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app.py

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+import gradio as gr
+import cfbd
+import numpy as np
+import pandas as pd
+from fastai.tabular import *
+from fastai.tabular.all import *
+
+configuration = cfbd.Configuration()
+configuration.api_key["Authorization"] = "be81N7cB5mpl4z+BdHBuD5wUACNkh5YSAhO8uaC3tKCRAgC9WMhJjoHrO3Qx3TFp"
+configuration.api_key_prefix["Authorization"] = "Bearer"
+api_config = cfbd.ApiClient(configuration)
+teams_api = cfbd.TeamsApi(api_config)
+ratings_api = cfbd.RatingsApi(api_config)
+games_api = cfbd.GamesApi(api_config)
+betting_api = cfbd.BettingApi(api_config)
+
+all_teams = teams_api.get_teams()
+
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+
+def normalize_teams():
+    teams = teams_api.get_fbs_teams()
+    team_names = []
+    for team in teams:
+        team_names.append(team.school)
+
+    return team_names
+
+
+def home_team(year, team):
+    elo = find_most_recent_elo(year, team)
+    team_details = filter_team(team)
+
+    return dict(
+        team=team,
+        conference=team_details.conference,
+        elo=elo
+    )
+
+
+def away_team(year, team):
+    elo = find_most_recent_elo(year, team)
+    team_details = filter_team(team)
+
+    return dict(
+        team=team,
+        conference=team_details.conference,
+        elo=elo
+    )
+
+
+def filter_team(team_name):
+    for team in all_teams:
+        if team.school == team_name:
+            return team
+
+
+def enable_teams():
+    return gr.update(choices=teams, value=None, interactive=True), gr.update(choices=teams, value=None,
+                                                                             interactive=True)
+
+
+def predict(hteam, ateam):
+    learn = load_learner('talking_tech_neural_net.pkl')
+
+    game_details = dict(
+        neutral_site=False,
+        home_team=hteam['team'],
+        home_conference=hteam['conference'],
+        home_elo=hteam['elo'],
+        away_team=ateam['team'],
+        away_conference=ateam['conference'],
+        away_elo=ateam['elo'],
+    )
+
+    pdf = pd.DataFrame([game_details])
+    dl = learn.dls.test_dl(pdf)
+    pdf["predicted"] = learn.get_preds(dl=dl)[0].numpy()
+
+    return pdf
+
+
+def find_most_recent_elo(year, team):
+    games = games_api.get_games(team=team, year=year)
+    reversed = sorted(games, key=lambda x: x.week, reverse=True)
+
+    elo = null
+    if reversed[0].home_team == team:
+        if reversed[0].home_postgame_elo != None:
+            elo = reversed[0].home_postgame_elo
+        else:
+            elo = reversed[0].home_pregame_elo
+    else:
+        if reversed[0].away_postgame_elo != None:
+            elo = reversed[0].away_postgame_elo
+        else:
+            elo = reversed[0].away_pregame_elo
+
+    return elo
+
+
+teams = normalize_teams()
+
+with gr.Blocks() as app:
+    with gr.Row():
+        with gr.Column(scale=1):
+            year = gr.Dropdown([2023, 2022, 2021, 2020, 2019, 2018, 2017], label="Year")
+    with gr.Row():
+        with gr.Column(scale=1):
+            team_one = gr.Dropdown(choices=teams, label="Home Team", interactive=False)
+        with gr.Column(scale=1):
+            team_two = gr.Dropdown(choices=teams, label="Away Team", interactive=False)
+    with gr.Row():
+        with gr.Column(scale=1):
+            team_one_label = gr.JSON(label='Home Team Details')
+        with gr.Column(scale=1):
+            team_two_label = gr.JSON(label='Away Team Details')
+    with gr.Row():
+        predict_btn = gr.Button('Predict Spread')
+    with gr.Row():
+        data_table = gr.Dataframe(
+            label="Prediction Details",
+            headers=["Neutral Site", "Home Team", "Home Conference", "Home ELO", "Home Team", "Home Conference",
+                     "Home ELO", "Predicted Spread"],
+            datatype=["bool", "str", "str", "number", "str", "str", "number", "number"],
+            row_count=1,
+            col_count=(8, "dynamic")
+        )
+
+    year.select(enable_teams, outputs=[team_one, team_two])
+    team_one.select(home_team, inputs=[year, team_one], outputs=[team_one_label])
+    team_two.select(away_team, inputs=[year, team_two], outputs=[team_two_label])
+    predict_btn.click(predict, inputs=[team_one_label, team_two_label], outputs=[data_table])
+
+app.launch()

requirements.txt

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+cfbd
+fastai
+pandas

talking_tech_neural_net.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3df4b8e982e3aa3501401a01ebd7bb178009473c8516bd4bc8cf3c9144b1b18f
+size 137060