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import gradio as gr
import pickle
import pandas as pd
# Load model (make sure Heart_Project.pkl is in repo root)
with open("Heart_Project.pkl", "rb") as f:
model = pickle.load(f)
# Prediction function
def predict(Age, Sex, ChestPainType, RestingBP, Cholesterol,
FastingBS, RestingECG, MaxHR, ExerciseAngina, Oldpeak, ST_Slope):
SEX_MAP = {'M': 1, 'F': 0}
CP_MAP = {'TA': 0, 'ATA': 1, 'NAP': 2, 'ASY': 3}
ECG_MAP = {'Normal': 0, 'ST': 1, 'LVH': 2}
ANGINA_MAP = {'Y': 1, 'N': 0}
SLOPE_MAP = {'Up': 2, 'Flat': 1, 'Down': 0}
# Build feature row (same order as used during training)
features = [[
Age,
SEX_MAP[Sex],
CP_MAP[ChestPainType],
RestingBP,
Cholesterol,
FastingBS,
ECG_MAP[RestingECG],
MaxHR,
ANGINA_MAP[ExerciseAngina],
Oldpeak,
SLOPE_MAP[ST_Slope]
]]
df = pd.DataFrame(features, columns=[
'Age','Sex','ChestPainType','RestingBP','Cholesterol',
'FastingBS','RestingECG','MaxHR','ExerciseAngina',
'Oldpeak','ST_Slope'
])
# Use predict_proba if available, otherwise fallback to predict
if hasattr(model, "predict_proba"):
prob = model.predict_proba(df)[0, 1]
else:
# fallback: model might not have predict_proba
prob = None
pred = model.predict(df)[0]
if prob is None:
return f"Prediction: {'Heart Disease' if pred==1 else 'No Heart Disease'}"
return f"Prediction: {'Heart Disease' if pred==1 else 'No Heart Disease'} | Probability: {prob:.2f}"
# Build Gradio UI
demo = gr.Interface(
fn=predict,
inputs=[
gr.Number(label="Age"),
gr.Radio(["M","F"], label="Sex"),
gr.Radio(["TA","ATA","NAP","ASY"], label="Chest Pain Type"),
gr.Number(label="RestingBP"),
gr.Number(label="Cholesterol"),
gr.Radio([0,1], label="FastingBS"),
gr.Radio(["Normal","ST","LVH"], label="RestingECG"),
gr.Number(label="MaxHR"),
gr.Radio(["Y","N"], label="ExerciseAngina"),
gr.Number(label="Oldpeak"),
gr.Radio(["Up","Flat","Down"], label="ST Slope")
],
outputs="text",
title="❤️ Heart Disease Predictor",
description="Enter patient details to predict the risk of heart disease."
)
if __name__ == "__main__":
demo.launch()
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