MultiAgent Chain of Expert is an advanced Python application designed for sophisticated text processing using the Groq API. It employs a dual-model architecture, leveraging Gemma for in-depth input analysis and LLaMA for generating polished, context-aware responses. The application features a modern, dark-themed GUI built with tkinter and ttkbootstrap, offering a seamless user experience with history tracking, file handling, and customizable AI parameters.

• Dual-Model AI Processing: Combines Gemma for structured analysis and LLaMA for comprehensive responses.
• Modern User Interface: Sleek, dark-themed GUI with intuitive controls, progress bars, and real-time feedback.
• History Management: Saves processing history in JSON format for easy review and retrieval.
• Flexible Configuration: Adjust AI parameters (temperature, max tokens) via a dedicated settings panel.
• Robust File Handling: Supports loading text files, saving results as TXT or JSON, and clipboard operations.
• Secure API Integration: Manages Groq API keys securely using python-dotenv and .env files.
• Error Handling & Feedback: Provides clear error messages and API connection status monitoring.

Follow these steps to set up the project locally:

1. Clone the Repository:

   git clone https://github.com/Arash-Mansourpour/MultiAgent-Chain-of-Expert.git
   cd MultiAgent-Chain-of-Expert

2. Install Dependencies: Ensure Python 3.8+ is installed, then run:

   pip install -r requirements.txt

3. Set Up Environment Variables: Create a .env file in the project root and add your Groq API key:

   GROQ_API_KEY=your-groq-api-key-here
   

4. Run the Application:

   python multi-agent.py

1. Input Text: Enter your query or text in the input area.
2. Configure Settings: Adjust AI parameters (temperature, max tokens) in the Settings tab if needed.
3. Process: Click "Process Text" to generate analysis (Gemma) and final response (LLaMA).
4. Review & Save: View results in dedicated panels, copy to clipboard, save as TXT/JSON, or access past interactions via the History tab.

• Python: 3.8 or higher
• Dependencies:
  • ttkbootstrap: For modern GUI styling
  • groq: For API integration
  • python-dotenv: For secure API key management
  • See requirements.txt for a complete list

Install dependencies:

pip install ttkbootstrap groq python-dotenv

• API Key: Store your Groq API key in a .env file, which is excluded from version control via .gitignore.
• Data Privacy: History and configuration files (ai_processor_history.json, ai_processor_config.json) are stored locally and not tracked in the repository.

MultiAgent-Chain-of-Expert/
├── multi-agent.py         # Main application code
├── requirements.txt       # Project dependencies
├── .gitignore            # Ignored files and directories
├── .env.example          # Sample environment file
├── LICENSE               # MIT License
└── README.md             # Project documentation

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -m "Add your feature").
4. Push to the branch (git push origin feature/your-feature).
5. Open a Pull Request.

Please ensure your code adheres to PEP 8 guidelines and includes appropriate documentation.

This project is licensed under the MIT License.

For questions or feedback, reach out to Arash Mansourpour or open an issue on this repository.

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