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| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| import pickle | |
| import xgboost as xgb | |
| from catboost import CatBoostRegressor | |
| import mlbstatsapi | |
| import tensorflow as tf | |
| pd.set_option('display.max_columns', None) | |
| # pd.set_option('display.max_rows', None) | |
| def predict(inning, game_id): | |
| if inning == "Seven": | |
| inning = 7 | |
| elif inning == "Eight": | |
| inning = 8 | |
| elif inning == "Five": | |
| inning = 5 | |
| elif inning == "Six": | |
| inning = 6 | |
| df = data_retrieve(inning, game_id) | |
| # print(df) | |
| df_home = df[df['Home/Away'] == "Home"] | |
| df_away = df[df['Home/Away'] == "Away"] | |
| if (len(df_home) < inning) or (len(df_away) < inning): | |
| return [None, None] | |
| # print(df_home) | |
| df_main = pd.read_csv("Score_prediction_dataset_25th_August_TS_3seas.csv") | |
| df_main = df_main.drop(columns=['Opp_LOB']) | |
| df_main = df_main[(df_main['Inning'] <= inning)] | |
| df_main = df_main[df_main['Team_Name'].isin(team_names)] | |
| df_main = df_main[df_main['Opposition_Team'].isin(team_names)] | |
| df_main = df_main[(df_main['Team_Name'] != 'American League All-Stars') & (df_main['Team_Name'] != 'National League All-Stars')] | |
| df_main = df_main.dropna() | |
| df_main = df_main.drop_duplicates() | |
| df_main = df_main.pivot(index=['Game_ID', 'Team_Name', 'Opposition_Team', 'Home/Away', 'Final_Score'], columns='Inning', values=['Hits', 'Opp_Hits', 'Errors', 'Runs', 'Opp_Runs', 'LOB']) | |
| df_main.columns = [f'{feature}_{inning}' for feature, inning in df_main.columns] | |
| df_main = df_main.reset_index() | |
| df_main = df_main.drop(columns=['Final_Score']) | |
| df_main = pd.get_dummies(df_main, columns=['Team_Name', 'Opposition_Team']) | |
| # df = pd.DataFrame([data], columns=["Team_Name", "Opposition_Team", "Inning", "Home/Away", "Hits", "Opp_Hits", "Errors", "Runs", "Opp_Runs", "LOB"]) | |
| pivoted_df_home = df_home.pivot(index=['Game_ID', 'Team_Name', 'Opposition_Team', 'Home/Away', 'Final_Score'], columns='Inning', values=['Hits', 'Opp_Hits', 'Errors', 'Runs', 'Opp_Runs', 'LOB']) | |
| pivoted_df_home.columns = [f'{feature}_{inning}' for feature, inning in pivoted_df_home.columns] | |
| # print(pivoted_df_home) | |
| pivoted_df_home = pivoted_df_home.reset_index() | |
| pivoted_df_home = pd.get_dummies(pivoted_df_home, columns=['Team_Name', 'Opposition_Team']) | |
| pivoted_df_home = pivoted_df_home.reindex(columns=df_main.columns, fill_value=0) | |
| pivoted_df_away = df_away.pivot(index=['Game_ID', 'Team_Name', 'Opposition_Team', 'Home/Away', 'Final_Score'], columns='Inning', values=['Hits', 'Opp_Hits', 'Errors', 'Runs', 'Opp_Runs', 'LOB']) | |
| pivoted_df_away.columns = [f'{feature}_{inning}' for feature, inning in pivoted_df_away.columns] | |
| pivoted_df_away = pivoted_df_away.reset_index() | |
| pivoted_df_away = pd.get_dummies(pivoted_df_away, columns=['Team_Name', 'Opposition_Team']) | |
| pivoted_df_away = pivoted_df_away.reindex(columns=df_main.columns, fill_value=0) | |
| print(pivoted_df_home) | |
| home_away_status = {'Home': 0, 'Away': 1} | |
| pivoted_df_home['Home/Away'] = pivoted_df_home['Home/Away'].map(home_away_status) | |
| pivoted_df_away['Home/Away'] = pivoted_df_away['Home/Away'].map(home_away_status) | |
| pivoted_df_home = pivoted_df_home.astype(int) | |
| pivoted_df_away = pivoted_df_away.astype(int) | |
| pivoted_df_home = pivoted_df_home.drop(['Game_ID'], axis=1) | |
| pivoted_df_away = pivoted_df_away.drop(['Game_ID'], axis=1) | |
| # return | |
| # print(len(df.columns)) | |
| if inning == 8: | |
| model = tf.keras.models.load_model('ANNR_ts_CLAS_inn8_exp8_model.keras') | |
| elif inning ==7: | |
| model = tf.keras.models.load_model('ANNR_ts_CLAS_inn7_exp11_model.keras') | |
| elif inning ==6: | |
