SIENTIAPDE-1199

Refactor logging in Gates and MLFlow activities to use info level for key operations

- Updated logging statements in the Gates class to replace debug logs with info logs for input and output gate operations, enhancing visibility.
- Modified MLFlow class to use info logs for data transformation and prediction processes, improving clarity in the logging output.
- Adjusted OPC class to return the count of successfully written tags, providing better insight into data writing operations.

laborious/activities/mlflow.py

@@ -41,7 +41,7 @@ class MLFlow(BaseActivity):
             dict[str, Any]: The transformed data.
         """
         metadata = input_data['metadata']
-        self.debug('Transforming data...', metadata)
+        self.info('Transforming data...', metadata)
         data = DataFrame(input_data['data'])
         model_name = input_data['model_name']
         model_retention = input_data['model_retention']
@@ -70,6 +70,8 @@ class MLFlow(BaseActivity):
         self.debug("Transform response data:", metadata)
         self.debug(json.dumps(response_data, indent=4), metadata)
 
+        self.info("Data transformed successfully", metadata)
+
         return response_data
 
     @activity.defn(name="request_predict")
@@ -85,7 +87,7 @@ class MLFlow(BaseActivity):
             dict[str, Any]: The predicted data.
         """
         metadata = input_data['metadata']
-        self.debug('Predicting data...', metadata)
+        self.info('Predicting data...', metadata)
         data = DataFrame(input_data['data'])
         model_name = input_data['model_name']
         model_retention = input_data['model_retention']
@@ -100,6 +102,8 @@ class MLFlow(BaseActivity):
         self.debug("Prediction response data:", metadata)
         self.debug(json.dumps(response_data, indent=4), metadata)
 
+        self.info("Prediction completed successfully", metadata)
+
         return response_data
 
     @activity.defn(name="retrain_model")