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.gitattributes

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+best_XGB.pkl filter=lfs diff=lfs merge=lfs -text

app.py

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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)

best_XGB.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3d5422417966b1cc081d0b6e9772e6cee262ee72f203853be4a28d53859fbbcf
+size 1347623

requirements.txt

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+cloudpickle==3.1.0
+gradio==5.1.0
+gradio_client==1.4.0
+joblib==1.4.2
+numpy==1.26.4
+pandas==2.2.2
+shap==0.46.0
+scikit-learn==1.4.2
+slicer==0.0.8
+xgboost==2.0.3
+matplotlib