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import gradio as gr
import joblib
import pandas as pd
import numpy as np
# Load the model and unique brand values
model = joblib.load('model.joblib')
# Define the prediction function
def predict(p_yds, p_cmp,p_att,ints,cmp_pct,rate,p_adj_ypa,p_ypa,r_att,year_drafted) :
y_pds = int(p_yds)
p_cmp = int(p_cmp)
p_att = int(p_att)
ints = int(ints)
cmp_pct = float(cmp_pct)
rate = float(rate)
p_adj_ypa = float(p_adj_ypa)
p_ypa = float(p_ypa)
r_att = int(r_att)
year_drafted = int(year_drafted)
input_data = pd.DataFrame({
'p_yds': [p_yds],
'p_cmp': [p_cmp],
'p_att': [p_att],
'ints': [ints],
'cmp_pct': [cmp_pct],
'rate': [rate],
'p_adj_ypa': [p_adj_ypa],
'p_ypa': [p_ypa],
'r_att': [r_att],
'year_drafted': [year_drafted]
})
# Perform the prediction
prediction = model.predict(input_data)
return str(prediction[0])
# Create the Gradio interface
interface = gr.Interface(
fn=predict,
inputs=[
gr.Textbox(label="Pass Yards"),
gr.Textbox(label="Pass Completions"),
gr.Textbox(label="Pass Attempts"),
gr.Textbox(label="Interceptions"),
gr.Textbox(label="Completion Percentage"),
gr.Textbox(label="Passer Rating"),
gr.Textbox(label="Adjusted Yards per Attempt"),
gr.Textbox(label="Pass Yards per Attempt"),
gr.Textbox(label="Rush Attempts"),
gr.Textbox(label="Year Drafted")
],
outputs="textbox",
title="passing to touchdown Predictor",
description="Enter all informations."
)
# Launch the app
interface.launch()
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