server/utils/post_manager.py
@@ -0,0 +1,241 @@
+#
+# Module: post_manager
+#
+# This module implements the PostManager class for managing the posts for an agent.
+#
+# Title: Post Manager
+# Summary: Post manager implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2024-12-31
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-04
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+
+# standard imports
+import os
+import json
+import datetime
+from dotenv import load_dotenv
+import sys
+
+# Add project root to Python path
+project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(project_root)
+
+# custom ARAI code imports
+import connectors.twitter_connector as twitter
+from connectors.twitter_api_free_connector import (
+ login_and_save_state,
+ post_tweet_with_saved_state
+)
+from handlers import menu_handlers
+
+# Load environment variables
+load_dotenv()
+
+# Define constants from environment variables
+X_ENABLED = os.getenv("X_ENABLED", "False")
+X_API_OFFICIAL = os.getenv("X_API_OFFICIAL", "False")
+X_USERNAME = os.getenv("X_USERNAME", "")
+X_PASSWORD = os.getenv("X_PASSWORD","")
+X_PHONE_OR_EMAIL = os.getenv("X_PHONE_OR_EMAIL", "")
+X_DRY_RUN = os.getenv("X_DRY_RUN", "True")
+
+class PostManager:
+ """Manages posts for an agent using JSON configuration files.
+
+ Attributes:
+ agent_name (str): The name of the agent
+ master_file (str): Path to the agent's master JSON file
+ twitter_connector: Twitter API connector instance
+ """
+ def __init__(self, agent_name: str):
+ """Initialize the post manager
+
+ Args:
+ agent_name (str): The name of the agent
+
+ Raises:
+ FileNotFoundError: If master JSON file doesn't exist
+ """
+ self.agent_name = agent_name
+ self.master_file = os.path.join("configs", agent_name, f"{agent_name}_master.json")
+ self.is_logged_in = False
+
+ # Initialize Twitter connectors based on environment variable
+ if X_API_OFFICIAL == "True":
+ print(f"[Post Manager] Initializing Twitter Connector with official API")
+ self.twitter_connector = twitter.TwitterConnector()
+ self.twitter_api_free = None
+ else:
+ print(f"[Post Manager] Initializing Twitter API Free")
+ self.twitter_connector = None
+ self.twitter_api_free = login_and_save_state(username=X_USERNAME, password=X_PASSWORD, phone_or_email=X_PHONE_OR_EMAIL)
+ if self.twitter_api_free:
+ self.is_logged_in = True
+
+ # Load master configuration
+ with open(self.master_file, 'r', encoding='utf-8') as f:
+ self.master_data = json.load(f)
+
+ # Get current tracking info
+ self.tracker = self.master_data["agent"]["tracker"]
+ self.current_season = self.tracker["current_season_number"]
+ self.current_episode = self.tracker["current_episode_number"]
+ self.current_post = self.tracker["current_post_number"]
+
+ def _save_master_file(self):
+ """Save the master JSON file with updated tracking information"""
+ # Update master data from local variables
+ self.master_data["agent"]["tracker"]["current_season_number"] = self.current_season
+ self.master_data["agent"]["tracker"]["current_episode_number"] = self.current_episode
+ self.master_data["agent"]["tracker"]["current_post_number"] = self.current_post
+
+ # Save to file
+ with open(self.master_file, 'w', encoding='utf-8') as f:
+ json.dump(self.master_data, f, indent=2)
+
+ def change_season(self, season_number: int):
+ """Change the current season
+
+ Args:
+ season_number (int): The season number (1-based, will be converted to 0-based for array access)
+ """
+ # Convert from 1-based season number to 0-based array index
+ season_index = season_number
+
+ # Check if season exists and is not posted yet
+ if 0 <= season_index < len(self.master_data["agent"]["seasons"]):
+ print(f"Checking season: {season_index}")
+ season = self.master_data["agent"]["seasons"][season_index]
+ if not season.get("season_posted", False):
+ self.current_season = season_index # Store as 0-based index
+ self.current_episode = 0
+ self.current_post = 0
+ self._save_master_file()
+ print(f"Changed to season {season_number}") # Display as 1-based number
+ else:
+ print(f"Season {season_number} has already been posted")
+ raise ValueError(f"Season {season_number} has already been posted")
+ else:
+ print("No more unposted seasons available")
