added the mock callback for entry classification;

analysis_module/mockserver.py

@@ -362,6 +362,29 @@ def process_entry_extraction_mock(body) -> Any:
         except Exception:
             logger.error("Could not send data to callback url", exc_info=True)
 
+def entry_classification_mock(body) -> Any:
+    process_entry_extraction_mock.apply_async(
+        args=(body,), countdown=2
+    )  # Trigger task after 2 seconds
+    return json.dumps({"status": "Successfully received the request."}), 200
+
+@shared_task
+def process_entry_extraction_mock(body) -> Any:
+    callback_payload = MOCK_ENTRY_CLASSIFICATION
+    callback_payload.update({
+       "client_id": body["entries"][0]["client_id"]
+    })
+    callback_url = body["callback_url"]
+    try:
+        requests.post(
+            callback_url,
+            json=callback_payload,
+            timeout=30
+        )
+        logger.info("Successfully send data on callback url for entry classification")
+    except Exception:
+        logger.error("Could not send data to callback url", exc_info=True)
+
 
 TYPE_ACTIONS_MOCK = {
     "topicmodel": topicmodeling_mock_model,
@@ -370,7 +393,8 @@ def process_entry_extraction_mock(body) -> Any:
     "ngrams": ngrams_mock_model,
     "geolocation": geolocation_mock_model,
     "text-extraction": text_extraction_mock,
-    "entry-extraction-classification": entry_extraction_mock
+    "entry-extraction-classification": entry_extraction_mock,
+    "entry-classification": entry_classification_mock
 }
 
 

analysis_module/views/predictions.py

@@ -13,6 +13,7 @@
 from nlp_scripts.model_prediction.tags_mapping import AF2NLPMapping
 from nlp_scripts.model_prediction.model_prediction import ModelTagsPrediction
 from nlp_scripts.model_prediction.all_tags_mapping import get_vf_list
+from analysis_module.mockserver import process_mock_request
 
 import logging
 
@@ -63,12 +64,13 @@ def entry_classification(request: Request):
     serializer.is_valid(raise_exception=True)
 
     entries = serializer.validated_data["entries"]
+    req_type = NLPRequest.FeaturesType.ENTRY_CLASSIFICATION
+
     if serializer.validated_data.get("mock") or IS_MOCKSERVER:
-        data = MOCK_ENTRY_CLASSIFICATION
-        data.update({
-            "client_id": entries[0]["client_id"]
-        })
-        return Response(data)
+        return process_mock_request(
+            request=serializer.validated_data,
+            request_type=req_type
+        )
     if not entries:
         return Response({})
     # Create a NLPRequest object
@@ -79,8 +81,6 @@ def entry_classification(request: Request):
         created_by=request.user,
     )
 
-    req_type = NLPRequest.FeaturesType.ENTRY_CLASSIFICATION
-
     resp = {
         "type": req_type,
         "message": "Request has been successfully queued.",