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app.py

@@ -153,14 +153,12 @@ def batch_predict():
                 
                 # Create DataFrame for prediction
                 input_data = pd.DataFrame([{
-                    #'Product_Id': f'FD{i+1:03d}',  # Unique product ID for batch
                     'Product_Weight': float(item['Product_Weight']),
                     'Product_Sugar_Content': str(item['Product_Sugar_Content']),
                     'Product_Allocated_Area': float(item['Product_Allocated_Area']),
                     'Product_Type': str(item['Product_Type']),
                     'Product_MRP': float(item['Product_MRP']),
                     'Store_Id': f'OUT{i+1:03d}',  # Unique store ID for batch
-                    #'Store_Establishment_Year': int(item['Store_Establishment_Year']),
                     'Store_Size': str(item['Store_Size']),
                     'Store_Location_City_Type': str(item['Store_Location_City_Type']),
                     'Store_Type': str(item['Store_Type']),

best_sales_forecasting_model.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:98f88da84764904acc790665b84defba51475a0e016939d503da47feb5f38b71
-size 45495287
+oid sha256:0c62202e5aa050bf89601aaff69b019618a661bb63046e047505d3a709ec4463
+size 45492999