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app.py

@@ -7,34 +7,30 @@ from flask import Flask, request, jsonify
 super_kart_api = Flask("Super Kart Price Predictor")
 
 # Load the trained machine learning model
-model_path = "super_kart_model_v1_0.joblib"  # Path after upload (root)
-try:
-    model = joblib.load(model_path)
-    print(f"Model loaded successfully from {model_path}")
-except FileNotFoundError:
-    raise FileNotFoundError(f"Model file not found at {model_path}. Ensure it's uploaded to the repo root.")
+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_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',
+    'Product_Type_Hard Drinks', 'Product_Type_Health and Hygiene', 'Product_Type_Household',
+    'Product_Type_Meat', 'Product_Type_Others', 'Product_Type_Seafood', 'Product_Type_Snack Foods',
+    'Product_Type_Soft Drinks', 'Product_Type_Starchy Foods', 'Store_Type_Departmental Store',
+    'Store_Type_Food Mart', 'Store_Type_Supermarket Type1', 'Store_Type_Supermarket Type2',
+    'Product_Sugar_Content', 'Store_Size', 'Store_Location_City_Type', 'Product_Weight',
+    'Product_Allocated_Area', 'Product_MRP', 'Store_Establishment_Year'
+]
 
 # Define a route for the home page (GET request)
 @super_kart_api.get('/')
 def home():
-    """
-    This function handles GET requests to the root URL ('/') of the API.
-    It returns a simple welcome message.
-    """
     return "Welcome to the Super Kart Price Prediction API!"
 
 # Define an endpoint for single product sales prediction (POST request)
 @super_kart_api.post('/v1/sales')
 def predict_sales():
-    """
-    This function handles POST requests to the '/v1/sales' endpoint.
-    It expects a JSON payload containing product and store details and returns
-    the predicted sales total as a JSON response.
-    """
-    # Get the JSON data from the request body
     input_data = request.get_json()
-
-    # Extract relevant features from the JSON data
+    
     sample = {
         'Product_Weight': input_data['Product_Weight'],
         'Product_Sugar_Content': input_data['Product_Sugar_Content'],
@@ -46,35 +42,29 @@ def predict_sales():
         'Store_Location_City_Type': input_data['Store_Location_City_Type'],
         'Store_Type': input_data['Store_Type']
     }
-
-    # Convert the extracted data into a Pandas DataFrame
+    
     features_df = pd.DataFrame([sample])
-
-    # Apply one-hot encoding for nominal columns (matching training)
+    
+    # Apply one-hot encoding
     features_df = pd.get_dummies(features_df, columns=['Product_Type', 'Store_Type'], drop_first=True)
-
-    # Apply ordinal encoding (based on provided orders)
+    
+    # Apply ordinal encoding
     sugar_mapping = {'No Sugar': 0, 'Low Sugar': 1, 'Regular': 2}
     size_mapping = {'Small': 0, 'Medium': 1, 'High': 2}
     city_mapping = {'Tier 3': 0, 'Tier 2': 1, 'Tier 1': 2}
-
+    
     features_df['Product_Sugar_Content'] = features_df['Product_Sugar_Content'].map(sugar_mapping)
     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)
-
-    # Make prediction (assuming direct sales prediction; adjust if log-transformed)
+    
+    # Align columns with expected model features (add missing with 0, drop extras)
+    features_df = features_df.reindex(columns=EXPECTED_COLUMNS, fill_value=0)
+    
+    # Make prediction
     predicted_sales = model.predict(features_df)[0]
-
-    # If your model predicts log(sales), uncomment and use this instead:
-    # predicted_log_sales = model.predict(features_df)
-    # predicted_sales = np.exp(predicted_log_sales)
-
-    # Convert to Python float and round to 2 decimals
     predicted_sales = round(float(predicted_sales), 2)
-
-    # Return the predicted sales total
+    
     return jsonify({'Predicted Sales Total (in dollars)': predicted_sales})
 
-# Run the app (for testing locally; remove or adjust for production)
 if __name__ == '__main__':
     super_kart_api.run(debug=True)