+ # Reset to beginning
+ self.current_season = -1
+ self.current_episode = -1
+ self.current_post = -1
+ self._save_master_file()
+ raise ValueError(f"No more content available to post")
+
+ def change_episode(self, episode_number: int):
+ """Change the current episode
+
+ Args:
+ episode_number (int): The episode number (1-based, will be converted to 0-based for array access)
+ """
+ # Convert from 1-based episode number to 0-based array index
+ episode_index = episode_number
+
+ current_season = self.master_data["agent"]["seasons"][self.current_season]
+ if episode_index >= len(current_season["episodes"]):
+ print(f"Moving to next season: {self.current_season + 1}")
+ self.change_season(self.current_season + 1) # Move to next season
+ return
+
+ self.current_episode = episode_index
+ self.current_post = 0
+ self._save_master_file()
+ print(f"Changed to episode {episode_number}") # Display as 1-based number
+
+ def next_post_number(self, post_number: int) -> str:
+ """Get the next post content and update tracking
+
+ Args:
+ post_number (int): The post number (1-based, will be converted to 0-based for array access)
+ """
+ # Convert from 1-based post number to 0-based array index
+ post_index = post_number
+
+ current_season = self.master_data["agent"]["seasons"][self.current_season]
+ current_episode = current_season["episodes"][self.current_episode]
+
+ if post_index >= len(current_episode["posts"]):
+ print(f"Moving to next episode: {self.current_episode + 1}")
+ self.change_episode(self.current_episode + 1) # Move to next episode
+ post_index = 0
+
+ print(f"Getting post: {post_index}")
+ post = current_episode["posts"][post_index]
+ post_content = post["post_content"]
+
+ self.current_post = post_index + 1 # Store as 1-based for consistency with display
+ self._save_master_file()
+
+ return post_content
+
+ def post_to_twitter(self):
+ """Post content to Twitter"""
+ tweet_content = self.next_post_number(self.current_post)
+
+ if X_ENABLED:
+ if X_API_OFFICIAL:
+ if self.twitter_connector and not X_DRY_RUN:
+ tweet_result = self.twitter_connector.post_tweet(tweet_content)
+ else:
+ print(f"[Post Manager] - Dry run mode, not posting to Twitter", tweet_content)
+ else:
+ if self.twitter_api_free and not X_DRY_RUN:
+ tweet_result = post_tweet_with_saved_state(tweet_content)
+ else:
+ print(f"[Post Manager] - Dry run mode, not posting to Twitter", tweet_content)
+
+
+ print(f"[Post Manager] Tweet result: {tweet_result}")
+
+ if tweet_result.startswith("Error"):
+ self.current_post -= 1
+ self.tracker["current_post_number"] = self.current_post
+ self._save_master_file()
+ else:
+ print(f"\nWould have posted: \n{tweet_content}")
+
+ # Log the post
+ log_file = os.path.join("configs", self.agent_name, f"{self.agent_name}_post_log.json")
+ try:
+ try:
+ with open(log_file, 'r', encoding='utf-8') as f:
+ log_data = json.load(f)
+ except FileNotFoundError:
+ log_data = {"posts": []}
+
+ log_data["posts"].append({
+ "post_id": f"s_{self.current_season + 1}_e_{self.current_episode + 1}_p_{self.current_post}",
+ "content": tweet_content,
+ "timestamp": datetime.datetime.now().isoformat()
+ })
+
+ with open(log_file, 'w', encoding='utf-8') as f:
+ json.dump(log_data, f, indent=2)
+
+ except Exception as e:
+ print(f"Error writing to log file: {e}")
+
+ @classmethod
+ def post_single_tweet(cls, tweet_content: str) -> str:
+ """Class method to post a single tweet without needing to instantiate PostManager
+
+ Args:
+ tweet_content (str): The content to tweet
+
+ Returns:
+ str: Result of the tweet operation
+ """
+ if X_ENABLED == "True" and X_DRY_RUN == "False":
+ return post_tweet_with_saved_state(tweet_content)
+ else:
+ print(f"[Post Manager] - Dry run mode, would have posted: {tweet_content}")
+ return "Dry run - tweet not posted"
+
diff --git a/server/utils/scheduler.py b/server/utils/scheduler.py
new file mode 100644
index 0000000..c6bd34b
--- /dev/null
+++ b/server/utils/scheduler.py
@@ -0,0 +1,109 @@
+# standard imports
+import threading
+import schedule
+import time
+import os
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+class AgentScheduler:
+ def __init__(self):
+ self.scheduler_running = False
+ self.pause_event = threading.Event()
