Adding information messages about the log path and prediction result

app.py

@@ -38,12 +38,10 @@ print(f"saved_model_file_path: {saved_model_file_path}\n")
 # Retrieve serialized model object
 insurance_charge_predictor = joblib.load(filename=saved_model_file_path)
 
-
 # Prepare the logging functionality
 log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
 log_folder = log_file.parent
-print(f"\nDebug:\n\tlog_file: {log_file}\n\tlog_folder: {log_folder}\n")
-
+print(f"\nInformation:\n\tlog_file: {log_file}\n\tlog_folder: {log_folder}\n")
 
 # Scheduler will log every 2 API calls:
 scheduler = CommitScheduler(
@@ -54,8 +52,10 @@ scheduler = CommitScheduler(
     every=2
 )
 
-# Define the predict function which will take features, convert to dataframe and make predictions using the saved model
+# Define the "predict function" which will take features, convert to dataframe and make predictions using the saved model
 # the functions runs when 'Submit' is clicked or when a API request is made
+# IMPORTANT Note: do not modify the names of keys for "sample" and "scheduler"; the keys should be named exactly as the names in the columns in the DataFrame.
+#                 Otherwise, an run-time error will occur.
 #-------------------------------------------------------------------------------------------------------------------------------------------------------------
 def predict_insurance_charge(age, bmi, children, sex, smoker, region):
     sample = {
@@ -89,12 +89,12 @@ def predict_insurance_charge(age, bmi, children, sex, smoker, region):
             f.write("\n")
 
     prediction_result = prediction[0][0]
-    print(f"\nDebug - Prediction result: {prediction_result} - {type(prediction_result)}\n")
-    #print(f"\nDebug - Prediction result[0]: {prediction_result[0]} - {type(prediction_result[0])}\n")
-    #print(f"\nDebug - Prediction result: {prediction_result[0][0]} - {type(prediction_result[0][0])}\n")    
+    print(f"\nPrediction result: {prediction_result} - {type(prediction_result)}\n")
+    #print(f"\nDebug - prediction[0]: {prediction[0]} - {type(prediction[0])}\n")
+    #print(f"\nDebug - prediction[0][0]: {prediction[0][0]} - {type(prediction[0][0])}\n")
     return prediction_result
-    
     #return prediction[0]
+    #return prediction[0][0]
 #--------------------------------------------------------------------------------------------------------------------------------------------------------------
 
 # Set up UI components for input and output
@@ -108,7 +108,6 @@ region_input = gr.Dropdown(['southeast', 'southwest', 'northeast', 'northwest'],
 # Output component
 model_output = gr.Label(label="Insurance Charge [$]")
 
-
 # Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"
 demo = gr.Interface(
     fn=predict_insurance_charge,