| model = tf.keras.models.load_model('ANNR_ts_CLAS_inn6_exp7_model.keras') | |
| elif inning ==5: | |
| model = tf.keras.models.load_model('ANNR_ts_CLAS_inn5_exp4_model.keras') | |
| # with open('pca_model4.pkl', 'rb') as f: | |
| # pca = pickle.load(f) | |
| # with open('label_encoder_teams_xgbr1_exp3.pkl', 'rb') as f: | |
| # label_encoder = pickle.load(f) | |
| # print(pivoted_df_home) | |
| # df = pca.transform(df) | |
| # return | |
| winner_prob_1 = model.predict(pivoted_df_home) | |
| winner__prob_2 = model.predict(pivoted_df_away) | |
| if winner_prob_1 < winner__prob_2: | |
| winner = 'Home_Team' | |
| else: | |
| winner = 'Away_Team' | |
| # return score_1 | |
| return winner | |
| def data_retrieve(inning, game_id): | |
| mlb = mlbstatsapi.Mlb() | |
| df = pd.DataFrame(columns = ["Game_ID", "Team_Name", "Opposition_Team", "Inning", "Home/Away", "Hits", "Opp_Hits", "Errors", "Runs", "Opp_Runs", "LOB", "Opp_LOB", "Final_Score"]) | |
| try: | |
| linescore = mlb.get_game_line_score(game_id, verify = False) | |
| except: | |
| gr.Info("Error retrieving data!!!") | |
| home_runs = 0 | |
| away_runs = 0 | |
| home_hits = 0 | |
| away_hits = 0 | |
| home_errors = 0 | |
| away_errors = 0 | |
| home_leftonbase = 0 | |
| away_leftonbase = 0 | |
| count = 0 | |
| for i in range(inning): | |
| try: | |
| inning = linescore.innings[i].num | |
| home_team_name = mlb.get_game_box_score(game_id, verify = False).teams.home.team.name | |
| away_team_name = mlb.get_game_box_score(game_id, verify = False).teams.away.team.name | |
| home_runs += linescore.innings[i].home.runs | |
| away_runs += linescore.innings[i].away.runs | |
| home_hits += linescore.innings[i].home.hits | |
| away_hits += linescore.innings[i].away.hits | |
| home_errors += linescore.innings[i].home.errors | |
| away_errors += linescore.innings[i].away.errors | |
| home_leftonbase += linescore.innings[i].home.leftonbase | |
| away_leftonbase += linescore.innings[i].away.leftonbase | |
| home_score = linescore.teams.home.runs | |
| away_score = linescore.teams.away.runs | |
| except: | |
| gr.Info(f"Error retrieving inning {i+1} data!!!") | |
| continue | |
| home_dict = {"Game_ID": game_id, "Team_Name": home_team_name, "Opposition_Team": away_team_name, "Inning": inning, "Home/Away": 'Home', "Hits": home_hits, "Opp_Hits": away_hits, "Errors": home_errors, "Runs": home_runs, "Opp_Runs": away_runs, "LOB": home_leftonbase, "Opp_LOB": away_leftonbase, "Final_Score": home_score} | |
| away_dict = {"Game_ID": game_id, "Team_Name": away_team_name, "Opposition_Team": home_team_name, "Inning": inning, "Home/Away": 'Away', "Hits": away_hits, "Opp_Hits": home_hits, "Errors": away_errors, "Runs": away_runs, "Opp_Runs": home_runs, "LOB": away_leftonbase, "Opp_LOB": home_leftonbase, "Final_Score": away_score} | |
| #print(home_dict) | |
| home_df = pd.DataFrame([home_dict]) | |
| away_df = pd.DataFrame([away_dict]) | |
| df = pd.concat([df,home_df], ignore_index=True) | |
| df = pd.concat([df,away_df], ignore_index=True) | |
| count += 1 | |
| if count != inning: | |
| gr.Info("All reuiqred innings are not available!!!") | |
| return df | |
| team_names = ["Arizona Diamondbacks", | |
| "Atlanta Braves", | |
| "Baltimore Orioles", | |
| "Boston Red Sox", | |
| "Chicago Cubs", | |
| "Chicago White Sox", | |
| "Cincinnati Reds", | |
| "Cleveland Guardians", | |
| "Colorado Rockies", | |
| "Detroit Tigers", | |
| "Houston Astros", | |
| "Kansas City Royals", | |
| "Los Angeles Angels", | |
| "Los Angeles Dodgers", | |
| "Miami Marlins", | |
| "Milwaukee Brewers", | |
| "Minnesota Twins", | |
| "New York Mets", | |
| "New York Yankees", | |
| "Oakland Athletics", | |
| "Philadelphia Phillies", | |
| "Pittsburgh Pirates", | |
| "San Diego Padres", | |
| "San Francisco Giants", | |
| "Seattle Mariners", | |
| "St. Louis Cardinals", | |
| "Tampa Bay Rays", | |
