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import joblib
import pandas as pd
import gradio as gr
# Load model and preprocessor
try:
model = joblib.load("stroke_rf_model.pkl")
preprocessor = joblib.load("preprocessor.pkl")
print("✅ Model and preprocessor loaded successfully.")
except Exception as e:
print(f"❌ Error loading model/preprocessor: {str(e)}")
model = None
preprocessor = None
# Define prediction function
def predict_stroke(gender, age, hypertension, heart_disease, ever_married,
work_type, residence_type, avg_glucose_level, smoking_status, bmi):
if model is None or preprocessor is None:
return "Error: Model or preprocessor not loaded."
data = pd.DataFrame([{
'gender': gender,
'age': age,
'hypertension': hypertension,
'heart_disease': heart_disease,
'ever_married': ever_married,
'work_type': work_type,
'Residence_type': residence_type,
'avg_glucose_level': avg_glucose_level,
'smoking_status': smoking_status,
'bmi': bmi
}])
try:
processed_data = preprocessor.transform(data)
prediction = model.predict(processed_data)
return "⚠️ Stroke Risk" if prediction[0] == 1 else "✅ No Stroke Risk"
except Exception as e:
print(f"❌ Prediction error: {str(e)}")
return f"Error: {str(e)}"
# Gradio Interface
iface = gr.Interface(
fn=predict_stroke,
inputs=[
gr.Radio(choices=["Male", "Female"], label="Gender"),
gr.Slider(minimum=1, maximum=100, step=1, label="Age"),
gr.Radio(choices=[0, 1], label="Hypertension (0=No, 1=Yes)"),
gr.Radio(choices=[0, 1], label="Heart Disease (0=No, 1=Yes)"),
gr.Dropdown(choices=["Yes", "No"], label="Ever Married"),
gr.Dropdown(choices=["Private", "Self-employed", "Govt_job", "children", "Never_worked"], label="Work Type"),
gr.Radio(choices=["Urban", "Rural"], label="Residence Type"),
gr.Number(label="Average Glucose Level"),
gr.Dropdown(choices=["never smoked", "formerly smoked", "smokes", "Unknown"], label="Smoking Status"),
gr.Number(label="BMI")
],
outputs="text",
title="🩺 Stroke Risk Prediction App",
description="Predict the likelihood of stroke based on health metrics.",
allow_flagging="never"
)
# Launch the app
if __name__ == "__main__":
iface.launch() |