QUICKSTART.md

Quick Reference Guide

🚀 Starting the Backend

First Time Setup (Windows)

cd backend
python setup.py
venv\Scripts\activate
python main.py

First Time Setup (macOS/Linux)

cd backend
python setup.py
source venv/bin/activate
python main.py

Subsequent Times

cd backend
run.bat      # Windows
# OR
./run.sh     # macOS/Linux

📡 API Quick Test

Health Check

curl http://localhost:8000/

Analyze a File

curl -X POST http://localhost:8000/analyze \
  -H "Content-Type: application/json" \
  -d '{"file_url": "https://example.com/video.mp4"}'

Documentation

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

🔧 Configuration

Edit backend/.env:

HOST=127.0.0.1
PORT=8000
DEFAULT_DETECTOR_MODEL=mock
LOG_LEVEL=INFO
DOWNLOAD_TIMEOUT=30
MAX_FILE_SIZE=104857600

📦 Project Structure

backend/
├── app/
│   ├── api/         # Routes
│   ├── services/    # Business logic (download, ML models, queuing)
│   ├── models/      # Data schemas
│   ├── core/        # Configuration
│   └── utils/       # Exceptions
├── main.py          # Entry point
├── README.md        # Full API docs
└── DEVELOPMENT.md   # Adding models, Redis, etc.

➕ Adding a New ML Model

1. Copy DETECTOR_TEMPLATE.py
2. Implement the detect() method
3. Register in app/services/detector/__init__.py
4. Update .env if setting as default

See DEVELOPMENT.md for detailed steps.

🔗 Integrating with Discord Bot

Use DISCORD_BOT_EXAMPLE.py as a template:

from discord_bot_example import setup

# In your bot startup:
await setup(bot)

# Then use in your bot:
# !deepfake_check https://example.com/video.mp4
# !backend_status

🐛 Common Issues

Problem	Solution
ModuleNotFoundError	Activate venv first
Port 8000 in use	Change port: PORT=8001 python main.py
Import errors	pip install -r requirements.txt
Download timeout	Increase: DOWNLOAD_TIMEOUT=60 python main.py

📊 Supported File Types

Any file type via URL:

• Videos: .mp4, .webm, .avi, etc.
• Images: .jpg, .png, .gif, etc.
• Any file up to 100 MB (configurable)

🔄 Async Support

Backend is fully async:

• Non-blocking file downloads
• Concurrent requests supported
• Scalable to Redis task queuing

📝 Logging Levels

# Normal operation
LOG_LEVEL=INFO python main.py

# Verbose debugging
LOG_LEVEL=DEBUG python main.py

# Warnings and errors only
LOG_LEVEL=WARNING python main.py

🚀 Production Deployment

For production, use Gunicorn with Uvicorn:

pip install gunicorn
gunicorn main:app -w 4 -k uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000

📞 Response Format

Success:

{
  "is_deepfake": true,
  "confidence": 0.95,
  "analysis_time": 1.5,
  "model_used": "mock"
}

Error:

{
  "error": "Invalid URL format",
  "status_code": 400,
  "details": null
}

🔐 Default Security Settings

• Max file size: 100 MB
• Download timeout: 30 seconds
• URL validation: Enabled
• Error details: Minimal (no leakage)

Increase security for production:

• Add API keys/authentication
• Implement rate limiting
• Use HTTPS
• Add CORS restrictions

🎯 Next Steps

1. ✅ Backend running?
2. ⏳ Test with sample URLs
3. ⏳ Create Discord bot using example
4. ⏳ Add your ML models
5. ⏳ Deploy to production

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For complete documentation, see README.md and DEVELOPMENT.md in the backend folder.