📅 2024-04-06 — Session: Developed and Tested Flask API for ML Predictions
🕒 18:25–19:05
🏷️ Labels: Flask, API, Machine Learning, Python, Error Handling
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The objective of this session was to set up a Flask API to serve predictions from a machine learning model, ensuring the application is well-structured and tested.
Key Activities
- Setting Up Flask API: Implemented a Flask API to handle model predictions, including setup and code implementation.
- Organizing Application Structure: Defined a structured approach for the Flask application with separate files for API routes and the main entry point.
- Centralizing Prediction Logic: Centralized prediction logic to maintain consistency and adhere to DRY principles.
- Testing the API: Created a Python script to test the Flask API using POST requests with JSON data.
- Troubleshooting Errors: Addressed ‘[Errno 111] Connection refused’ and
ModuleNotFoundErrorissues, ensuring the Flask server runs correctly and import paths are properly set. - Fixing Import Errors: Resolved
ImportErrorrelated to Flask Blueprints by adjusting the application structure.
Achievements
- Successfully set up and tested a Flask API for machine learning predictions.
- Resolved critical errors related to server connectivity and module imports.
Pending Tasks
- Further optimization of the Flask application for production deployment.
- Comprehensive testing with additional datasets to ensure robustness.