README.md

metadata

title: Tchoupinoux Inviders - Sticker Hunt
emoji: 🎯
colorFrom: gray
colorTo: green
sdk: gradio
sdk_version: 5.47.2
app_file: app.py
pinned: false

Tchoupinoux Inviders - Sticker Hunt

A gamified sticker collection application where users hunt for B15 Tchoupinoux stickers around office locations.

Features

• User Authentication: Register and login to track your personal collection
• Camera Integration: Capture stickers directly from your device
• Location-Based Tracking: Select from predefined locations
• Points System:
  • 50 points for stickers in Tour Franklin
  • 100 points for stickers elsewhere
• Collection Gallery: View all your found stickers
• Leaderboard: Compete with other collectors

How to Use

1. Register/Login: Create an account or login with existing credentials
2. Hunt: Use your camera to capture stickers you find
3. Select Location: Choose where you found the sticker
4. Submit: The app will verify the sticker and add it to your collection
5. Collect: Track your progress in the Collection tab

Tech Stack

• Framework: Gradio
• Storage: JSON-based file system
• Authentication: SHA-256 password hashing
• Image Processing: PIL + NumPy

Deployment

This app is designed to run on Hugging Face Spaces.

Setup Instructions

1. Create a new Space on Hugging Face
2. Choose "Gradio" as the SDK
3. Upload app.py and requirements.txt
4. The app will automatically start

File Structure

.
├── app.py                  # Main application
├── requirements.txt        # Python dependencies
└── data/                   # Auto-created data directory
    ├── users.json          # User accounts
    ├── collections.json    # User collections
    ├── stickers.json       # Sticker configurations
    └── images/             # Captured sticker images

Configuration

Edit the stickers configuration in the code to add new locations:

stickers = {
    "Location Name": {
        "count": 2,              # Number of stickers at this location
        "points": 50,            # Points per sticker
        "found_indices": []
    }
}

Note on Sticker Verification

The current implementation uses a basic image verification system. For production use, consider implementing:

• CLIP-based similarity matching
• Custom trained computer vision model
• Template matching algorithms
• OCR for text detection

License

MIT License