Add 3 Files

app.py

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+import gradio as gr
+import joblib
+import pandas as pd
+import numpy as np
+
+# Load the saved XGBoost model
+model = joblib.load("xgboost_cardiovascular_model.pkl")
+
+# Define feature names (same as training)
+feature_names = ["age", "ap_hi", "ap_lo", "cholesterol", "gluc", "smoke", "alco", "active", "weight"]
+
+# Define the prediction function
+def predict_heart_disease(age, systolic_bp, diastolic_bp, cholesterol, glucose, smoking, alcohol, active, weight):
+    # Convert inputs into a DataFrame
+    input_data = pd.DataFrame([[age, systolic_bp, diastolic_bp, cholesterol, glucose, smoking, alcohol, active, weight]], columns=feature_names)
+
+    # Make prediction
+    prediction = model.predict(input_data)[0]
+
+    # Return result
+    return "High Risk of Cardiovascular Disease" if prediction == 1 else "Low Risk of Cardiovascular Disease"
+
+# Define Gradio interface
+inputs = [
+    gr.Slider(29, 64, step=1, label="Age (in years)"),
+    gr.Slider(90, 180, step=1, label="Systolic Blood Pressure (ap_hi)"),
+    gr.Slider(60, 120, step=1, label="Diastolic Blood Pressure (ap_lo)"),
+    gr.Radio([1, 2, 3], label="Cholesterol Level (1=Normal, 2=Above Normal, 3=Well Above Normal)"),
+    gr.Radio([1, 2, 3], label="Glucose Level (1=Normal, 2=Above Normal, 3=Well Above Normal)"),
+    gr.Radio([0, 1], label="Smoking (0=No, 1=Yes)"),
+    gr.Radio([0, 1], label="Alcohol Intake (0=No, 1=Yes)"),
+    gr.Radio([0, 1], label="Physically Active (0=No, 1=Yes)"),
+    gr.Slider(40, 180, step=1, label="Weight (in kg)")
+]
+
+output = gr.Textbox(label="Cardiovascular Disease Risk")
+
+gr.Interface(fn=predict_heart_disease, inputs=inputs, outputs=output, title="Heart Disease Risk Predictor",
+             description="Enter your health details to estimate your cardiovascular disease risk using AI.",
+             theme="compact").launch(share=True)

requirements.txt

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+xgboost
+shap
+gradio
+scikit-learn
+pandas
+numpy
+matplotlib
+seaborn
+joblib
+tensorflow

xgboost_cardiovascular_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:72c207d7d17a6751683ee8d9cbca33468525f2f07ccbd9f1f215d2fbbfe6156d
+size 769691