enhancements

app.py

@@ -275,48 +275,49 @@ hba1c_level = st.number_input("HbA1c Level", min_value=0.0, value=6.0)
 blood_glucose_level = st.number_input("Blood Glucose Level", min_value=0, value=100)
 
 if st.button("Predict Diabetes"):
-    # Create a DataFrame for the user input
-    input_data = pd.DataFrame({
-        'gender': [gender],
-        'age': [age],
-        'hypertension': [int(hypertension)],  # Convert categorical numerical features to int
-        'heart_disease': [int(heart_disease)],
-        'smoking_history': [smoking_history],
-        'bmi': [bmi],
-        'HbA1c_level': [hba1c_level],
-        'blood_glucose_level': [blood_glucose_level]
-    })
-
-    # Ensure encoding is applied correctly
-    encoded_input = encoder.transform(input_data[['gender', 'smoking_history']])
-    encoded_input_df = pd.DataFrame(encoded_input.toarray(), columns=encoder.get_feature_names_out())
-
-    # Drop the original categorical columns and concatenate the encoded features
-    input_data = input_data.drop(['gender', 'smoking_history'], axis=1)
-    input_data = pd.concat([input_data, encoded_input_df], axis=1)
-
-    # Ensure that the input data has the same columns as training data
-    missing_cols = set(X_train.columns) - set(input_data.columns)  # ✅ Corrected line
-    for col in missing_cols:
-        input_data[col] = 0  # Add missing columns with zero values
-
-    # Reorder columns to match training data
-    input_data = input_data.reindex(columns=X_train.columns, fill_value=0)
-
-    # Convert all column names to strings
-    input_data.columns = input_data.columns.astype(str)
-
-    # Scale the user input (convert back to DataFrame after transformation)
-    input_data_scaled = scaler.transform(input_data)
-    input_data_scaled = pd.DataFrame(input_data_scaled, columns=input_data.columns)  # Convert back to DataFrame
-
-    # Make prediction using the selected model
-    prediction = selected_model.predict(input_data_scaled)
-
-    # Display the prediction
-    st.write("Prediction:")
-    if prediction[0] == 0:
-        st.info("The model predicts that you do not have diabetes.")
-    else:
-        st.warning("The model predicts that you have diabetes.")
+    with st.spinner("Prrocessing inputs..."):
+        # Create a DataFrame for the user input
+        input_data = pd.DataFrame({
+            'gender': [gender],
+            'age': [age],
+            'hypertension': [int(hypertension)],  # Convert categorical numerical features to int
+            'heart_disease': [int(heart_disease)],
+            'smoking_history': [smoking_history],
+            'bmi': [bmi],
+            'HbA1c_level': [hba1c_level],
+            'blood_glucose_level': [blood_glucose_level]
+        })
+
+        # Ensure encoding is applied correctly
+        encoded_input = encoder.transform(input_data[['gender', 'smoking_history']])
+        encoded_input_df = pd.DataFrame(encoded_input.toarray(), columns=encoder.get_feature_names_out())
+
+        # Drop the original categorical columns and concatenate the encoded features
+        input_data = input_data.drop(['gender', 'smoking_history'], axis=1)
+        input_data = pd.concat([input_data, encoded_input_df], axis=1)
+
+        # Ensure that the input data has the same columns as training data
+        missing_cols = set(X_train.columns) - set(input_data.columns)  # ✅ Corrected line
+        for col in missing_cols:
+            input_data[col] = 0  # Add missing columns with zero values
+
+        # Reorder columns to match training data
+        input_data = input_data.reindex(columns=X_train.columns, fill_value=0)
+
+        # Convert all column names to strings
+        input_data.columns = input_data.columns.astype(str)
+
+        # Scale the user input (convert back to DataFrame after transformation)
+        input_data_scaled = scaler.transform(input_data)
+        input_data_scaled = pd.DataFrame(input_data_scaled, columns=input_data.columns)  # Convert back to DataFrame
+
+        # Make prediction using the selected model
+        prediction = selected_model.predict(input_data_scaled)
+
+        # Display the prediction
+        st.write("Prediction:")
+        if prediction[0] == 0:
+            st.info("The model predicts that you do not have diabetes.")
+        else:
+            st.warning("The model predicts that you have diabetes.")