📅 2024-04-19 — Session: Debugged and Enhanced Plotting in Flask ML App

🕒 06:45–07:55
🏷️ Labels: Flask, Debugging, Plotting, Machine Learning, Javascript
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal:

The primary objective of this session was to debug and enhance the plotting functionality within a Flask-based machine learning application.

Key Activities:

  • Debugging Plot Display Issues: Addressed issues related to plot display by ensuring correct handling of run_id, saving prediction data, and updating plot data endpoints.
  • Integration of Plot Updates: Modified backend (Python Flask) and frontend (JavaScript) to ensure updatePlots function is triggered post-model retraining.
  • File Handling and Plotting: Improved file path consistency and utilized run_id in Flask to fetch prediction files and generate plots.
  • Recursive File Search: Implemented a Flask endpoint for recursive file search using Python’s os module to locate prediction CSV files.
  • Enhanced Logging: Updated JavaScript functions with detailed logging for better debugging of asynchronous operations.
  • Python Plotting Function: Developed a Python function to retrieve prediction data and generate plots, including debugging logs.
  • Debugging Flask Endpoints: Added print statements and enhanced logging to troubleshoot 404 errors and improve data flow understanding.

Achievements:

  • Successfully debugged plot display issues and integrated plot updates post-model retraining.
  • Enhanced logging in both JavaScript and Python functions to aid in debugging.
  • Implemented robust file handling and retrieval mechanisms using recursive search and the glob module.

Pending Tasks:

  • Further testing is required to ensure all edge cases are handled, particularly in file retrieval and plot updates.
  • Continuous monitoring and logging improvements to preemptively address future debugging needs.