Update app.py

app.py

@@ -3,13 +3,12 @@ import numpy as np
 import pandas as pd
 import joblib
 
-# Load trained models and scaler
+# Load models and scaler
 model_binary = joblib.load("model_binary_cv.pkl")
 model_multiclass = joblib.load("model_multiclass_cv.pkl")
 scaler = joblib.load("scaler.pkl")
 label_enc = joblib.load("label_encoder_multiclass.pkl")
 
-# Feature names for consistency with scaler
 feature_names = [
     'Patient Id', 'Age', 'Total cholesterol', 'HDL', 'LDL', 'VLDL',
     'TRIGLYCERIDES', 'before glycemic control random blood sugar',
@@ -37,51 +36,49 @@ def predict_diabetes(
     smoking_status,
     family_history_cardiovascular_disease
 ):
-    # Convert binary inputs
-    alcohol_consumption = int(alcohol_consumption)
-    family_history_diabetes = int(family_history_diabetes)
+    try:
+        alcohol_consumption = int(alcohol_consumption)
+        family_history_diabetes = int(family_history_diabetes)
 
-    # One-hot encoding
-    Gender_FEMALE = 1 if Gender == "Female" else 0
-    Gender_MALE = 1 if Gender == "Male" else 0
+        Gender_FEMALE = 1 if Gender == "Female" else 0
+        Gender_MALE = 1 if Gender == "Male" else 0
 
-    dietary_non_veg = 1 if dietary_habits == "Non-vegetarian" else 0
-    dietary_non_veg_dup = dietary_non_veg
-    dietary_veg = 1 if dietary_habits == "Vegetarian" else 0
+        dietary_non_veg = 1 if dietary_habits == "Non-vegetarian" else 0
+        dietary_non_veg_dup = dietary_non_veg
+        dietary_veg = 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
+        smoking_no = 1 if smoking_status == "No" else 0
+        smoking_yes = 1 if smoking_status == "Yes" else 0
 
-    family_cvd_no = 1 if family_history_cardiovascular_disease == "No" else 0
-    family_cvd_yes = 1 if family_history_cardiovascular_disease == "Yes" else 0
+        family_cvd_no = 1 if family_history_cardiovascular_disease == "No" else 0
+        family_cvd_yes = 1 if family_history_cardiovascular_disease == "Yes" else 0
 
-    # Create input DataFrame
-    input_values = [[
-        0, Age, Total_cholesterol, HDL, LDL, VLDL, TRIGLYCERIDES,
-        before_random_blood_sugar, before_HbA1c, alcohol_consumption, family_history_diabetes,
-        Gender_FEMALE, Gender_MALE, dietary_non_veg, dietary_non_veg_dup, dietary_veg,
-        smoking_no, smoking_yes, family_cvd_no, family_cvd_yes
-    ]]
-    input_df = pd.DataFrame(input_values, columns=feature_names)
+        input_values = [[
+            0, Age, Total_cholesterol, HDL, LDL, VLDL, TRIGLYCERIDES,
+            before_random_blood_sugar, before_HbA1c, alcohol_consumption, family_history_diabetes,
+            Gender_FEMALE, Gender_MALE, dietary_non_veg, dietary_non_veg_dup, dietary_veg,
+            smoking_no, smoking_yes, family_cvd_no, family_cvd_yes
+        ]]
+        input_df = pd.DataFrame(input_values, columns=feature_names)
 
-    # Scale input
-    input_scaled = scaler.transform(input_df)
+        input_scaled = scaler.transform(input_df)
 
-    # Step 1: Binary prediction
-    binary_pred = model_binary.predict(input_scaled)[0]
-    if binary_pred == 0:
-        return "✅ Prediction: No Diabetes Risk (Normal)"
+        binary_pred = model_binary.predict(input_scaled)[0]
+        if binary_pred == 0:
+            return "✅ Prediction: No Diabetes Risk (Normal)"
 
-    # Step 2: Multiclass prediction
-    multi_pred = model_multiclass.predict(input_scaled)[0]
-    status = label_enc.inverse_transform([multi_pred])[0]
+        multi_pred = model_multiclass.predict(input_scaled)[0]
+        status = label_enc.inverse_transform([multi_pred])[0]
 
-    status_display = "Prediabetes" if status == "prediabetes" else "Diabetes"
-    emoji = "⚠️🟡" if status == "prediabetes" else "🚨🔴"
+        status_display = "Prediabetes" if status == "prediabetes" else "Diabetes"
+        emoji = "⚠️🟡" if status == "prediabetes" else "🚨🔴"
+
+        return f"{emoji} At Risk: {status_display}"
+
+    except Exception as e:
+        return f"❌ Error during prediction: {str(e)}"
 
-    return f"{emoji} At Risk: {status_display}"
 
-# Gradio UI
 with gr.Blocks() as demo:
     gr.Markdown("## 🩺 Diabetes Risk and Type Predictor")
     gr.Markdown(
@@ -108,8 +105,8 @@ with gr.Blocks() as demo:
             before_HbA1c = gr.Number(label="HbA1c (before control)", 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")
+            alcohol_consumption = gr.Checkbox(label="Alcohol Consumption (Yes)", value=False)
+            family_history_diabetes = gr.Checkbox(label="Family History of Diabetes (Yes)", value=False)
             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")