Enhanced Flask API for Dynamic Model Management

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📅 2024-04-19 — Session: Enhanced Flask API for Dynamic Model Management

🕒 04:40–06:24
🏷️ Labels: Flask, API, Machine Learning, Debugging, UI
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary objective of this session was to enhance a Flask API to support dynamic model management, including preprocessing, error handling, and user interface updates.

Key Activities

  • Modified Preprocessing Function: Adapted the preprocessing function to handle both dictionary and DataFrame inputs, ensuring consistent feature processing for predictions.
  • Revised /predict Endpoint: Enhanced the /predict endpoint to dynamically load the latest model and preprocessor files, improving error handling and data processing.
  • Resolved Import Errors: Addressed various import errors in the Flask application by adjusting the Python path, using __init__.py, and refining import statements.
  • Filtered Model Files: Implemented a filter to exclude preprocessor files from the model list in the API response.
  • UI Enhancements: Updated the user interface to display model version and metrics, and implemented an auto-refresh feature for the model information table.
  • Debugging: Added debug statements to the /predict endpoint to trace data flow and identify issues.

Achievements

  • Successfully implemented dynamic model and preprocessor loading in the Flask API.
  • Improved error handling and data processing in the /predict endpoint.
  • Enhanced user interface with auto-refresh and simplified model information display.
  • Resolved import errors, ensuring smooth module recognition and application functionality.

Pending Tasks

  • Further testing of the auto-refresh feature in various browsers and environments to ensure consistency.
  • Continued monitoring of the /predict endpoint for any additional debugging needs.

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