How nayan-satapathy/algerian_forest_fire_predictor works
This is a small Flask forest-fire risk prediction app. A user opens the home page, submits environmental values through the predictor form, and receives an FWI prediction: the application scales the submitted features, runs the regression model, and renders the result. The sampled application code confirms these runtime steps; template contents were not sampled, so the graph does not infer their internal behavior.
Main entry points: application.py Flask application · form.html Prediction form · home.html Home page · Scaler.pkl Feature scaler
Components 6
Web interface
- Flask application
application.py - Home page
templates/home.html - Prediction form
templates/form.html
Prediction runtime
- Feature scaler
models/Scaler.pkl - Regression model
models/LinearReg.pkl
Other components
- User
Connections 7
- User → Flask application: requests pages
- Flask application → Home page: renders
- Flask application → Prediction form: renders
- User → Flask application: submits features
- Flask application → Feature scaler: transforms features
- Flask application → Regression model: predicts FWI
- Flask application → Prediction form: renders prediction