How rakesh-mora/classification-of-pulmonary-diseases-using-respiratory-sounds- works
This is a Flask web application for classifying respiratory disorders from uploaded lung sounds. A patient uploads a recording; the app passes it to the classifier, which extracts audio features and predicts the leading and runner-up conditions. The app also creates waveform and MFCC visualizations for the result page. Separate routes provide login, registration, and chart pages. The model and prediction path are supported by sampled source; chart content and other template details are inferred only from routes and the file tree.
Main entry points: app.py Flask routes · rdc_model.py Classifier · model.h5 Trained model · first.html Landing and auth views
Components 13
Web application
- Flask routes
app.py - Account state
app.py - Temporary audio
app.py - Audio visualizations
app.py
Audio classification
- Classifier
rdc_model.py - Audio features
rdc_model.py - Trained model
model/model.h5
User interface
- Landing and auth views
templates/first.html - Login and registration views
templates/login.html - Upload and result view
templates/index.html - Chart views
templates/chart.html - Pie chart view
templates/piechart.html
Other components
- Patient
Connections 14
- Patient → Flask routes: uploads audio
- Patient → Flask routes: submits credentials
- Flask routes → Temporary audio: saves audio
- Flask routes → Classifier: classifies recording
- Classifier → Audio features: extracts features
- Classifier → Trained model: predicts with
- Classifier → Flask routes: returns predictions
- Flask routes → Audio visualizations: generates plots
- Flask routes → Account state: reads and updates
- Flask routes → Landing and auth views: renders landing
- Flask routes → Login and registration views: renders auth
- Flask routes → Upload and result view: renders upload and results
- Flask routes → Chart views: renders chart
- Flask routes → Pie chart view: renders pie chart