import gradio as gr import pandas as pd try: clustered_data = pd.read_csv("clustered_data.csv") except FileNotFoundError: print("Error: 'clustered_data.csv' not found. Make sure it's in the same directory.") exit() players_2024 = sorted(clustered_data[clustered_data['year'] == 2024]['player_name'].unique()) def get_similar_players(selected_player): """Returns a table of similar players based on cluster, including selected player and year.""" if selected_player is None: return pd.DataFrame(columns=['player_name', 'year', 'targets', 'receptions', 'rec_yards', 'air_yards', 'routes_ran', 'yprr', 'tprr', 'adot', 'target_share', 'year_two_half_ppr']) try: selected_player_data = clustered_data[clustered_data['player_name'] == selected_player].iloc[0].to_dict() selected_player_cluster = selected_player_data['cluster'] except IndexError: return pd.DataFrame(columns=['player_name', 'year', 'targets', 'receptions', 'rec_yards', 'air_yards', 'routes_ran', 'yprr', 'tprr', 'adot', 'target_share', 'year_two_half_ppr']) similar_players = clustered_data[ (clustered_data['cluster'] == selected_player_cluster) & (clustered_data['player_name'] != selected_player) ].copy() # Select and return desired columns, including 'player_name' and 'year' similar_players = similar_players[['player_name', 'year', 'targets', 'receptions', 'rec_yards', 'air_yards', 'routes_ran', 'yprr', 'tprr', 'adot', 'target_share', 'year_two_half_ppr']] # Round specified columns to two decimal places for col in ['yprr', 'tprr', 'adot', 'target_share', 'year_two_half_ppr']: similar_players[col] = similar_players[col].round(2) # --- Corrected handling of selected player data --- selected_player_df = pd.DataFrame([selected_player_data]) # Create DataFrame from dictionary selected_player_df = selected_player_df[['player_name', 'year', 'targets', 'receptions', 'rec_yards', 'air_yards', 'routes_ran', 'yprr', 'tprr', 'adot', 'target_share', 'year_two_half_ppr']] # Order columns for col in ['yprr', 'tprr', 'adot', 'target_share', 'year_two_half_ppr']: selected_player_df[col] = selected_player_df[col].round(2) similar_players = pd.concat([selected_player_df, similar_players], ignore_index=True) return similar_players with gr.Blocks() as demo: player_dropdown = gr.Dropdown(choices=players_2024, label="Select a 2024 Player") output_table = gr.DataFrame(label="Similar Players") player_dropdown.change(get_similar_players, inputs=player_dropdown, outputs=output_table) demo.launch(share = True)