Update threshold value in GBDT forecast plot in app.py from 100 to 200 to reflect new compliance standards, enhancing the interpretability of forecast results.

Add horizontal threshold line to GBDT forecast plot in app.py for enhanced visualization. This line indicates a compliance threshold, improving interpretability of forecast results.

Remove Japanese font configuration from matplotlib in app.py to streamline plotting settings and improve compatibility.

Add coordinate data JSON output to GBDT forecasting functions in app.py. Update return types and error handling to include new data structure. Enhance Gradio UI to display graph coordinates for improved user experience.

Enhance app.py with comprehensive documentation and Japanese translations for GBDT water quality forecasting application. Add detailed docstrings for key functions, including data retrieval, feature extrapolation, and prediction processes. Improve clarity and accessibility for users.

Update plot labels and title in app.py for improved clarity in GBDT forecasting visualization, translating Japanese terms to English for better accessibility.

Refactor app.py to enhance documentation and improve clarity in GBDT forecasting. Update comments for better understanding, streamline data retrieval from Supabase, and refine feature extrapolation logic. Adjust Gradio UI elements for a more intuitive user experience.

Refactor app.py to improve documentation and enhance clarity in GBDT forecasting process. Update comments, streamline hyperparameter grid configuration, and refine plot labels for better visualization. Adjust fallback parameters for improved model performance on small datasets.

Refactor GBDT cross-validation function in app.py to improve hyperparameter grid search and enhance model selection logic. Update fallback parameters and streamline grid configuration for better performance on small datasets.

Update plot labels and title in app.py to enhance clarity in GBDT forecasting visualization.

Refactor app to utilize GBDT for forecasting from Supabase data. Update documentation, enhance error handling, and improve Gradio UI for better user experience. Streamline data retrieval and feature extrapolation processes.

Update plot labels and title in app.py for improved clarity in PLS forecasting visualization.

Refactor app to utilize PLS regression for forecasting from Supabase data. Update documentation, enhance error handling, and improve Gradio UI for better user experience. Add support for future feature extrapolation and streamline data retrieval process.

Update type hint in predict_from_supabase function to improve type accuracy in app.py

Enhance documentation and type hints in app.py for better clarity and maintainability. Update function signatures and add docstrings for key functions to improve code readability.

Enable MCP server in demo launch for enhanced functionality

Refactor app to integrate Supabase for data retrieval and update forecasting logic. Rename file to app_supabase.py and enhance error handling. Update Gradio UI for improved user experience.

Implement initial project structure and setup

initial commit