How nikhildeshmuk98/deepflix works
This is primarily a Flask OTT/content recommendation application: users interact with content and recommendation routes, while administrators manage the service. The sampled application code also shows search, chat, profile-image handling, and administrative content/user queries. A separate training script reads content stories and genres from MySQL to train a classifier; the recommendation engine itself is explicitly a placeholder. The excerpts do not establish how most routes invoke the engine or whether the standalone database and graph modules are used, so those links are omitted.
Main entry points: app.py Flask routes · train_model.py Model training · setup_db.py Schema setup · hybrid_engine.py Hybrid recommender
Components 11
Web application
- Flask routes
app.py - Content search
app.py - Admin management
app.py - User messaging
app.py - Profile uploads
app.py
Recommendations
- Hybrid recommender
hybrid_engine.py - Model training
train_model.py
Data and training
- MySQL database
- Schema setup
setup_db.py
Other components
- Users
- Administrators
Connections 12
- Users → Flask routes: use routes
- Administrators → Admin management: manage service
- Flask routes → Content search: handles search
app.py - Flask routes → Admin management: serves dashboard
app.py - Flask routes → User messaging: stores messages
app.py - Flask routes → Profile uploads: handles uploads
app.py - Flask routes → Hybrid recommender: imports
app.py - Content search → MySQL database: queries content
app.py - Admin management → MySQL database: queries records
app.py - User messaging → MySQL database: writes chats
app.py - Model training → MySQL database: reads stories
train_model.py - Schema setup → MySQL database: creates tables
setup_db.py