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app.py

@@ -17,20 +17,20 @@ def home():
 @sales_revenue_predictor_api.post('/v1/product_sales_revenue')
 def predict_churn():
     # Get JSON data from the request
-    customer_data = request.get_json()
+    product_data = request.get_json()
 
     # Extract relevant customer features from the input data
     sample = {
-        'Product_Weight': customer_data['Product_Weight'],
-        'Product_Sugar_Content': customer_data['Product_Sugar_Content'],
-        'Product_Allocated_Area': customer_data['Product_Allocated_Area'],
-        'Product_Type': customer_data['Product_Type'],
-        'Product_MRP': customer_data['Product_MRP'],
-        'Store_Id': customer_data['Store_Id'],
-        'Store_Establishment_Year': customer_data['Store_Establishment_Year'],
-        'Store_Size': customer_data['Store_Size'],
-        'Store_Location_City_Type': customer_data['Store_Location_City_Type'],
-        'Store_Type': customer_data['Store_Type']
+        'Product_Weight': product_data.get('Product_Weight'),
+        'Product_Sugar_Content': product_data.get('Product_Sugar_Content'),
+        'Product_Allocated_Area': product_data.get('Product_Allocated_Area'),
+        'Product_Type': product_data.get('Product_Type'),
+        'Product_MRP': product_data.get('Product_MRP'),
+        'Store_Id': product_data.get('Store_Id'),
+        'Store_Establishment_Year': product_data.get('Store_Establishment_Year'),
+        'Store_Size': product_data.get('Store_Size'),
+        'Store_Location_City_Type': product_data.get('Store_Location_City_Type'),
+        'Store_Type': product_data.get('Store_Type')
     }
 
     # Convert the extracted data into a DataFrame