Add info_columns_dict initialization in app.py: implement logic to store key metrics from working_frame in session state for improved data tracking, and refactor filtering logic to enhance clarity and maintainability of lineup processing.

Enhance name matching logic in app.py: improve clarity of output messages by specifying the source of matches for both portfolio and projections, and update projections DataFrame in session state with matched names for better data consistency.

Refactor portfolio reset functionality in app.py: modify the reset button logic to delete the working_frame from session state, ensuring a clearer reset process, and update working_frame initialization to consistently reference the origin_portfolio.

Adjust score cutoff values in app.py: lower the score_cutoff for name matching from 90 to 85 to improve matching flexibility, and update working_frame assignment in session state for clarity in portfolio reset functionality.

Add export dictionary initialization in app.py: implement logic to create an export_dict from csv_file, enhancing data handling for export operations while ensuring fallback for missing keys.

Update projection names handling in app.py: replace direct reference to projections DataFrame with session state for improved data consistency and clarity in name matching logic.

Refactor name matching logic in app.py: streamline the process of matching portfolio names to projection names by utilizing session state for improved clarity and efficiency, while enhancing the output messages for better user feedback.

Refactor map_dict handling in app.py: replace local map_dict variable with session state reference for improved consistency and clarity in data processing across various operations.

Enhance working_frame processing in app.py: add prediction and trimming logic to session state, ensuring maximum values are tracked for key metrics, improving data handling and analysis capabilities.

Add working_frame initialization in app.py: create a copy of the portfolio in session state to ensure consistent data handling across operations, enhancing clarity and maintainability.

Refactor portfolio handling in app.py: replace direct references to the portfolio with a working_frame variable for improved clarity and maintainability, ensuring consistent data processing across various operations.

Update app.py: temporarily add an info message for ongoing work on the export feature, and simplify display logic by directly using the portfolio state while commenting out the selectbox for display options.

Refactor export handling in app.py: simplify the initialization of the export_merge session state variable by removing the conditional check, ensuring it always reflects the current portfolio state for accurate export operations.

Improve export handling in app.py: add a conditional check to initialize the export_merge session state variable only if it doesn't already exist, ensuring data integrity during portfolio export operations.

Update export logic in app.py: introduce a new session state variable to store a copy of the portfolio for export, ensuring accurate data handling when adding to the export base and improving display options for paginated data.

Fix export logic in app.py: update the export base to concatenate the current portfolio instead of the export file, ensuring accurate data representation when adding to the export.

Refactor portfolio calculation logic in app.py: streamline salary, median, and ownership computations for Classic and Showdown types, ensuring accurate data handling and improved clarity in the code structure.

Enhance portfolio calculations in app.py: implement conditional logic for salary, median, and ownership calculations based on portfolio type and sport, ensuring accurate data handling for both Classic and Showdown formats.

Refactor export button logic in app.py: streamline the addition of portfolios to the export base by replacing the button context with a conditional check, enhancing user interaction and code clarity.

Refactor export functionality in app.py: streamline portfolio download and merging processes, introduce export base for consolidated data management, and enhance display options for portfolio and export data.

ping

Enhance player summary generation in app.py: refactor logic to handle Captain and FLEX positions separately for improved accuracy in lineup statistics, and ensure consistent display of player performance metrics in the summary table.

Update app.py to utilize session state for trimming dictionary max values: enhance state management by ensuring consistent access to ownership type thresholds during portfolio updates.

Update trimming dictionary initialization in app.py: ensure consistent handling of maximum values for ownership types during portfolio updates, improving state management and preventing data conflicts.

Remove trimming_dict_maxes from session state before reinitializing in app.py: ensure clean state management during portfolio updates and prevent potential data conflicts.

Refactor trimming dictionary handling in app.py: initialize trimming dictionary max values in session state for improved state management and ensure accurate threshold inputs during portfolio updates.

