AI Web Assistant

A powerful web automation assistant that provides real-time browser control and task execution through an interactive web interface.

Features

• Real-Time Browser Control: Live view of browser actions through WebSocket connection
• Interactive Console: Execute tasks directly from the web interface
• Task Queue Management: Handle multiple sequential tasks with proper queuing and execution
• Robust Error Handling: Automatic retries and recovery strategies
• Visual Feedback: Real-time status updates and browser screenshots
• Cookie Consent Handling: Automatic handling of cookie consent popups
• Extensible Architecture: Easy to add new task types and capabilities

Requirements

• Python 3.8+
• Playwright
• aiohttp
• OpenCV (cv2)
• NumPy
• Pillow

Installation

1. Clone the repository:

git clone https://github.com/airhick/AIwebagent.git
cd AIwebagent

2. Install dependencies:

pip install -r requirements.txt

3. Install Playwright browsers:

playwright install

Usage

1. Start the assistant:

python src/main.py

2. Open your browser and navigate to http://localhost:3000

3. Enter tasks in the interactive console, for example:

• "go to google"
• "search for python programming"
• "go to reddit"

Project Structure

.
├── src/
│   ├── main.py                 # Entry point
│   ├── web_assistant.py        # Core assistant class
│   ├── task_executor.py        # Task execution logic
│   ├── task_planner.py         # Task analysis and planning
│   ├── task_queue.py           # Async task queue management
│   └── element_detector.py     # Visual element detection
├── browser_viewer/
│   ├── index.html             # Web interface
│   └── browser_state.json     # Current browser state
└── browser_screenshots/       # Screenshot storage

Features in Detail

Real-Time Browser Control

• WebSocket-based communication
• Live screenshot updates
• Task status monitoring
• Interactive console input

Task Management

• Asynchronous task queue
• Automatic retries on failure
• Progress tracking
• Error recovery strategies

Visual Processing

• Screenshot capture and analysis
• Element detection
• Visual state verification
• Change detection

Error Handling

• Automatic recovery attempts
• Detailed error logging
• Status updates
• Retry mechanisms

Contributing

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

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Playwright for browser automation
• aiohttp for async web server
• OpenCV for visual processing