app.py

app.py

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+import gradio as gr
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+import joblib
+
+# Load the trained model
+model = joblib.load('your_trained_model.pkl')
+
+# Define the function to preprocess user inputs
+def preprocess_input(age, sex, cp_type, resting_bp, cholesterol, fasting_bs, ecg, max_hr, exercise_angina, oldpeak, st_slope):
+    # Convert categorical features to one-hot encoding
+    sex_mapping = {'F': [0, 1], 'M': [1, 0]}
+    sex_encoded = sex_mapping.get(sex, [0, 0])
+    
+    cp_mapping = {'ASY': [1, 0, 0, 0], 'ATA': [0, 1, 0, 0], 'NAP': [0, 0, 1, 0], 'TA': [0, 0, 0, 1]}
+    cp_encoded = cp_mapping.get(cp_type, [0, 0, 0, 0])
+    
+    ecg_mapping = {'Normal': [1, 0, 0], 'ST': [0, 1, 0], 'LVH': [0, 0, 1]}
+    ecg_encoded = ecg_mapping.get(ecg, [0, 0, 0])
+    
+    # Normalize numerical features
+    numerical_features = [age, resting_bp, cholesterol, max_hr, oldpeak]
+    numerical_features = np.array(numerical_features).reshape(1, -1)
+    scaler = StandardScaler()
+    numerical_features_scaled = scaler.fit_transform(numerical_features)
+    
+    # Combine all features
+    processed_input = np.concatenate([numerical_features_scaled, [fasting_bs], sex_encoded, cp_encoded, ecg_encoded, [exercise_angina], [st_slope]], axis=1)
+    
+    return processed_input
+
+# Define the function to make predictions
+def predict_heart_disease(age, sex, cp_type, resting_bp, cholesterol, fasting_bs, ecg, max_hr, exercise_angina, oldpeak, st_slope):
+    processed_input = preprocess_input(age, sex, cp_type, resting_bp, cholesterol, fasting_bs, ecg, max_hr, exercise_angina, oldpeak, st_slope)
+    prediction = model.predict(processed_input)[0]
+    return "Heart Disease Likely" if prediction == 1 else "No Heart Disease"
+
+# Create Gradio interface
+inputs = [
+    gr.inputs.Slider(0, 100, default=50, label="Age"), 
+    gr.inputs.Radio(['F', 'M'], label="Sex"),
+    gr.inputs.Dropdown(['TA', 'ATA', 'NAP', 'ASY'], label="Chest Pain Type"),
+    gr.inputs.Slider(80, 200, default=120, label="Resting Blood Pressure"),
+    gr.inputs.Slider(100, 600, default=200, label="Cholesterol"),
+    gr.inputs.Checkbox(label="Fasting Blood Sugar > 120 mg/dl"),
+    gr.inputs.Dropdown(['Normal', 'ST', 'LVH'], label="Resting ECG"),
+    gr.inputs.Slider(60, 202, default=100, label="Maximum Heart Rate Achieved"),
+    gr.inputs.Radio(['Y', 'N'], label="Exercise-induced Angina"),
+    gr.inputs.Slider(0, 10, default=5, label="Oldpeak"),
+    gr.inputs.Dropdown(['Up', 'Flat', 'Down'], label="ST Slope")
+]
+
+output = gr.outputs.Textbox(label="Heart Disease Prediction")
+
+gr.Interface(predict_heart_disease, inputs, output, title="Heart Disease Prediction", description="Enter patient information to predict likelihood of heart disease.").launch()