How anubhabmondal496-coder/aiims-dholakpur works
This Django app serves a form and two linear-regression predictions: calorie burn and medical cost. Requests reach the corresponding view, which converts submitted fields into model features, runs a saved model, and returns an HTML result; medical cost additionally exponentiates the prediction. The README confirms the ML focus, while the sampled views establish these specific flows. The saved model files are present, but their training and feature provenance are not shown.
Main entry points: medical/urls.py Medical routes · views.py HTTP result · Hospital/urls.py Project routes · model1.pkl Calorie model
Components 13
Web entry
- Project routes
Hospital/Hospital/urls.py - Medical routes
Hospital/medical/urls.py - Home view
Hospital/medical/views.py - Input page
Hospital/templates/index.html - HTTP result
Hospital/medical/views.py
Prediction flows
- Calorie features
Hospital/medical/views.py - Calorie prediction
Hospital/medical/views.py - Cost features
Hospital/medical/views.py - Cost prediction
Hospital/medical/views.py - Cost transform
Hospital/medical/views.py
Model assets
- Calorie model
Hospital/savedmodels/model1.pkl - Cost model
Hospital/savedmodels/model2.pkl
Other components
- User
Connections 16
- User → Project routes: sends request
- Project routes → Medical routes: includes routes
- Medical routes → Home view: dispatches home
- Medical routes → Calorie features: dispatches POST
- Medical routes → Cost features: dispatches POST
- Home view → Input page: renders
- User → Calorie features: submits fields
- User → Cost features: submits fields
- Calorie model → Calorie prediction: loaded at startup
- Cost model → Cost prediction: loaded at startup
- Calorie features → Calorie prediction: predicts
- Cost features → Cost prediction: predicts
- Cost prediction → Cost transform: exponentiates
- Calorie prediction → HTTP result: formats result
- Cost transform → HTTP result: formats result
- HTTP result → User: returns response