+ self.scheduler_thread = None
+
+ def run_scheduler(self):
+ """Continuously run the scheduler in a loop"""
+ while self.scheduler_running:
+ if not self.pause_event.is_set():
+ schedule.run_pending()
+ time.sleep(1)
+
+ def start(self):
+ """Start the scheduler thread"""
+ if not self.scheduler_thread or not self.scheduler_thread.is_alive():
+ self.scheduler_running = True
+ self.scheduler_thread = threading.Thread(target=self.run_scheduler)
+ self.scheduler_thread.daemon = True
+ self.scheduler_thread.start()
+
+ def stop(self):
+ """Stop the scheduler thread"""
+ self.scheduler_running = False
+ if self.scheduler_thread:
+ self.scheduler_thread.join()
+
+ def pause(self):
+ """Pause the scheduler"""
+ self.pause_event.set()
+
+ def resume(self):
+ """Resume the scheduler"""
+ self.pause_event.clear()
+
+ def is_paused(self):
+ """Check if scheduler is paused"""
+ return self.pause_event.is_set()
+
+ def is_running(self):
+ """Check if scheduler is running"""
+ return self.scheduler_running and (self.scheduler_thread and self.scheduler_thread.is_alive())
+
+ def schedule_posts(self, post_manager, tracker_data):
+ """Schedule posts with special handling for first post"""
+
+ # Schedule first post right away
+ self.schedule_first_post(post_manager, tracker_data)
+
+ # check what we to do the scheduler if all content is posted
+ self.schedule_last_post(post_manager, tracker_data)
+
+ # Schedule regular posts
+ # Production schedule:
+ if os.getenv("X_ENABLED") == "True":
+ schedule.every(tracker_data.get('post_every_x_minutes')).minutes.do(post_manager.post_to_twitter)
+ else:
+ # Test schedule if you want to :
+ schedule.every(5).seconds.do(post_manager.post_to_twitter)
+
+ def schedule_first_post(self, post_manager, tracker_data):
+ """Schedule posts with special handling for first post"""
+ if (tracker_data['season_number'] == 0 and
+ tracker_data['episode_number'] == 0 and
+ tracker_data['post_number'] == 0):
+
+ # Post immediately for first post of first episode/season
+ post_manager.post_to_twitter()
+
+ def schedule_last_post(self, post_manager, tracker_data):
+ if (tracker_data['season_number'] == -1 and
+ tracker_data['episode_number'] == -1 and
+ tracker_data['post_number'] == -1):
+
+ # check what we to do the scheduler if all content is posted
+ self.check_generate_post_env(tracker_data)
+
+ def check_generate_post_env(self, tracker_data):
+ """Check what settings the auto generate posts is set to"""
+
+ if os.getenv("AUTO_GENERATE_POSTS") == "AUTO":
+ # generate new content
+ # post_manager.create_content()
+
+ # tracker_data['season_number'] = length seasons -1
+ # tracker_data['episode_number'] = 0
+ # tracker_data['post_number'] = 0
+ self.stop()
+
+ elif os.getenv("AUTO_GENERATE_POSTS") == "LOOP":
+ tracker_data['season_number'] = 0
+ tracker_data['episode_number'] = 0
+ tracker_data['post_number'] = 0
+
+ elif os.getenv("AUTO_GENERATE_POSTS") == "STOP":
+ self.stop()
+ else:
+ print("Auto generate posts is not set to a valid setting")
+ print("Please set AUTO_GENERATE_POSTS to AUTO, LOOP, or STOP")
diff --git a/server/utils/template_types.py b/server/utils/template_types.py
new file mode 100644
index 0000000..fe7b26c
--- /dev/null
+++ b/server/utils/template_types.py
@@ -0,0 +1,34 @@
+#
+# Module: template_types
+#
+# This module implements the TemplateType class for defining the type of template.
+#
+# Title: Template Types
+# Summary: Template types implementation.
+# Authors:
+# - @TheBlockRhino
+# Created: 2024-12-31
+# Last edited by: @TheBlockRhino
+# Last edited date: 2025-01-04
+# URLs:
+# - https://arai-ai.io
+# - https://github.com/ARAI-DevHub/arai-ai-agents
+# - https://x.com/TheBlockRhino
+
+class TemplateType:
+ """This class is used to define the type of template.
+
+ Attributes:
+ AGENT (str): the type of template for an agent
+ SEASON (str): the type of template for a season
+ EPISODE (str): the type of template for an episode
+ PROFILE_PICTURE (str): the type of template for a profile picture
+ """
+ MASTER = "master"
+ TRACKER = "tracker"
+ AGENT = "agent"
+ SEASON = "season"
+ EPISODE = "episode"
+ PROFILE_IMAGE = "profile_image"
+ PROFILE_IMAGE_OPTIONS = "profile_image_options"
+ CHAT = "chat"
\ No newline at end of file