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from fastai.tabular.all import * |
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import gradio as gr |
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import pathlib |
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plt = platform.system() |
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if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath |
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df = pd.read_csv("rookie_year.csv") |
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learn = load_learner("export.pkl") |
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columns = ["Name", "G", "GS", "Cmp", "Att", "Yds", "Cmp%", "TD", "Int", "Y/G", "Sk"] |
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def predict2(data): |
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row = data.drop("Name", axis=1).astype(float) |
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row["Cmp"] = row["Att"].item() * row["Cmp%"].item() |
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pred_row, clas, probs = learn.predict(row.iloc[0]) |
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prediction = pred_row.decode()["Tier"].item() |
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return prediction |
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demo2 = gr.Interface(fn=predict2, |
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inputs=gr.Dataframe(row_count=1, col_count=8, headers=[x for x in columns if x not in ["Cmp", "G", "GS"]], label="Rookie Year Stats"), |
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outputs=gr.Textbox(label="Prediction"), |
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title="Rookie QB Career Prediction (Stats)", |
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description="Given stats of a presumed rookie QB, predict their career tier. Uses data from https:\/\/www.pro-football-reference.com. Tiers based on PFR Approximate Value.", |
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article="See more details at https://github.com/mhrice/Rookie-QB-Predictions" |
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) |
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demo2.launch() |
