Update app.py

app.py

@@ -41,11 +41,20 @@ model = RandomForestClassifier(n_estimators=100, random_state=42)
 model.fit(X_train, y_train)
 
 # Define Prediction Function
-def predict_diabetes(gender, age, hypertension, heart_disease, smoking_history, bmi, HbA1c_level, blood_glucose_level):
-    input_data = np.array([[gender, age, hypertension, heart_disease, smoking_history, bmi, HbA1c_level, blood_glucose_level]])
-    input_scaled = scaler.transform(input_data)
+def predict_diabetes(gender, age, hypertension, heart_disease, smoking_history, bmi, HbA1c, blood_glucose):
+    # Convert categorical inputs to numeric
+    gender_numeric = 1 if gender == "Male" else 0  # Encode 'Male' as 1, 'Female' as 0
+    
+    # Prepare the input as a NumPy array
+    input_data = np.array([[gender_numeric, age, hypertension, heart_disease, smoking_history, bmi, HbA1c, blood_glucose]])
+
+    # Scale the input data
+    input_scaled = scaler.transform(input_data)  # Ensure all values are numeric
+
+    # Make prediction
     prediction = model.predict(input_scaled)
-    return "Diabetic" if prediction[0] == 1 else "Non-Diabetic"
+
+    return "Diabetes Positive" if prediction[0] == 1 else "Diabetes Negative"
 
 # Define UI with Gradio
 iface = gr.Interface(