Revert "feat: HUMSIG-37: Enhancing ML backend connection experience (… (#5569)

Revert "feat: HUMSIG-37: Enhancing ML backend connection experience (#5546)"

This reverts commit b198728.

Co-authored-by: nik <nik@heartex.net>

Author: niklub

.pre-commit-dev.yaml

@@ -9,4 +9,3 @@ repos:
     rev: v0.9.1
     hooks:
       - id: blue
-        args: [ --verbose ]

label_studio/core/all_urls.json

@@ -946,12 +946,6 @@
         "module": "ml.api.MLBackendTrainAPI",
         "name": "ml:api:ml-train",
         "decorators": ""
-    },
-      {
-        "url": "/api/ml/<int:pk>/predict/test",
-        "module": "ml.api.MLBackendPredictAPI",
-        "name": "ml:api:ml-predict-test",
-        "decorators": ""
     },
     {
         "url": "/api/ml/<int:pk>/interactive-annotating",

label_studio/data_manager/actions/basic.py

@@ -6,7 +6,7 @@
 from core.permissions import AllPermissions
 from core.redis import start_job_async_or_sync
 from core.utils.common import load_func
-from data_manager.functions import retrieve_predictions
+from data_manager.functions import evaluate_predictions
 from django.conf import settings
 from projects.models import Project
 from tasks.functions import update_tasks_counters
@@ -24,7 +24,7 @@ def retrieve_tasks_predictions(project, queryset, **kwargs):
     :param project: project instance
     :param queryset: filtered tasks db queryset
     """
-    retrieve_predictions(queryset)
+    evaluate_predictions(queryset)
     return {'processed_items': queryset.count(), 'detail': 'Retrieved ' + str(queryset.count()) + ' predictions'}
 
 
@@ -138,7 +138,6 @@ def async_project_summary_recalculation(tasks_ids_list, project_id):
         'title': 'Retrieve Predictions',
         'order': 90,
         'dialog': {
-            'modal_title': 'Retrieve Predictions',
             'text': 'Send the selected tasks to all ML backends connected to the project.'
             'This operation might be abruptly interrupted due to a timeout. '
             'The recommended way to get predictions is to update tasks using the Label Studio API.'

label_studio/data_manager/actions/next_task.py

@@ -32,7 +32,6 @@ def next_task(project, queryset, **kwargs):
     # serialize task
     context = {'request': request, 'project': project, 'resolve_uri': True, 'annotations': False}
     serializer = NextTaskSerializer(next_task, context=context)
-
     response = serializer.data
     response['queue'] = queue_info
     return response

label_studio/data_manager/actions/predictions_to_annotations.py

@@ -80,7 +80,7 @@ def predictions_to_annotations_form(user, project):
                 {
                     'type': 'select',
                     'name': 'model_version',
-                    'label': 'Choose predictions',
+                    'label': 'Choose a model',
                     'options': versions,
                 }
             ],
@@ -95,10 +95,8 @@ def predictions_to_annotations_form(user, project):
         'title': 'Create Annotations From Predictions',
         'order': 91,
         'dialog': {
-            'modal_title': 'Create Annotations From Predictions',
-            'text': 'Create annotations from predictions using selected predictions set '
-            'for each selected task.'
-            'Your account will be assigned as an owner to those annotations. ',
+            'text': 'This action will create new annotations from predictions with the selected model version '
+            'for each selected task.',
             'type': 'confirm',
             'form': predictions_to_annotations_form,
         },

label_studio/data_manager/api.py

@@ -8,7 +8,7 @@
 from core.utils.common import int_from_request, load_func
 from core.utils.params import bool_from_request
 from data_manager.actions import get_all_actions, perform_action
-from data_manager.functions import get_prepare_params, get_prepared_queryset
+from data_manager.functions import evaluate_predictions, get_prepare_params, get_prepared_queryset
 from data_manager.managers import get_fields_for_evaluation
 from data_manager.models import View
 from data_manager.serializers import DataManagerTaskSerializer, ViewResetSerializer, ViewSerializer
@@ -255,15 +255,11 @@ def get(self, request):
             # keep ids ordering
             page = [tasks_by_ids[_id] for _id in ids]
 
