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app.py

@@ -47,16 +47,16 @@ def predict_sales():
     input_data = request.get_json()
 
     sample = {
-        'product_weight': input_data['Product_Weight'],
-        'product_sugar_content': input_data['Product_Sugar_Content'],
-        'product_allocated_area': input_data['Product_Allocated_Area'],
-        'product_type': input_data['Product_Type'],
-        'product_quantity': input_data[ 'Product_Quantity'],
-        'product_mrp': input_data['Product_Quantity'],
-        'store_establishment_year': input_data['Store_Establishment_Year'],
-        'store_size': input_data['Store_Size'],
-        'store_location_city_type': input_data['Store_Location_City_Type'],
-        'store_type': input_data['Store_Type']
+        'Product_Weight': input_data['Product_Weight'],
+        'Product_Sugar_Content': input_data['Product_Sugar_Content'],
+        'Product_Allocated_Area': input_data['Product_Allocated_Area'],
+        'Product_Type': input_data['Product_Type'],
+        'Product_Quantity': input_data[ 'Product_Quantity'],
+        'Product_MRP': input_data['Product_MRP'],
+        'Store_Establishment_Year': input_data['Store_Establishment_Year'],
+        'Store_Size': input_data['Store_Size'],
+        'Store_Location_City_Type': input_data['Store_Location_City_Type'],
+        'Store_Type': input_data['Store_Type']
     }
 
     # Convert the extracted data into a Pandas DataFrame