README.md

metadata

title: Python to C++ Code Optimizer
emoji: 🚀
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit

🚀 Python to C++ Code Optimizer

An AI-powered tool that converts Python code to high-performance C++ using GPT-4o and Claude-3.5-Sonnet.

🎯 What It Does

This application takes Python code as input and uses frontier AI models to automatically convert it to optimized C++ code. It then allows you to:

• Compare the generated C++ code side-by-side with the original Python
• Execute both versions and compare performance
• See real-world speedups (typically 10-100x faster)

🔐 Password Protection

This Space is password-protected. To access:

1. Username: user
2. Password: Contact the Space owner for the password

The password helps limit access while keeping the Space publicly discoverable.

⚠️ Security Warning

IMPORTANT: This application executes arbitrary code (both Python and C++).

• Only run code from trusted sources
• Malicious code can harm the system
• Use at your own risk
• Not recommended for production use without proper sandboxing

🔧 Setup Instructions

For Hugging Face Spaces

1. Fork or duplicate this Space
2. Go to Settings → Repository secrets
3. Add the following secrets:
   • OPENAI_API_KEY - Your OpenAI API key from https://platform.openai.com/api-keys
   • ANTHROPIC_API_KEY - Your Anthropic API key from https://console.anthropic.com/
   • APP_PASSWORD - Your chosen password for accessing the app (e.g., mySecurePass123)

For Local Development

1. Clone the repository
2. Install dependencies:

   pip install -r requirements.txt
   

3. Set environment variables:

   export OPENAI_API_KEY="your-openai-key"
   export ANTHROPIC_API_KEY="your-anthropic-key"
   

4. Run the app:

   python app.py
   

For C++ Compilation

The app requires a C++ compiler:

• Linux: g++ (usually pre-installed)
• macOS: Install Xcode Command Line Tools
• Windows: Install MinGW or use WSL

The default compilation command uses g++. You may need to adjust the compiler flags in app.py for your platform.

🎓 Educational Use

This project is designed for educational purposes to demonstrate:

• AI-powered code generation and optimization
• Performance differences between Python and C++
• Real-time streaming from AI models
• Interactive code execution environments

📊 Example Performance

Pi Calculation (100M iterations):

• Python: ~6.3 seconds
• C++ (optimized): ~0.6 seconds
• Speedup: ~10x

Maximum Subarray (10K elements, 20 runs):

• Python: ~45 seconds
• C++ (optimized): ~0.4 seconds
• Speedup: ~110x

🛡️ Safety Features

• Execution timeouts (30 seconds)
• Error handling for compilation failures
• Clear security warnings in UI
• Sandboxed execution recommended for production

📝 Model Options

Choose between two frontier models:

1. GPT-4o (OpenAI)

   • Excellent at code generation
   • Good optimization strategies
   • Fast streaming responses

2. Claude-3.5-Sonnet (Anthropic)

   • Strong code understanding
   • Detailed optimization
   • High-quality output

💰 Cost Considerations

Both models are paid APIs:

• GPT-4o: ~$5 per million input tokens
• Claude-3.5-Sonnet: ~$3 per million input tokens

For ultra-low cost, modify the code to use:

• gpt-4o-mini (20x cheaper)
• claude-3-haiku (15x cheaper)

🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Submit a pull request

📄 License

MIT License - feel free to use and modify as needed.

⚡ Credits

Created as part of an LLM Engineering course demonstrating practical applications of frontier AI models.

🔗 Resources

• OpenAI Platform
• Anthropic Console
• Gradio Documentation
• C++ Performance Optimization

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Disclaimer: This tool is for educational and research purposes. Always review generated code before using in production. The developers assume no liability for damages caused by code execution.