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import gradio as gr
import xgboost as xgb
import joblib
import numpy as np
from sklearn.preprocessing import StandardScaler
import pandas as pd
import shap
import matplotlib.pyplot as plt
# Load the model and the scaler
model = joblib.load('best_XGB.pkl')
scaler = joblib.load('scaler.pkl') # Ensure the scaler is saved and loaded with the same scikit-learn version
cutoff = 42 # Custom cutoff probability
# Use TreeExplainer for XGBoost models
explainer = shap.TreeExplainer(model)
# Define the prediction function with preprocessing, scaling, and SHAP analysis
def predict_heart_attack(Gender, age, cigsPerDay, BPMeds, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose):
# Define feature names in the same order as the training data
feature_names = ['Gender', 'age', 'cigsPerDay', 'BPMeds', 'prevalentHyp', 'diabetes', 'totChol', 'sysBP', 'diaBP', 'BMI', 'heartRate', 'glucose']
# Create a DataFrame with the correct feature names for prediction
features = pd.DataFrame([[Gender, age, cigsPerDay, BPMeds, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose]], columns=feature_names)
# Standardize the features (scaling)
scaled_features = scaler.transform(features)
# Predict probabilities
proba = model.predict_proba(scaled_features)[:, 1] # Probability of class 1 (heart attack)
# Apply custom cutoff
if proba[0]*100 >= cutoff:
prediction_class = "Heart_Attack_Risk.Consult your doctor"
else:
prediction_class = "No_Heart_Attack_Risk.Still make regular checkup "
# Generate SHAP values for the prediction using the explainer
shap_values = explainer(features)
# Plot SHAP values
plt.figure(figsize=(8, 6))
shap.waterfall_plot(shap_values[0]) # Using the SHAP Explanation object
plt.savefig('shap_plot.png') # Save SHAP plot to a file
result = f"Predicted Probability: {proba[0]*100:.2f}%. Predicted Class with cutoff {cutoff}%: {prediction_class}"
return result, 'shap_plot.png' # Return the prediction and SHAP plot
# Create the Gradio interface with preprocessing, prediction, and SHAP visualization
with gr.Blocks() as app:
with gr.Row():
with gr.Column():
Gender = gr.Radio([0, 1], label="Gender (0=Female, 1=Male)")
cigsPerDay = gr.Slider(0, 40, step=1, label="Cigarettes per Day")
prevalentHyp = gr.Radio([0, 1], label="Prevalent Hypertension (0=No, 1=Yes)")
totChol = gr.Slider(100, 400, step=1, label="Total Cholesterol in mg/dl")
diaBP = gr.Slider(60, 120, step=1, label="Diastolic/Lower BP")
heartRate = gr.Slider(50, 120, step=1, label="Heart Rate")
with gr.Column():
age = gr.Slider(20, 80, step=1, label="Age (years)")
BPMeds = gr.Radio([0, 1], label="On BP Medications (0=No, 1=Yes)")
diabetes = gr.Radio([0, 1], label="Diabetes (0=No, 1=Yes)")
sysBP = gr.Slider(90, 200, step=1, label="Systolic BP/Higher BP")
BMI = gr.Slider(15, 40, step=0.1, label="Body Mass Index (BMI) in kg/m2")
glucose = gr.Slider(50, 250, step=1, label="Fasting Glucose Level")
# Center-aligned prediction output
with gr.Row():
gr.HTML("<div style='text-align: center; width: 100%'>Heart Attack Prediction</div>")
with gr.Row():
prediction_output = gr.Textbox(label="", interactive=False, elem_id="prediction_output")
with gr.Row():
shap_plot_output = gr.Image(label="SHAP Analysis")
# Link inputs and prediction output
submit_btn = gr.Button("Submit")
submit_btn.click(fn=predict_heart_attack, inputs=[Gender, age, cigsPerDay, BPMeds, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose], outputs=[prediction_output, shap_plot_output])
app.launch(share = True)
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