Refactor sport selection in app.py to change the order of options in radio buttons for loading and optimizing data from 'NBA, NFL' to 'NFL, NBA'. This adjustment improves user experience by prioritizing NFL in the selection process.

Enhance app.py to improve timestamp retrieval for NBA and NFL datasets. Added error handling to fallback on alternative data sources if primary timestamps are unavailable, ensuring robustness in data processing for player projections.

Refactor app.py to conditionally calculate 'CPT_Salary' and 'flex_proj' salaries based on sport selection (NBA or NFL). This update enhances the accuracy of player projections by applying different salary multipliers for each sport, improving the overall functionality of the application.

Refactor app.py to include 'player_id' in the raw_display DataFrame. This change enhances data structure for player projections, ensuring better integration with subsequent data processing and analysis.

Enhance app.py to support NBA and NFL player projections with distinct formatting. Introduced nba_player_roo_format and nfl_player_roo_format for improved data display based on sport selection. Updated dataframe rendering logic to conditionally format projections based on the selected sport, ensuring a more tailored user experience.

Refactor app.py to replace Google Sheets integration with MongoDB for data retrieval. Updated data loading functions to support NBA and NFL datasets, adjusted caching mechanisms, and streamlined user interface for sport selection. Removed deprecated code related to Google Sheets and improved data handling for player projections.

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Rename streamlit_app.py to app.py