Update trimming dictionary max values in app.py: adjust ownership type thresholds to improve filtering accuracy and maintain consistency in portfolio management.

Add trimming dictionary max values in app.py: define maximum thresholds for ownership types to enhance filtering options and maintain consistency in portfolio management.

Reintroduce trimming dictionary in app.py: move the calculation of maximum values for ownership types back into the submission logic, ensuring accurate threshold inputs during portfolio updates and maintaining consistency in the trimming process.

Enhance trimming functionality in app.py: introduce a trimming dictionary to streamline the retrieval of maximum values for performance and ownership types, improving the accuracy of threshold inputs in the portfolio management process.

Refactor filter options in app.py: streamline the organization of macro and micro filter inputs into expandable sections, enhance player locking and removal functionalities, and ensure consistent handling of trimming parameters for improved user experience during portfolio management.

Refactor filter options in app.py: reorganize macro and micro filter inputs into expandable sections for improved user experience, and ensure consistent handling of player locking and removal functionalities during portfolio management.

Update input fields in app.py: add unique keys to selectboxes and number inputs for sorting and trimming variables, enhancing state management and user experience in the portfolio trimming process.

Refactor trimming options in app.py: enhance user interface by organizing input fields into columns for better clarity and usability, and update threshold inputs to allow for minimum and maximum values, improving the portfolio trimming process.

Add trim slack parameter to portfolio trimming functionality in app.py and trim_portfolio.py, allowing for more flexible threshold adjustments during portfolio management.

Update app.py and trim_portfolio.py to change threshold parameters from integers to floats, enhancing precision in portfolio trimming functionality and ensuring consistent data handling.

Enhance portfolio trimming functionality in app.py and trim_portfolio.py: add performance and own thresholds for trimming criteria, improving data filtering capabilities and allowing for more precise portfolio management.

Refactor salary processing in app.py: update salary conversion to drop NaN values before converting to integers, ensuring cleaner data handling and enhancing the robustness of projections.

Refactor salary processing in app.py: streamline salary conversion by removing unwanted characters before converting to integers, enhancing clarity and ensuring consistent data handling during projections file loading.

Enhance salary processing in app.py: add error handling during salary conversion to integers, ensuring robustness in data processing while maintaining clarity in projections file loading.

Refactor salary processing in app.py: improve clarity by separating character replacements for salary values into distinct operations, ensuring consistent formatting during projections file loading.

Refactor salary processing in app.py: move salary character replacement to app.py for improved clarity and maintainability, ensuring consistent handling of salary values during projections file loading.

Update app.py and load_file.py: add regex import in app.py for future enhancements and replace print with write in load_file.py for improved output handling.

Refactor file type checks in load_file.py and load_ss_file.py: streamline file extension validation using endswith method for improved readability. Update app.py to handle salary conversion more robustly by removing unnecessary exception handling.

Refactor salary data processing in app.py: streamline salary conversion by consolidating character removal into a single regex operation, enhancing data integrity and consistency during projections file handling.

Enhance salary data processing in app.py: add a try-except block to convert salary values to integers after removing unwanted characters, improving data integrity during projections file handling.

Refactor file type checks in loading functions: update conditions in load_dk_fd_file.py, load_file.py, and load_ss_file.py to use substring checks for file extensions, improving readability and maintainability. Remove debug print statements from load_dk_fd_file.py and app.py to clean up the code.

Add debug print statement for export portfolio in app.py: include a print statement to output the export portfolio for debugging purposes during file uploads, aiding in troubleshooting and data verification.

Update score cutoff in portfolio matching logic in app.py: increase the score cutoff from 85 to 90 to enhance the accuracy of portfolio name matches.

Update salary input limits in app.py: increase the maximum acceptable salary from 60,000 to 100,000 to accommodate a wider range of salary options for users. Adjust conditional checks in predict_dupes.py to use 'elif' for improved clarity in type_var evaluations.