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2b71e59 289b24d 2b71e59 289b24d 2b71e59 289b24d 2b71e59 289b24d 2b71e59 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | import gradio as gr
import pandas as pd
import joblib
# Load the saved model
model = joblib.load("stroke_prediction_model.pkl")
# Define feature names (from cleaned dataset)
feature_names = [
'age', 'hypertension', 'heart_disease', 'avg_glucose_level', 'bmi',
'gender_Male', 'ever_married_Yes', 'work_type_Never_worked',
'work_type_Private', 'work_type_Self-employed', 'work_type_children',
'Residence_type_Urban', 'smoking_status_formerly smoked',
'smoking_status_never smoked', 'smoking_status_smokes'
]
# Define prediction function
def predict_stroke(age, hypertension, heart_disease, avg_glucose_level, bmi,
gender, ever_married, work_type, residence_type, smoking_status):
# Encode categorical features
gender_Male = 1 if gender == "Male" else 0
ever_married_Yes = 1 if ever_married == "Yes" else 0
work_type_Never_worked = work_type_Private = work_type_Self_employed = work_type_children = 0
if work_type == "Never worked":
work_type_Never_worked = 1
elif work_type == "Private":
work_type_Private = 1
elif work_type == "Self_employed":
work_type_Self_employed = 1
elif work_type == "children":
work_type_children = 1
Residence_type_Urban = 1 if residence_type == "Urban" else 0
smoke_former = smoke_never = smoke_yes = 0
if smoking_status == "formerly smoked":
smoke_former = 1
elif smoking_status == "never smoked":
smoke_never = 1
elif smoking_status == "smokes":
smoke_yes = 1
# Build input DataFrame
input_data = pd.DataFrame({
'age': [age],
'hypertension': [hypertension],
'heart_disease': [heart_disease],
'avg_glucose_level': [avg_glucose_level],
'bmi': [bmi],
'gender_Male': [gender_Male],
'ever_married_Yes': [ever_married_Yes],
'work_type_Never_worked': [work_type_Never_worked],
'work_type_Private': [work_type_Private],
'work_type_Self_employed': [work_type_Self_employed],
'work_type_children': [work_type_children],
'Residence_type_Urban': [Residence_type_Urban],
'smoking_status_formerly smoked': [smoke_former],
'smoking_status_never smoked': [smoke_never],
'smoking_status_smokes': [smoke_yes]
})
# Make prediction
prediction = model.predict(input_data)[0]
probability = model.predict_proba(input_data)[0][1]
return "⚠️ High Risk of Stroke" if prediction == 1 else "✅ Low Risk of Stroke", f"{probability:.2%}"
# Create Gradio Interface
interface = gr.Interface(
fn=predict_stroke,
inputs=[
gr.Slider(0, 100, value=45, label="Age"),
gr.Radio(["No", "Yes"], label="Hypertension"),
gr.Radio(["No", "Yes"], label="Heart Disease"),
gr.Number(label="Average Glucose Level"),
gr.Number(label="BMI"),
gr.Radio(["Female", "Male"], label="Gender"),
gr.Radio(["No", "Yes"], label="Ever Married"),
gr.Radio(["Never worked", "Private", "Self_employed", "children"], label="Work Type"),
gr.Radio(["Rural", "Urban"], label="Residence Type"),
gr.Radio(["never smoked", "formerly smoked", "smokes"], label="Smoking Status")
],
outputs=[
gr.Textbox(label="Prediction"),
gr.Textbox(label="Stroke Probability")
],
title="🩺 Stroke Risk Prediction",
description="Predict the risk of stroke based on patient health data.",
allow_flagging='never'
)
# Launch locally for testing
if __name__ == "__main__":
interface.launch() |