A modern, interactive AI chatbot built with Flask and Mistral-7B model. Features a beautiful web interface with real-time messaging, typing indicators, and conversation history.

• 🤖 AI-Powered Responses: Uses Mistral-7B model for intelligent conversations
• 💬 Real-time Messaging: Instant message exchange with typing indicators
• 🎨 Modern UI: Beautiful, responsive design with animations
• 📱 Mobile Friendly: Works perfectly on all devices
• 💾 Conversation History: Saves and loads chat history
• 📤 Export Chat: Export conversations as JSON files
• 🗑️ Clear Chat: Clear conversation history
• ⌨️ Keyboard Shortcuts: Ctrl+Enter to send, Escape to close modals
• 🔄 Auto-resize Input: Textarea automatically resizes as you type
• ⚡ Fast & Lightweight: Optimized for performance

The chatbot features a modern interface with:

• Gradient header with bot avatar and status
• Message bubbles with user/bot avatars
• Typing indicators
• Auto-resizing input field
• Action buttons for clearing and exporting chat

• Python 3.8 or higher
• At least 8GB RAM (for model loading)
• Mistral-7B model file

1. Clone or download the project

   git clone <https://github.com/iremteker/chatbot.git>
   cd chatbot

2. Create virtual environment

   python -m venv venv
   
   # On Windows
   venv\Scripts\activate
   
   # On macOS/Linux
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Download the Mistral model

   Download the Mistral-7B model file and place it in the models/ directory:

   models/
   └── mistral-7b-instruct-v0.1.Q4_K_M.gguf
   

   You can download it from Hugging Face or other sources.

5. Run the application

   python app.py

6. Open in browser

   Navigate to http://localhost:5000 in your web browser.

1. Type your message in the input field
2. Press Enter or click the send button
3. Wait for the AI response
4. Continue the conversation

• Enter: Send message
• Shift + Enter: New line in input
• Ctrl/Cmd + Enter: Send message
• Escape: Close modals

• Clear Chat: Click the trash icon to clear conversation history
• Export Chat: Click the download icon to export chat as JSON
• Auto-scroll: Chat automatically scrolls to latest messages

Send a message and get AI response.

Request:

{
  "message": "Hello, how are you?"
}

Response:

{
  "success": true,
  "response": "Hello! I'm doing well, thank you for asking. How can I help you today?",
  "message_id": "uuid-string"
}

Get conversation history for current session.

Response:

{
  "messages": [
    {
      "id": "uuid",
      "type": "user",
      "message": "Hello",
      "timestamp": "2024-01-01T12:00:00Z"
    }
  ]
}

Clear conversation history for current session.

Check server health status.

Edit main.py to adjust model parameters:

llm = Llama(
    model_path="models/mistral-7b-instruct-v0.1.Q4_K_M.gguf",
    n_ctx=2048,        # Context window size
    n_threads=4,       # Number of CPU threads
    n_gpu_layers=0,    # GPU layers (0 for CPU only)
    verbose=False       # Verbose logging
)

Edit app.py to change server settings:

app.secret_key = 'your-secret-key-here'  # Change for production
app.run(debug=True, host='0.0.0.0', port=5000)

chatbot/
├── app.py              # Flask application
├── main.py             # AI model and response generation
├── requirements.txt    # Python dependencies
├── README.md          # This file
├── models/
│   └── mistral-7b-instruct-v0.1.Q4_K_M.gguf  # AI model
├── static/
│   ├── style.css      # CSS styles
│   └── script.js      # JavaScript functionality
└── templates/
    └── index.html     # HTML template

• Ensure the model file exists in models/ directory
• Check that you have enough RAM (8GB+ recommended)
• Try reducing n_threads if you have limited CPU cores

• Reduce n_ctx (context window) for faster responses
• Use fewer n_threads if CPU is overloaded
• Consider using GPU acceleration if available

• Close other applications to free up RAM
• Restart the application if memory usage is high
• Consider using a smaller model variant

1. Modify app.py for new API endpoints
2. Update main.py for model improvements
3. Edit templates/index.html for UI changes
4. Modify static/style.css for styling
5. Update static/script.js for frontend functionality

• Test on different browsers and devices
• Verify mobile responsiveness
• Check API endpoints with tools like Postman

This project is open source and available under the MIT License.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

If you encounter any issues:

1. Check the troubleshooting section
2. Review the console for error messages
3. Ensure all dependencies are installed
4. Verify the model file is correctly placed

Note: This chatbot uses the Mistral-7B model which requires significant computational resources. For production use, consider using cloud services or more optimized model variants.