Flask

Load the pre-trained model into global memory upon app initialization.

Create a POST endpoint that accepts JSON input data.

Pre-process the input using NumPy or Pandas.

Perform model inference and capture the prediction.

Return the result as a JSON response to the client.

Define a Flask service specifically for inventory management.

Connect the service to a dedicated PostgreSQL database using Flask-SQLAlchemy.

Expose internal endpoints for order reconciliation.

Deploy the service as a Docker container.

Use an API Gateway to route traffic to the Flask microservice.

Use Jinja2 templates for the UI.

Integrate Flask-FlatPages to handle Markdown-based content files.

Implement a custom admin route for content uploads.

Deploy to a serverless platform like AWS Lambda using Zappa.

Use Flask-SocketIO to establish a bi-directional WebSocket connection.

Receive sensor data via a specialized Flask-RESTful endpoint.

Emit the data to connected browser clients in real-time.

Visualize data on the frontend using D3.js or Chart.js.

Create a dedicated route for the vendor's webhook URL.

Validate the signature provided in the headers to ensure security.

Parse the payload and trigger an asynchronous background task using Celery.

Respond with a 200 OK immediately to acknowledge receipt.

Integrate the Elasticsearch-py client into the Flask app.

Build a search route that handles parameters for filtering and pagination.

Map the Flask request objects to Elasticsearch queries.

Format the search hits into a clean JSON output for the frontend.

Utilize Flask-Login for session-based user authentication.

Store course content in an SQLite database for portability.

Use Flask-Uploads to manage student assignment submissions.

Serve dynamic PDFs for certificates using the WeasyPrint library.