-            # TODO MM TODO this needs a discussion, because I'd expect
-            # people to retrieve manually instead on DM load, plus it
-            # will slow down initial DM load
-
             # retrieve ML predictions if tasks don't have them
-            # if not review and project.retrieve_predictions_automatically:
-            #     tasks_for_predictions = Task.objects.filter(id__in=ids, predictions__isnull=True)
-            #     retrieve_predictions(tasks_for_predictions)
-            #     [tasks_by_ids[_id].refresh_from_db() for _id in ids]
+            if not review and project.evaluate_predictions_automatically:
+                tasks_for_predictions = Task.objects.filter(id__in=ids, predictions__isnull=True)
+                evaluate_predictions(tasks_for_predictions)
+                [tasks_by_ids[_id].refresh_from_db() for _id in ids]
 
             if flag_set('fflag_fix_back_leap_24_tasks_api_optimization_05092023_short'):
                 serializer = self.task_serializer_class(
@@ -275,18 +271,13 @@ def get(self, request):
             else:
                 serializer = self.task_serializer_class(page, many=True, context=context)
             return self.get_paginated_response(serializer.data)
-
-        # TODO
         # all tasks
-        # if project.retrieve_predictions_automatically:
-        #     retrieve_predictions(queryset.filter(predictions__isnull=True))
-
+        if project.evaluate_predictions_automatically:
+            evaluate_predictions(queryset.filter(predictions__isnull=True))
         queryset = Task.prepared.annotate_queryset(
             queryset, fields_for_evaluation=fields_for_evaluation, all_fields=all_fields, request=request
         )
-
         serializer = self.task_serializer_class(queryset, many=True, context=context)
-
         return Response(serializer.data)
 
 
@@ -386,7 +377,6 @@ def post(self, request):
         # perform action and return the result dict
         kwargs = {'request': request}  # pass advanced params to actions
         result = perform_action(action_id, project, queryset, request.user, **kwargs)
-
         code = result.pop('response_code', 200)
 
         return Response(result, status=code)

label_studio/data_manager/functions.py

@@ -314,23 +314,16 @@ def get_prepared_queryset(request, project):
     return queryset
 
 
-def retrieve_predictions(tasks, backend=None):
-    """Call ML backend to retrieve predictions with the task queryset as an input"""
+def evaluate_predictions(tasks):
+    """Call ML backend for prediction evaluation of the task queryset"""
     if not tasks:
         return
 
-    if not backend:
-        project = tasks[0].project
-        backend = project.ml_backends.first()
+    project = tasks[0].project
 
-    # IMPORTANT change here, ml_backends.all => ml_backends.first
-    # we are using only one ML backend, not multiple
-    if backend:
-        return backend.predict_and_save(tasks=tasks)
-
-    # for ml_backend in project.ml_backends.first():
-    #     # tasks = tasks.filter(~Q(predictions__model_version=ml_backend.model_version))
-    #     ml_backend.predict_and_save(tasks=tasks)
+    for ml_backend in project.ml_backends.all():
+        # tasks = tasks.filter(~Q(predictions__model_version=ml_backend.model_version))
+        ml_backend.predict_tasks(tasks)
 
 
 def filters_ordering_selected_items_exist(data):

label_studio/ml/api.py

@@ -6,7 +6,6 @@
 from core.feature_flags import flag_set
 from core.permissions import ViewClassPermission, all_permissions
 from django.conf import settings
-from django.http import Http404
 from django.utils.decorators import method_decorator
 from django_filters.rest_framework import DjangoFilterBackend
 from drf_yasg.utils import swagger_auto_schema
@@ -77,11 +76,10 @@ class MLBackendListAPI(generics.ListCreateAPIView):
     def get_queryset(self):
         project_pk = self.request.query_params.get('project')
         project = generics.get_object_or_404(Project, pk=project_pk)
-
         self.check_object_permissions(self.request, project)
-
-        ml_backends = project.update_ml_backends_state()
-
+        ml_backends = MLBackend.objects.filter(project_id=project.id)
+        for mlb in ml_backends:
+            mlb.update_state()
         return ml_backends
 
     def perform_create(self, serializer):
@@ -204,58 +202,6 @@ def post(self, request, *args, **kwargs):
         return Response(status=status.HTTP_200_OK)
 
