Add comprehensive GitHub Copilot-like chat interface for Browser.AI automation

[Copilot]

This PR introduces a complete chat interface for Browser.AI that provides a GitHub Copilot-like experience for web automation tasks. Users can now describe automation tasks in natural language and monitor their execution in real-time through an intuitive chat interface.

Key Features

🤖 GitHub Copilot-Style Chat Interface

• Natural language task description with real-time responses
• Familiar chat UI that developers will recognize from VS Code
• Animated status indicators and live progress updates
• Complete conversation history with timestamps

🔄 Real-Time Task Monitoring

• WebSocket-based live log streaming from Browser.AI execution
• Task lifecycle management (create, start, stop, monitor)
• Real-time status updates with animated indicators
• System health monitoring and connection status

⚙️ Multi-Provider LLM Support

• Support for OpenAI, Anthropic, and Ollama providers
• Easy configuration management with validation
• API key protection and secure handling
• Provider-specific model selection and settings

🎨 Dual Interface Options

• Modern Web App: Responsive HTML/CSS/JS interface with professional styling
• Streamlit GUI: Python-based interface for easy deployment and customization
• Mobile-responsive design that works on all devices

Architecture

The implementation follows a clean, modular architecture without modifying any existing Browser.AI code:

chat_interface/
├── backend/           # FastAPI server with WebSocket support
├── streamlit_gui/     # Streamlit-based interface 
├── web_app/          # Modern HTML/CSS/JS interface
└── launcher.py       # One-click deployment script

Backend Components

• FastAPI Server: REST API + WebSocket endpoints for real-time communication
• Task Manager: Orchestrates Browser.AI agent execution with lifecycle management
• Event Adapter: Custom logging handler that captures Browser.AI logs without intrusion
• WebSocket Handler: Manages real-time client connections and message routing
• Config Manager: Handles LLM provider configuration and validation

Non-Intrusive Integration

The implementation uses an event listener adapter pattern that captures Browser.AI logs through custom logging handlers, ensuring zero modifications to the existing Browser.AI codebase while providing complete real-time monitoring capabilities.

Usage Examples

Users can now describe automation tasks naturally:

"Navigate to Google and search for 'Browser.AI automation'"
"Go to Amazon, find wireless headphones under $100" 
"Visit GitHub, star the Browser.AI repository"
"Open LinkedIn, update my headline to 'AI Automation Expert'"

Quick Start

# Install dependencies
pip install -r chat_interface/requirements.txt

# Configure LLM provider
export OPENAI_API_KEY="your-api-key"

# Launch web interface
cd chat_interface && python launcher.py --web-app

# Or launch Streamlit GUI
python launcher.py --streamlit

Technical Highlights

• Event-Driven Architecture: Real-time updates through WebSocket communication
• Type-Safe Implementation: Complete type hints and Pydantic model validation
• Production-Ready: Comprehensive error handling, logging, and monitoring
• Scalable Design: Supports multiple concurrent tasks and client connections
• Security Conscious: API key protection, input validation, and secure WebSocket handling

This enhancement significantly improves the Browser.AI user experience by providing a modern, intuitive interface for web automation while maintaining full compatibility with existing functionality.

Created from VS Code via the GitHub Pull Request extension.

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