🤖 IntelliDebug

A powerful web-based Python debugger with AI-powered error explanations and fix suggestions using Google's Gemini AI. Execute Python code safely in a sandboxed environment and get intelligent debugging assistance.

✨ Features

• 🔒 Safe Code Execution: Sandboxed Python code execution with timeout protection
• 🤖 AI-Powered Analysis: Intelligent error explanations using Google Gemini AI
• 🔍 Syntax Validation: Real-time syntax error detection before execution
• ⚡ Runtime Debugging: Comprehensive runtime error analysis and handling
• 🌐 Modern Web Interface: Beautiful, responsive UI with syntax highlighting
• 📊 Real-time Results: Instant feedback with AI-powered suggestions
• 💡 Educational Focus: Student-friendly explanations and learning approach

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• Google Gemini API key (for AI features)

Installation

1. Clone and setup

   git clone <repository-url>
   cd IntelliDebug

2. Create virtual environment

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

3. Install dependencies

   pip install -r requirements.txt

4. Configure environment Create a .env file:

   GEMINI_API_KEY=your_google_gemini_api_key_here
   

5. Run the application

   python app.py

Visit http://127.0.0.1:5000 to start debugging!

📖 Usage

Web Interface

• Navigate to the web interface in your browser
• Enter Python code in the editor
• Click "Run Code" to execute and get AI analysis
• View detailed explanations and suggested fixes

API Usage

Debug Endpoint (POST /debug)

curl -X POST http://localhost:5000/debug \
  -H "Content-Type: application/json" \
  -d '{"code": "print(\"Hello, World!\")"}'

Response:

{
  "success": true,
  "output": "Hello, World!",
  "error": null,
  "ai_explanation": "Code executed successfully! 🎉"
}

Health Check (GET /health)

curl http://localhost:5000/health

🛡️ Security Features

• Sandboxed Execution: Code runs in isolated subprocesses
• Timeout Protection: 5-second execution limit prevents hanging
• Output Limits: Prevents memory exhaustion from large outputs
• Input Validation: Comprehensive request validation
• Error Sanitization: Safe error message handling

⚙️ Configuration

Environment Variables

• GEMINI_API_KEY: Your Google Gemini API key (required for AI features)
• HOST: Server host (default: 127.0.0.1)
• PORT: Server port (default: 5000)
• FLASK_DEBUG: Enable debug mode (default: True)

Optional Settings

• MAX_EXECUTION_TIME: Code execution timeout in seconds (default: 5)
• MAX_OUTPUT_LENGTH: Maximum output length (default: 2000)

🔧 Development

Project Structure

IntelliDebug/
├── app.py              # Main Flask application
├── requirements.txt    # Python dependencies
├── README.md          # Project documentation
├── .env               # Environment variables
├── .gitignore        # Git ignore rules
└── templates/
    └── index.html    # Web interface

Local Development

# Enable debug mode
export FLASK_DEBUG=True
python app.py

# Use custom port
export PORT=8000
python app.py

🐛 Troubleshooting

Common Issues

AI features not working

• Verify GEMINI_API_KEY is set correctly
• Check API key permissions in Google AI Studio
• Ensure stable internet connection

Code execution timeout

• Code execution limited to 5 seconds by default
• Check for infinite loops in your code
• Consider optimizing long-running algorithms

Port already in use

# Use different port
export PORT=8000
python app.py

# Or kill existing process
lsof -ti:5000 | xargs kill -9

Debug Information

Check debugger.log for detailed error information and troubleshooting data.

📊 API Reference

Endpoints

Method	Endpoint	Description
POST	/debug	Execute Python code and get analysis
GET	/health	Health check and service status
GET	/	Web interface

Debug Request Format

{
  "code": "your_python_code_here"
}

Debug Response Format

{
  "success": boolean,
  "output": string,
  "error": string|null,
  "error_type": "syntax"|"runtime"|null,
  "ai_explanation": string,
  "suggested_fix": string|null,
  "fix_explanation": string|null
}

📷 Screenshot

---

Made with ❤️ by Tuhin for Python learners

For more information, visit our GitHub repository.