How prabhavathi11-code/transfer-learning-based-classification-of-poultry-diseases-for-enhanced-health-management works
This is a Flask poultry-disease image classifier. The main workflow accepts an uploaded image, preprocesses it for a Keras model, maps the predicted class to a disease label, and displays the result. The sampled app.py is the only runtime implementation shown. The model file it loads is not in the tree, and the tree’s Templates/ capitalization differs from Flask’s conventional templates/ lookup; the available evidence does not establish how these are handled. Dataset directories and other templates are present, but no training or additional route wiring is shown.
Main entry points: app.py Upload handler · index.html Home page · predict.html Prediction page
Components 9
Web workflow
- Flask routes
app.py - Home page
Templates/index.html - Upload handler
app.py - Uploaded images
app.py - Prediction page
Templates/predict.html
Image classification
- Image preprocessing
app.py - Keras model artifact
- Class mapping
app.py
Other components
- User
Connections 9
- User → Flask routes: visits
- Flask routes → Home page: renders
- User → Upload handler: uploads image
- Flask routes → Upload handler: dispatches
- Upload handler → Uploaded images: saves image
- Upload handler → Image preprocessing: prepares image
- Upload handler → Keras model artifact: predicts with
- Upload handler → Class mapping: maps scores
- Upload handler → Prediction page: renders result