add dummy invoke

.github/workflows/MLPipelinePullRequest.yml

@@ -88,7 +88,7 @@ jobs:
         sleep 2
         curl -X POST "http://localhost:9000/2015-03-31/functions/function/invocations" \
           -H "Content-Type: application/json" \
-          -d "{\"body\": \"{\\\"file_location\\\": \\\"s3://retrofit-data-dev/sap_change_model/sample_data_for_cicd/${timestamp}/sample_test.parquet\\\", \\\"property_id\\\": 1, \\\"portfolio_id\\\": 4, \\\"created_at\\\": \\\"now\\\", \\\"testing\\\": true}\"}"
+          -d "{\"body\": \"{\\\"file_location\\\": \\\"s3://retrofit-data-dev/sap_change_model/sample_data_for_cicd/${timestamp}/sample_test.parquet\\\", \\\"property_id\\\": 1, \\\"portfolio_id\\\": 4, \\\"created_at\\\": \\\"now\\\", \\\"testing\\\": true, \\\"warm\\\": true}\"}"
 
     - name: Get Lambda logs
       run: |

deployment/handlers/prediction_app.py

@@ -66,14 +66,6 @@ def handler(event, context):
         created_at = body["created_at"]
 
         # TODO: Implement the loading of the model and prediction
-
-        if "testing" in body:
-            storage_filepath = body["file_location"].replace(
-                ".parquet", "_output.parquet"
-            )
-        else:
-            storage_filepath = f"s3://{PREDICTIONS_BUCKET}/{portfolio_id}/{property_id}/{created_at}.parquet"
-
         logger.info(f"--- Initiate MLModel ---")
 
         build_model_params = settings.build_model
@@ -83,6 +75,27 @@ def handler(event, context):
 
         model = model_factory(build_model_params["model_type"])
 
+        model_filepath = build_model_params["model_save_filepath"]
+
+        if "testing" in body:
+            storage_filepath = body["file_location"].replace(
+                ".parquet", "_output.parquet"
+            )
+        elif "warm" in body:
+            logger.info("Warm up invocation - skipping prediction")
+
+            import pandas as pd
+
+            model.load_model(model_filepath)
+            return {
+                "statusCode": 200,
+                "body": json.dumps(
+                    {"message": f"{model.predict(data=pd.DataFrame({'a': [1]}))}"}
+                ),
+            }
+        else:
+            storage_filepath = f"s3://{PREDICTIONS_BUCKET}/{portfolio_id}/{property_id}/{created_at}.parquet"
+
         logger.info(f"--- Initiate Input DataClient ---")
         input_dataclient = dataclient_factory(
             dataclient_type="aws-s3",
@@ -100,7 +113,7 @@ def handler(event, context):
             output_dataclient=output_dataclient,
             model=model,
             target=feature_process_params["feature_processor_config"]["target"],
-            model_filepath=build_model_params["model_save_filepath"],
+            model_filepath=model_filepath,
             test_data_filepath=body["file_location"],
             predictions_output_filepath=storage_filepath,
             predictions_column_name=generate_predictions_params[