📅 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.