Experience the future of social networking with Django backend, Python ML algorithms, and real-time matching. Connect with compatible people using AI matchmaking, location discovery, and social graph analysis.
Advanced Python ML algorithms using Scikit-learn and TensorFlow for intelligent compatibility analysis.
GPS-based matching with PostgreSQL geospatial queries for finding nearby compatible users.
Network analysis algorithms to understand social connections and recommend compatible matches.
Django REST Framework API with Redis for instant messaging and match notifications.
Multi-factor authentication and profile verification system for authentic user connections.
Celery task queue for ML model inference and asynchronous matching algorithm processing.
Experience our AI matchmaking algorithm with real-time compatibility scoring and personalized recommendations.
Find compatible matches in your area using our geospatial search and proximity-based filtering.
Test our instant messaging system with WebSocket support and real-time message delivery.
Experience our multi-layer verification system for authentic and trustworthy user profiles.
# Get User Profile
GET /api/discovery/user-profiles/{id}/
# Create User Profile
POST /api/discovery/user-profiles/
{
"bio": "Passionate developer looking for connections",
"location_lat": 40.7128,
"location_lng": -74.0060,
"interests": ["technology", "music", "travel"]
}
# Generate Matches
POST /api/discovery/user-profiles/{id}/generate_matches/
# Get User Matches
GET /api/discovery/matches/?user={user_id}
# Discovery Feed
GET /api/discovery/feed/
# ML Matching Algorithm
from discovery.ml_models.matcher import update_user_matches
from discovery.tasks import run_matching_algorithm
# Trigger background matching
run_matching_algorithm.delay(user_id=123)
# Core ML processing
def update_user_matches(user_id):
user_vec = get_user_vector(user_id)
candidates_vec = np.random.rand(100, 128)
similarities = cosine_similarity(user_vec, candidates_vec)
# Store top matches in database
return similarities
Powerful backend framework with Django REST API for scalable social networking platform.
TensorFlow, Scikit-learn, and Pandas for intelligent matchmaking and behavioral analysis.
Robust relational database with geospatial extensions for location-based queries.
High-performance caching and task queue for real-time messaging and background processing.
Containerized deployment with multi-service architecture for easy scaling and management.
Social graph algorithms for relationship mapping and influence scoring.
JWT authentication, encryption, and privacy-first design for user protection.
Real-time metrics and insights for matching optimization and user engagement.
# Clone the repository git clone https://github.com/rskworld/social-discovery.git cd social-discovery # Build and start services docker-compose build docker-compose up # Access the application # Web: http://localhost:8000 # API: http://localhost:8000/api/ # Admin: http://localhost:8000/admin/
# Create virtual environment python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate # Install dependencies pip install -r requirements.txt # Setup PostgreSQL database createdb social_discovery # Run migrations python manage.py migrate # Create superuser python manage.py createsuperuser # Start development server python manage.py runserver
# Environment variables (.env) DEBUG=True SECRET_KEY=your-secret-key-here DATABASE_URL=postgresql://postgres:postgres@localhost:5432/social_discovery CELERY_BROKER_URL=redis://localhost:6379/0 REDIS_URL=redis://localhost:6379/0 # Email settings (optional) EMAIL_HOST=smtp.gmail.com EMAIL_PORT=587 EMAIL_USE_TLS=True EMAIL_HOST_USER=your-email@gmail.com EMAIL_HOST_PASSWORD=your-app-password
Perfect for individuals and small communities
Best for growing businesses and platforms
For large organizations and custom needs