Create app.py

app.py

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+import gradio as gr
+import joblib
+import numpy as np
+import pandas as pd
+
+# Load saved artifacts
+model = joblib.load('xgb_model.pkl')
+scaler = joblib.load('scaler.pkl')
+label_enc = joblib.load('label_encoder.pkl')
+
+# List of feature names in correct order (exclude Patient Id and target)
+feature_names = [
+    'Age', 'Total cholesterol', 'HDL', 'LDL', 'VLDL', 'TRIGLYCERIDES',
+    'before glycemic control random blood sugar', 'before glycemic control HbA1c',
+    'alcohol consumption', 'family_history_diabetes', 'Gender_FEMALE', 'Gender_MALE',
+    'dietary habits_non-vegetarian', 'dietary habits_non-vegetarian ', 'dietary habits_vegetarian',
+    'smoking status_no', 'smoking status_yes',
+    'family_history_cardiovascular_disease_no', 'family_history_cardiovascular_disease_yes'
+]
+
+# Function to predict diabetes status
+def predict_diabetes(*inputs):
+    # Convert inputs to dataframe
+    input_df = pd.DataFrame([inputs], columns=feature_names)
+    
+    # Scale input features
+    scaled_features = scaler.transform(input_df)
+    
+    # Predict label index
+    pred_encoded = model.predict(scaled_features)[0]
+    
+    # Decode label to original class
+    pred_label = label_enc.inverse_transform([pred_encoded])[0]
+    
+    return pred_label
+
+# Define Gradio input components
+inputs = [
+    gr.Number(label=feature) for feature in feature_names
+]
+
+# Gradio interface
+title = "Diabetes Predictor"
+description = """
+Developed by Dr. Vinod Kumar Yata's research group  
+School of Allied and Healthcare Sciences, Malla Reddy University, Hyderabad, India
+
+⚠️ Warning:  
+This is an experimental tool and should not be used for medical diagnosis.  
+Always consult a licensed healthcare provider for medical advice.
+"""
+
+iface = gr.Interface(
+    fn=predict_diabetes,
+    inputs=inputs,
+    outputs=gr.Textbox(label="Predicted Diabetes Status"),
+    title=title,
+    description=description,
+    theme="default"
+)
+
+if __name__ == "__main__":
+    iface.launch()