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app.py

@@ -9,7 +9,7 @@ super_kart_api = Flask("Super Kart Price Predictor")
 # Load the trained machine learning model
 model = joblib.load("backend_files/super_kart_model_v1_0.joblib")
 
-# Expected feature names from training (copy these from your training code or model inspection)
+# Expected feature names from the model (adjust if your training columns differ)
 EXPECTED_COLUMNS = [
     'Product_Type_Baking Goods', 'Product_Type_Breads', 'Product_Type_Breakfast', 'Product_Type_Canned',
     'Product_Type_Dairy', 'Product_Type_Frozen Foods', 'Product_Type_Fruits and Vegetables',
@@ -57,7 +57,7 @@ def predict_sales():
     features_df['Store_Size'] = features_df['Store_Size'].map(size_mapping)
     features_df['Store_Location_City_Type'] = features_df['Store_Location_City_Type'].map(city_mapping)
     
-    # Align columns with expected model features (add missing with 0, drop extras)
+    # Align with expected columns (add missing as 0, drop extras)
     features_df = features_df.reindex(columns=EXPECTED_COLUMNS, fill_value=0)
     
     # Make prediction