🤗 MemoryHug

AI-powered photo merging web app - Create beautiful memories by merging your past and present photos!

🌟 Features

• 📸 Dual Photo Upload - Upload childhood and current photos
• 🎨 AI-Powered Merging - Intelligent photo blending with face detection
• 🎭 Multiple Styles - Classic, Cinematic, Vintage, Photoreal
• ⚙️ Advanced Controls - Blend amount, opacity, background effects
• 👀 Before/After Comparison - Interactive slider
• 📥 Download & Share - Save or share your memories
• 📱 Fully Responsive - Works on mobile, tablet, and desktop
• ♿ Accessible - WCAG compliant with keyboard navigation

🏗️ Project Structure

MemoryHug/
├── frontend-files/          # Frontend (HTML/CSS/JS)
│   ├── index.html          # Main page
│   ├── app.js              # JavaScript logic
│   ├── styles.css          # Styling
│   └── config.js           # Configuration
│
├── backend/                # Backend API (FastAPI)
│   ├── app.py              # Main FastAPI app
│   ├── config.py           # Configuration
│   ├── requirements.txt    # Python dependencies
│   ├── Dockerfile          # Docker configuration
│   ├── utils/              # Image processing utilities
│   └── models/             # AI model integration
│
└── README.md               # This file

🚀 Quick Start

Frontend (Local)

# Clone repository
git clone https://github.com/ayax-khan/MemoryHug.git
cd MemoryHug/frontend-files

# Open in browser
# Simply open index.html in your browser
# OR use a local server:
python -m http.server 8080
# Visit: http://localhost:8080

Backend (Local)

cd backend

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

# Install dependencies
pip install -r requirements.txt

# Run server
python app.py
# API will run at: http://localhost:7860

Full Stack Local Development

1. Start backend (port 7860)
2. Update frontend config.js:

   const API_BASE_URL = 'http://localhost:7860';

3. Open frontend in browser

🌐 Live Deployment

Frontend: Vercel

• Auto-deploys from main branch
• Live at: https://memoryhug.vercel.app

Backend: Hugging Face Spaces

• Deployed as Docker container
• API at: https://zeroday-00-past-present-backend.hf.space

🛠️ Tech Stack

Frontend

• HTML5, CSS3 (Modern)
• Vanilla JavaScript (ES6+)
• Google Fonts (Inter, Poppins)

Backend

• Framework: FastAPI (Python)
• Image Processing: Pillow, OpenCV
• Face Detection: Haar Cascade (CPU-friendly)
• AI Model: Stable Diffusion (optional, fallback blending default)
• Deployment: Docker on Hugging Face Spaces

📋 Requirements

Frontend

• Modern browser (Chrome 90+, Firefox 88+, Safari 14+)

Backend

• Python 3.10+
• Dependencies in backend/requirements.txt

🎨 How It Works

1. Upload - User uploads childhood and current photos
2. Face Detection - OpenCV detects faces in both images
3. Feature Extraction - Extract face embeddings
4. AI Merging - Generate merged image (AI model or fallback blend)
5. Post-Processing - Apply lighting match, effects, opacity
6. Download - User downloads or shares the result

🔧 Configuration

Frontend

Edit frontend-files/app.js:

const API_BASE_URL = 'YOUR_BACKEND_URL';

Backend

Edit backend/config.py:

TARGET_SIZE = (512, 512)  # Image processing size
MAX_FILE_SIZE = 10 * 1024 * 1024  # 10MB limit

🐛 Troubleshooting

Face Not Detected

• Use clear, front-facing photos
• Ensure good lighting
• Image should be at least 256x256 pixels

CORS Errors

• Backend allows all origins by default
• For production, update CORS settings in backend/app.py

Slow Processing

• Fallback blending: ~2-5 seconds (CPU)
• With AI model: ~5-15 seconds (GPU recommended)

📝 License

MIT License - Free for personal and commercial use

🙏 Acknowledgments

• Face detection: OpenCV Haar Cascades
• Fonts: Google Fonts (Inter, Poppins)
• Icons: Custom SVG icons

👨‍💻 Author

Ayax Khan - GitHub

🤝 Contributing

Contributions welcome! Please feel free to submit a Pull Request.

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

📧 Support

For issues or questions:

• Open an issue on GitHub
• Check existing issues for solutions

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Made with ❤️ for preserving memories