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| """A simple gradio app to predict NBA player performance this season""" | |
| import gradio as gr | |
| import pandas as pd | |
| from prophet import Prophet | |
| from datasets import load_dataset | |
| pd.options.plotting.backend = "plotly" | |
| # initialize empty players | |
| players = [""] | |
| # initialize empty seasons | |
| seasons = [1977, 2021] | |
| # load data | |
| nba_dataset = load_dataset("andrewkroening/538-NBA-Historical-Raptor") | |
| # initialize a dataframe from the nba dataset | |
| nba_df = pd.DataFrame(nba_dataset["train"]) | |
| # make a player_df with seasons and every player for that season | |
| player_df = nba_df[["season", "player_name"]].copy() | |
| def get_players(this_season, df=player_df): | |
| """Get the players for a given season""" | |
| # get the players for the given season | |
| season_players = df[df["season"] == this_season]["player_name"].unique().tolist() | |
| return gr.Dropdown.update(choices=season_players), gr.update(visible=False) | |
| def get_forecast(this_year, this_player): | |
| """Get the forecast for a given player and year and the performance for entire career""" | |
| # load data | |
| nba_data_fore = load_dataset("andrewkroening/538-NBA-Historical-Raptor") | |
| # initialize a dataframe from the nba dataset | |
| df = pd.DataFrame(nba_data_fore["train"]) | |
| # truncate to the player selected | |
| dataset = df[df["player_name"] == this_player] | |
| player_data = dataset[["season", "war_total"]].copy() | |
| # player_perform = player_df.copy() | |
| # make a list of the seasons the player played in | |
| player_seasons = player_data["season"].unique().tolist() | |
| # make two dfs, one for actual performance and one for the model | |
| # player_perform = player_perform[player_perform["season"] <= year + 5] | |
| player_data = player_data[player_data["season"] <= this_year] | |
| # convert the season column to a datetime object | |
| player_data["season"] = pd.to_datetime(player_data["season"], format="%Y") | |
| # set the df for prophet | |
| player_data.columns = ["ds", "y"] | |
| player_data = player_data.sort_values("ds") | |
| # build the prophet model | |
| m = Prophet(seasonality_mode="multiplicative").fit(player_data) | |
| future = m.make_future_dataframe(periods=5, freq="Y") | |
| forecast = m.predict(future) | |
| # plot the forecast | |
| fig1 = m.plot(forecast, xlabel="Year", ylabel="Wins Above Replacement") | |
| # plot the actual performance | |
| # fig2 = player_perform.plot( | |
| # x="season", y="war_total", title="Actual Performance", template="plotly_white") | |
| # return the figure | |
| return fig1, gr.Dropdown.update(choices=player_seasons), gr.update(visible=True) | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """ | |
| ### This is a slightly comical NBA Player Performance Predictor. | |
| ***It is designed to show a projection for performance (Wins Above Replacement) and compare it to the actual performance over a career.*** | |
| ***If the projection hangs, it is because the model is taking a long time to run. Refresh the page and give it another shot...get it?*** | |
| """ | |
| ) | |
| with gr.Row(): | |
| year = gr.Dropdown(1977, 2021, label="Season", interactive=True, step=1) | |
| player = gr.Dropdown(players, label="Player", interactive=True) | |
| with gr.Column(visible=False) as output_col: | |
| gr.Markdown( | |
| "**Below is the player forecast for the selected season plus 5 years. Next to the graph is a dropdown you can use to change the season and update the chart and see how a player's projection has changed over time.**" | |
| ) | |
| with gr.Row(): | |
| season = gr.Dropdown(seasons, label="Season", interactive=True, step=1) | |
| with gr.Row(): | |
| plt = gr.Plot() | |
| year.change(get_players, inputs=year, outputs=[player, output_col]) | |
| player.change( | |
| get_forecast, inputs=[year, player], outputs=[plt, season, output_col] | |
| ) | |
| season.change( | |
| get_forecast, inputs=[season, player], outputs=[plt, season, output_col] | |
| ) | |
| demo.launch() | |