 
-@method_decorator(
-    name='post',
-    decorator=swagger_auto_schema(
-        tags=['Machine Learning'],
-        operation_summary='Predict',
-        operation_description="""
-        After you add an ML backend, call this API with the ML backend ID to run a test prediction on specific task data               
-        """,
-        manual_parameters=[
-            openapi.Parameter(
-                name='id',
-                type=openapi.TYPE_INTEGER,
-                in_=openapi.IN_PATH,
-                description='A unique integer value identifying this ML backend.',
-            ),
-        ],
-        responses={
-            200: openapi.Response(title='Predicting OK', description='Predicting has successfully started.'),
-            500: openapi.Response(
-                description='Predicting error',
-                schema=openapi.Schema(
-                    title='Error message',
-                    description='Error message',
-                    type=openapi.TYPE_STRING,
-                    example='Server responded with an error.',
-                ),
-            ),
-        },
-    ),
-)
-class MLBackendPredictTestAPI(APIView):
-    serializer_class = MLBackendSerializer
-    permission_required = all_permissions.projects_change
-
-    def post(self, request, *args, **kwargs):
-        ml_backend = generics.get_object_or_404(MLBackend, pk=self.kwargs['pk'])
-        self.check_object_permissions(self.request, ml_backend)
-
-        random = request.query_params.get('random', False)
-        if random:
-            task = Task.get_random(project=ml_backend.project)
-            if not task:
-                raise Http404
-
-            kwargs = ml_backend._predict(task)
-            return Response(**kwargs)
-
-        # TODO this needs to be implemented and needs to have a specific task param
-        ml_backend.predict()
-        return Response(status=status.HTTP_200_OK)
-
-
 @method_decorator(
     name='post',
     decorator=swagger_auto_schema(
@@ -281,64 +227,29 @@ def post(self, request, *args, **kwargs):
     ),
 )
 class MLBackendInteractiveAnnotating(APIView):
-    """ """
 
     permission_required = all_permissions.tasks_view
 
-    def _error_response(self, message, log_function=logger.info):
-        """ """
-        log_function(message)
-        return Response({'errors': [message]}, status=status.HTTP_200_OK)
-
-    def _get_task(self, ml_backend, validated_data):
-        """ """
-        return generics.get_object_or_404(Task, pk=validated_data['task'], project=ml_backend.project)
-
-    def _get_credentials(self, request, context, project):
-        """ """
-        if flag_set('ff_back_dev_2362_project_credentials_060722_short', request.user):
-            context.update(
-                project_credentials_login=project.task_data_login,
-                project_credentials_password=project.task_data_password,
-            )
-        return context
-
-    def _get_ml_results(self, ml_api_result):
-        """ """
-        results = ml_api_result.response.get('results', [None])
-        if isinstance(results, list) and len(results) >= 1:
-            return results[0]
-
-        return None
-
     def post(self, request, *args, **kwargs):
-        """ """
         ml_backend = generics.get_object_or_404(MLBackend, pk=self.kwargs['pk'])
-        self.check_object_permissions(request, ml_backend)
-
+        self.check_object_permissions(self.request, ml_backend)
         serializer = MLInteractiveAnnotatingRequest(data=request.data)
         serializer.is_valid(raise_exception=True)
+        validated_data = serializer.validated_data
 
-        task = self._get_task(ml_backend, serializer.validated_data)
-        context = self._get_credentials(request, serializer.validated_data.get('context', {}), task.project)
-
-        ml_api_result = ml_backend.interactive_annotating(task, context, user=self.request.user)
+        task = generics.get_object_or_404(Task, pk=validated_data['task'], project=ml_backend.project)
+        context = validated_data.get('context')
 
-        if ml_api_result.is_error:
-            message = f'Prediction not created for project {self}: {ml_api_result.error_message}'
-            return self._error_response(message)
-
-        if not isinstance(ml_api_result.response, dict) or 'results' not in ml_api_result.response:
-            message = f'Incorrect response from ML service it must be a dict and contain "results" key: {ml_api_result.response}'
-            return self._error_response(message)
-
-        ml_results = self._get_ml_results(ml_api_result)
+        if flag_set('ff_back_dev_2362_project_credentials_060722_short', request.user):
+            context['project_credentials_login'] = task.project.task_data_login
+            context['project_credentials_password'] = task.project.task_data_password
 
-        if not ml_results:
-            message = f'ML backend has to return a list with at least 1 annotation but it returned: {type(ml_results)}'
-            return self._error_response(message, logger.warning)
+        result = ml_backend.interactive_annotating(task, context, user=request.user)
 
-        return Response({'data': ml_results}, status=status.HTTP_200_OK)
+        return Response(
+            result,
+            status=status.HTTP_200_OK,
+        )
 
 
 @method_decorator(