Update app.py

app.py

@@ -37,15 +37,14 @@ def predict_sales():
 
     # Extract relevant customer features from the input data
     sample = {
-    "Product_Sugar_Content": user_data["Product_Sugar_Content"],   # e.g. "Low Sugar"
-    "Product_Type": user_data["Product_Type"],                     # e.g. "Snack Foods"
-    "Store_Id": user_data["Store_Id"],                             # e.g. "OUT002"
-    "Store_Size": user_data["Store_Size"],                         # e.g. "Medium"
-    "Store_Location_City_Type": user_data["Store_Location_City_Type"], # e.g. "Tier 2"
-    "Store_Type": user_data["Store_Type"],                         # e.g. "Supermarket Type1"
-    "Product_Weight": user_data["Product_Weight"],                 # numeric field
-    "Product_Price": user_data["Product_Price"],                   # numeric field
-    "Store_Area": user_data["Store_Area"],                         # numeric field
+    "Product_Weight": user_data["Product_Weight"],
+    "Product_Sugar_Content": user_data["Product_Sugar_Content"],
+    "Product_Allocated_Area": user_data["Product_Allocated_Area"],
+    "Product_Type": user_data["Product_Type"],
+    "Product_MRP": user_data["Product_MRP"],
+    "Store_Size": user_data["Store_Size"],
+    "Product_Store_Sales_Total": user_data.get("Product_Store_Sales_Total", 0),
+    "Store_Age": user_data.get("Store_Age", 10)
     }
    
     # Convert to DataFrame and apply mapping