Update app.py

app.py

@@ -1,63 +1,117 @@
 import gradio as gr
-import joblib
 import numpy as np
-import pandas as pd
+import joblib
 
-# Load saved artifacts
-model = joblib.load('best_multiclass_model.pkl')
-scaler = joblib.load('feature_scaler.pkl')
+# Load saved objects
+model = joblib.load('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)
-    
+def predict_diabetes(
+    Age,
+    Total_cholesterol,
+    HDL,
+    LDL,
+    VLDL,
+    TRIGLYCERIDES,
+    before_random_blood_sugar,
+    before_HbA1c,
+    alcohol_consumption,
+    family_history_diabetes,
+    Gender,
+    dietary_habits,
+    smoking_status,
+    family_history_cardiovascular_disease
+):
+    # Convert categorical inputs to numeric
+    Gender_FEMALE = 1 if Gender == "Female" else 0
+    Gender_MALE = 1 if Gender == "Male" else 0
+
+    dietary_non_vegetarian = 1 if dietary_habits == "Non-vegetarian" else 0
+    dietary_vegetarian = 1 if dietary_habits == "Vegetarian" else 0
+
+    smoking_no = 1 if smoking_status == "No" else 0
+    smoking_yes = 1 if smoking_status == "Yes" else 0
+
+    family_history_cvd_no = 1 if family_history_cardiovascular_disease == "No" else 0
+    family_history_cvd_yes = 1 if family_history_cardiovascular_disease == "Yes" else 0
+
+    # Prepare input in the right order as during training
+    input_data = np.array([[
+        Age,
+        Total_cholesterol,
+        HDL,
+        LDL,
+        VLDL,
+        TRIGLYCERIDES,
+        before_random_blood_sugar,
+        before_HbA1c,
+        int(alcohol_consumption),
+        int(family_history_diabetes),
+        Gender_FEMALE,
+        Gender_MALE,
+        dietary_non_vegetarian,
+        dietary_vegetarian,
+        smoking_no,
+        smoking_yes,
+        family_history_cvd_no,
+        family_history_cvd_yes
+    ]])
+
     # 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"
-)
+    input_scaled = scaler.transform(input_data)
+
+    # Predict
+    pred_enc = model.predict(input_scaled)
+    pred_label = label_enc.inverse_transform(pred_enc)[0]
+
+    return f"Predicted Status: {pred_label}"
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Diabetes Predictor")
+    gr.Markdown(
+        """
+        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.
+        """
+    )
+
+    with gr.Row():
+        with gr.Column():
+            Age = gr.Number(label="Age", value=30)
+            Total_cholesterol = gr.Number(label="Total cholesterol", value=180)
+            HDL = gr.Number(label="HDL", value=50)
+            LDL = gr.Number(label="LDL", value=100)
+            VLDL = gr.Number(label="VLDL", value=20)
+            TRIGLYCERIDES = gr.Number(label="TRIGLYCERIDES", value=150)
+            before_random_blood_sugar = gr.Number(label="Before glycemic control random blood sugar", value=120)
+            before_HbA1c = gr.Number(label="Before glycemic control HbA1c", value=5.5)
+
+        with gr.Column():
+            alcohol_consumption = gr.Radio(label="Alcohol consumption", choices=["0", "1"], value="0")
+            family_history_diabetes = gr.Radio(label="Family history of diabetes", choices=["0", "1"], value="0")
+            Gender = gr.Radio(label="Gender", choices=["Female", "Male"], value="Female")
+            dietary_habits = gr.Radio(label="Dietary habits", choices=["Vegetarian", "Non-vegetarian"], value="Vegetarian")
+            smoking_status = gr.Radio(label="Smoking status", choices=["No", "Yes"], value="No")
+            family_history_cardiovascular_disease = gr.Radio(label="Family history of cardiovascular disease", choices=["No", "Yes"], value="No")
+
+    submit_btn = gr.Button("Submit")
+    output = gr.Textbox(label="Prediction")
+
+    submit_btn.click(
+        fn=predict_diabetes,
+        inputs=[
+            Age, Total_cholesterol, HDL, LDL, VLDL, TRIGLYCERIDES,
+            before_random_blood_sugar, before_HbA1c,
+            alcohol_consumption, family_history_diabetes, Gender,
+            dietary_habits, smoking_status, family_history_cardiovascular_disease
+        ],
+        outputs=output
+    )
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()