lschlessinger/usatt-rating-analyzer

1from pathlib import Path2 3import gradio as gr4 5import match_parser as mp6 7 8def usatt_rating_analyzer(file_obj):9    # Load data.10    df, is_tournament = mp.load_match_df(Path(file_obj.name))11 12    # Create outputs.13    current_rating = mp.get_current_rating(df)14    peak_rating = mp.get_max_rating(df)15    n_competitions_played = mp.get_num_competitions_played(df, is_tournament)16    n_matches_played = len(df)17    first_comp_year = mp.get_first_competition_year(df, is_tournament)18    n_active_years = mp.get_num_active_years(df, is_tournament)19    matches_per_competition_fig = mp.get_matches_per_competition_fig(df, is_tournament)20    opponent_name_word_cloud_fig = mp.get_opponent_name_word_cloud_fig(df)21    competition_name_word_cloud_fig = mp.get_competition_name_word_cloud_fig(df, is_tournament)22    best_competitions = mp.make_df_columns_readable(mp.get_best_competitions(df, is_tournament), is_tournament)23    most_frequent_opponents = mp.make_df_columns_readable(mp.get_most_frequent_opponents(df), is_tournament)24    best_wins = mp.make_df_columns_readable(mp.get_best_wins(df), is_tournament)25    biggest_upsets = mp.make_df_columns_readable(mp.get_biggest_upsets(df), is_tournament)26    worst_recent_losses = mp.make_df_columns_readable(mp.get_worst_recent_losses(df, is_tournament), is_tournament)27    highest_rated_opponent = mp.make_df_columns_readable(mp.get_highest_rated_opponent(df), is_tournament)28    rating_over_time_fig = mp.get_rating_over_time_fig(df, is_tournament)29    match_with_longest_game = mp.make_df_columns_readable(mp.get_match_with_longest_game(df, is_tournament),30                                                          is_tournament)31    longest_match = mp.make_df_columns_readable(mp.get_longest_match(df, is_tournament), is_tournament)32    opponent_rating_distr_fig = mp.get_opponent_rating_distr_fig(df)33    opponent_rating_dist_over_time_fig = mp.get_opponent_rating_dist_over_time_fig(df, is_tournament)34 35    return (  # player_name,36        current_rating,37        peak_rating,38        n_competitions_played,39        n_matches_played,40        first_comp_year,41        n_active_years,42        rating_over_time_fig,43        opponent_rating_distr_fig,44        opponent_rating_dist_over_time_fig,45        best_wins,46        biggest_upsets,47        worst_recent_losses,48        best_competitions,49        most_frequent_opponents,50        highest_rated_opponent,51        match_with_longest_game,52        longest_match,53        opponent_name_word_cloud_fig,54        competition_name_word_cloud_fig,55        matches_per_competition_fig,56    )57 58 59with gr.Blocks() as demo:60    analyze_btn_title = "Analyze"61    gr.Markdown(f"""# USATT rating analyzer62    Analyze [USA table tennis](https://www.teamusa.org/usa-table-tennis) tournament and league results. The more matches63     and competitions you have played, the better the tool works. Additionally, due to limitations on the available 64    data, ratings are always displayed as the rating received *after* the competition has been played. Here, 65    "competition" is defined as a tournament or a league.66    ## Downloading match results67    1. Make sure you are [logged in](https://usatt.simplycompete.com/login/auth) to your USATT account.68    2. Find the *active* player you wish to analyze (e.g.,  [Kanak Jha](https://usatt.simplycompete.com/userAccount/up/3431)).69    3. Under 'Tournaments' or 'Leagues', click *Download Tournament/League Match History*.70    ## Usage71    1. Simply add your tournament/league match history CSV file and click the "{analyze_btn_title}" button.72    73    ---74    75    """)76    with gr.Row():77        with gr.Column():78            input_file = gr.File(label='USATT Results File', file_types=['file'])79            btn = gr.Button(analyze_btn_title)80 81    gr.Markdown("""<br />82    83    ## Overview84    85    <br />86    """)87 88    with gr.Group():89        with gr.Row():90            with gr.Column():91                current_rating_box = gr.Textbox(lines=1, label="Current rating")92            with gr.Column():93                peak_rating_box = gr.Textbox(lines=1, label="Highest rating")94            with gr.Column():95                num_comps_box = gr.Textbox(lines=1, label="Number of competitions played")96            with gr.Column():97                num_matches_box = gr.Textbox(lines=1, label="Number of matches played")98            with gr.Column():99                first_competition_box = gr.Textbox(lines=1, label="First competition played")100            with gr.Column():101                num_active_years_box = gr.Textbox(lines=1,102                                                  label="Number of active years (participated in at least 1 competition)")103 104        with gr.Row():105            with gr.Column():106                rating_over_time_plot = gr.Plot(show_label=False)107 108        with gr.Row():109            with gr.Column():110                opponent_rating_dist_plot = gr.Plot(show_label=False)111            with gr.Column():112                opponent_rating_dist_over_time_plot = gr.Plot(show_label=False)113 114        gr.Markdown("""<br />115 116        ## Best and Worst Matches117 118        <br />119        """)120 121        with gr.Row():122            with gr.Column():123                best_wins_gdf = gr.Dataframe(label="Best wins (matches won sorted by opponent post-competition rating)",124                                             height=500)125                biggest_upsets_gdf = gr.Dataframe(126                    label="Biggest upsets (matches won sorted by rating - opponent post-competition rating)",127                    height=500)128                worst_recent_losses_gdf = gr.Dataframe(label="Worst recent losses (matches lost sorted by opponent "129                                                             "post-competition rating from the 5 most recent "130                                                             "competitions)", height=500)131 132        gr.Markdown("""<br />133        134        ## Fun Facts135        136        <br />137        """)138 139        with gr.Row():140            with gr.Column():141                best_competitions_gdf = gr.Dataframe(142                    label="Best competitions (those having the largest increase in rating)",143                    height=500)144                most_frequent_opponents_gdf = gr.Dataframe(label="Most frequent opponents", height=500)145                highest_rated_opponent_gdf = gr.Dataframe(label="Highest rated opponent", height=100)146                match_longest_game_gdf = gr.Dataframe(label="Match with longest game", height=100)147                longest_match_gdf = gr.Dataframe(label="Longest match (highest number of points played)", height=100)148 149        with gr.Row():150            with gr.Column():151                opponent_names_plot = gr.Plot(label="Opponent names")152            with gr.Column():153                comp_names_plot = gr.Plot(label="Competition names")154            with gr.Column():155                matches_per_comp_plot = gr.Plot(show_label=False)156 157    inputs = [input_file]158    outputs = [159        current_rating_box,160        peak_rating_box,161        num_comps_box,162        num_matches_box,163        first_competition_box,164        num_active_years_box,165        rating_over_time_plot,166        opponent_rating_dist_plot,167        opponent_rating_dist_over_time_plot,168        best_wins_gdf,169        biggest_upsets_gdf,170        worst_recent_losses_gdf,171        best_competitions_gdf,172        most_frequent_opponents_gdf,173        highest_rated_opponent_gdf,174        match_longest_game_gdf,175        longest_match_gdf,176        opponent_names_plot,177        comp_names_plot,178        matches_per_comp_plot,179    ]180 181    btn.click(usatt_rating_analyzer, inputs=inputs, outputs=outputs)182 183if __name__ == "__main__":184    demo.launch()185