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from fastai.tabular.all import *
import gradio as gr
import pathlib
plt = platform.system()
if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
# path = Path()
df = pd.read_csv("rookie_year.csv")
learn = load_learner("export.pkl")
columns = ["Name", "G", "GS", "Cmp", "Att", "Yds", "Cmp%", "TD", "Int", "Y/G", "Sk"]
def predict2(data):
row = data.drop("Name", axis=1).astype(float)
row["Cmp"] = row["Att"].item() * row["Cmp%"].item()
pred_row, clas, probs = learn.predict(row.iloc[0])
prediction = pred_row.decode()["Tier"].item()
return prediction
demo2 = gr.Interface(fn=predict2,
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"),
outputs=gr.Textbox(label="Prediction"),
title="Rookie QB Career Prediction (Stats)",
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.",
article="See more details at https://github.com/mhrice/Rookie-QB-Predictions"
)
demo2.launch() |