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app.py

@@ -37,20 +37,6 @@ def predict_sales():
     # Convert the extracted data into a DataFrame
     input_data = pd.DataFrame([requestData])
 
-    # create encoder with OneHotEncoder for encoding the selected values to match the training data
-    # encoder = OneHotEncoder(handle_unknown='ignore', sparse_output=False) # Important for handling unseen categories
-
-    # You MUST use the *trained* encoder to transform the new data
-    # encoded_new_data = encoder.transform(input_data[['Product_Sugar_Content','Product_Type','Store_Id','Store_Size','Store_Location_City_Type','Store_Type']])
-
-    #encoded_new_data = pd.get_dummies(
-    #  input_data,
-    #  columns=['Product_Sugar_Content','Product_Type','Store_Id','Store_Size','Store_Location_City_Type','Store_Type'],
-    #  drop_first=True,
-    #);
-    #print("The data entered are below")
-    #print(encoded_new_data)
-
     # Make a Sales prediction using the trained model
     prediction = model.predict(input_data).tolist()[0]
 
@@ -61,4 +47,4 @@ def predict_sales():
     predicted_sales = round(float(predicted_sales), 2)
 
     # Return the prediction as a JSON response
-    return jsonify({'Predicted_Sale': predicted_sales})
+    return jsonify({'Predicted_Sale': prediction})