AIBlockerServerPy

Python/Django implementation of the AIBlocker API server for crowdsourcing AI content classifications on YouTube videos.

Tech Stack

• Framework: Django 5.x + Django REST Framework
• Database: PostgreSQL
• Async Tasks: Celery + Redis
• Python: 3.12+

Prerequisites

• Python 3.12+
• PostgreSQL
• Redis

Setup

1. Clone and create virtual environment

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

2. Install dependencies

pip install -r requirements.txt

3. Configure environment

cp .env.example .env
# Edit .env with your configuration

Required environment variables:

• SECRET_KEY - Django secret key
• DATABASE_URL - PostgreSQL connection string
• REDIS_URL - Redis connection string
• SALT - Salt for hashing user IDs (IMPORTANT: change in production!)

4. Create PostgreSQL database

createdb aiblocker

5. Run migrations

python manage.py migrate

6. Start the server

Development:

python manage.py runserver 8080

Production:

gunicorn aiblocker.wsgi:application --bind 0.0.0.0:8080

7. Start Celery worker (separate terminal)

celery -A aiblocker worker --loglevel=info

API Endpoints

Method	Endpoint	Description
GET	/api/health	Health check
POST	/api/reports	Submit AI content report
POST	/api/reports/{id}/vote	Vote on a report
GET	/api/videos/{hash}/status	Get video AI status
GET	/api/videos/{hash}/reports	Get detailed reports
GET	/api/videos/{hash}/reports/summary	Get aggregated summary
POST	/api/videos/batch	Batch query videos

API Compatibility

This server maintains full API compatibility with the original TypeScript/Node.js implementation, allowing the AIBlocker Chrome extension to work without modifications.

Development

Run tests:

python manage.py test

Check for issues:

python manage.py check