| "Texas Rangers", | |
| "Toronto Blue Jays", | |
| "Washington Nationals"] | |
| with gr.Blocks() as demo: | |
| # gr.Image("../Documentation/Context Diagram.png", scale=2) | |
| # gr(title="Your Interface Title") | |
| gr.Markdown(""" | |
| <center> | |
| <span style='font-size: 50px; font-weight: Bold; font-family: "Graduate", serif'> | |
| MLB Score Predictor V2 | |
| </span> | |
| </center> | |
| """) | |
| # gr.Markdown(""" | |
| # <center> | |
| # <span style='font-size: 30px; line-height: 0.1; font-weight: Bold; font-family: "Graduate", serif'> | |
| # Admin Dashboard | |
| # </span> | |
| # </center> | |
| # """) | |
| with gr.Row(): | |
| inning = gr.Radio(["Five", "Six", "Seven", "Eight"], label="Inning", scale=1) | |
| game_id = gr.Number(None, minimum=0, label="Game_ID", scale=1) | |
| # with gr.Row(): | |
| # # with gr.Column(): | |
| # # # venue = gr.Dropdown(choices = ["Home", "Away"], value="Away", max_choices = 1, label="Home/Away Status", scale=1) | |
| # # inning = gr.Number(None, label="Inning", minimum = 1, maximum = 8, scale=1) | |
| # with gr.Column(): | |
| # # opp_venue = gr.Dropdown(choices = ["Home", "Away"], value="Home", max_choices = 1, label="Opposition Home/Away Status", scale=1) | |
| # game_id = gr.Number(None, minimum=0, label="Game_ID", scale=1) | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| # team = gr.Dropdown(choices = team_names, max_choices = 1, label="Team", scale=1) | |
| # with gr.Column(): | |
| # opp_team = gr.Dropdown(choices = team_names, max_choices = 1, label="Opposition Team", scale=1) | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| # hits = gr.Number(None, minimum=0, label="Hits - (H)", scale=1) | |
| # with gr.Column(): | |
| # opp_hits = gr.Number(None, minimum=0, label="Opposition Hits - (H)", scale=1) | |
| # # summarize_btn = gr.Button(value="Summarize Text", size = 'sm') | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| # errors = gr.Number(None, minimum=0, label="Errors - (E)", scale=2) | |
| # with gr.Column(): | |
| # opp_errors = gr.Number(None, minimum=0, label="Opposition Errors - (E)", scale=2) | |
| # # runs = gr.Number(None, minimum=0, label="Runs - (R)", scale=1) | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| # lob = gr.Number(None, minimum=0, label="Left on Base - (LOB)", scale=1) | |
| # with gr.Column(): | |
| # opp_lob = gr.Number(None, minimum=0, label="Opposition Left on Base - (LOB)", scale=1) | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| # runs = gr.Number(None, minimum=0, label="Runs - (R)", scale=1) | |
| # with gr.Column(): | |
| # opp_runs = gr.Number(None, minimum=0, label="Opposition Runs - (R)", scale=1) | |
| with gr.Row(): | |
| predict_btn = gr.Button(value="Predict", size = 'sm') | |
| with gr.Row(): | |
| with gr.Column(): | |
| Winning_Team = gr.Textbox(label="Predicted Winner", scale=1) | |
| # with gr.Column(): | |
| # final_score_home1 = gr.Textbox(label="Home Team Predicted Score", scale=1) | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| # final_score_away2 = gr.Textbox(label="Predicted Score Model CATB", scale=1) | |
| # with gr.Column(): | |
| # final_score_home2 = gr.Textbox(label="Opposition Predicted Score Model CATB", scale=1) | |
| # patent_doc.upload(document_to_text, inputs = [patent_doc, slider, select_model], outputs=summary_doc) | |
| predict_btn.click(predict, inputs=[inning, game_id], outputs=[Winning_Team]) | |
| # predict_btn.click(predict, inputs=[inning, game_id], outputs=final_score_home1) | |
| # predict_btn.click(predict_2, inputs=[team, inning, venue, hits, errors, lob, runs, opp_team, opp_runs, opp_hits], outputs=final_score_away2) | |
| # predict_btn.click(predict_2, inputs=[opp_team, inning, opp_venue, opp_hits, opp_errors, opp_lob, opp_runs, team, runs, hits], outputs=final_score_home2) | |
| demo.launch(inbrowser=True) |