How kumarangithub6485/airfoil-regression-deployment- works
Purpose and entry point
This is a minimal deployed machine-learning inference service for airfoil regression. The primary runtime entry point is app.py; deployment process configuration is in Procfile. The trained regression artifact is bundled as model.pkl.
Web application boundary
app.py likely owns the HTTP application lifecycle: receives user inputs, invokes the regression model, and renders responses. Its UI/server-rendered presentation boundary is templates/home.html, rather than a separate frontend application or API service.
Prediction pipeline
The central flow is:
1. A browser requests the application route handled by app.py.
2. The server renders templates/home.html, which provides the airfoil feature-input surface.
3. Submitted values are parsed and arranged into the feature shape expected by model.pkl.
4. app.py loads or uses the serialized model and produces a regression prediction.
5. The result is returned through the rendered HTML response.
The key compatibility boundary is between form field names/types in templates/home.html and the model feature order and preprocessing assumptions encoded in model.pkl.
Model artifact
model.pkl is the repository’s learned-model boundary. It contains the fitted regression estimator (and possibly preprocessing state) used at runtime. Its serialization format couples deployment to the Python libraries and compatible versions declared in requirements.txt.
Deployment/runtime
Procfile defines the process command expected by a platform-as-a-service deployment environment, likely launching the Python web server around app.py. requirements.txt supplies the runtime dependencies, including the web framework and ML serialization/model libraries needed to load model.pkl.
Architectural constraints
There is no visible training pipeline, dataset, database, queue, external API, or separate model-serving layer. Training is assumed to occur outside this repository; deployment is inference-only. Model replacement requires updating model.pkl while preserving the input schema expected by app.py and templates/home.html.
Components 7
Web inference app
- Browser · Requests the form and submits airfoil inputs.
- Inference application · Handles routes, shapes features, and renders predictions.
app.py - Airfoil input form · Defines the feature-input and result presentation boundary.
templates/home.html - Regression model · Fitted estimator and possible preprocessing state.
model.pkl
Deployment runtime
- Process command · Launches the web application in deployment.
Procfile - Runtime dependencies · Provides web and ML libraries compatible with the model artifact.
requirements.txt - PaaS runtime
Connections 10
- Browser → Inference application: HTTP request / form submission
- Inference application → Airfoil input form: renders HTML
- Airfoil input form → Browser: form and prediction response
- Browser → Airfoil input form: supplies feature values
- Inference application → Regression model: loads and predicts
- Airfoil input form → Inference application: field schema and submitted values
- Process command → PaaS runtime: defines process command
- PaaS runtime → Inference application: runs
- Runtime dependencies → Inference application: web runtime libraries
- Runtime dependencies → Regression model: serialization compatibility