Fixed run_prediction

Author: VaghinakDev

src/superannotate/lib/core/usecases/models.py

@@ -479,7 +479,7 @@ def execute(self):
             images = self._service_provider.items.list_by_names(
                 project=self._project, folder=self._folder, names=self._images_list
             ).data
-            image_ids = [image.uuid for image in images]
+            image_ids = [image.id for image in images]
             image_names = [image.name for image in images]
 
             if not len(image_names):
@@ -502,36 +502,36 @@ def execute(self):
                 ml_model_id=ml_model.id,
                 image_ids=image_ids,
             )
-            if not res.ok:
-                return self._response.data
-
-            success_images = []
-            failed_images = []
-            while len(success_images) + len(failed_images) != len(image_ids):
-                images_metadata = self._service_provider.items.list_by_names(
-                    project=self._project, folder=self._folder, names=self._images_list
-                ).data
-
-                success_images = [
-                    img.name
-                    for img in images_metadata
-                    if img.prediction_status
-                    == constances.SegmentationStatus.COMPLETED.value
-                ]
-                failed_images = [
-                    img.name
-                    for img in images_metadata
-                    if img.prediction_status
-                    == constances.SegmentationStatus.FAILED.value
-                ]
-
-                complete_images = success_images + failed_images
-                logger.info(
-                    f"prediction complete on {len(complete_images)} / {len(image_ids)} images"
-                )
-                time.sleep(5)
+            if res.ok:
+                success_images = []
+                failed_images = []
+                while len(success_images) + len(failed_images) != len(image_ids):
+                    images_metadata = self._service_provider.items.list_by_names(
+                        project=self._project, folder=self._folder, names=self._images_list
+                    ).data
+
+                    success_images = [
+                        img.name
+                        for img in images_metadata
+                        if img.prediction_status
+                        == constances.SegmentationStatus.COMPLETED.value
+                    ]
+                    failed_images = [
+                        img.name
+                        for img in images_metadata
+                        if img.prediction_status
+                        == constances.SegmentationStatus.FAILED.value
+                    ]
+
+                    complete_images = success_images + failed_images
+                    logger.info(
+                        f"prediction complete on {len(complete_images)} / {len(image_ids)} images"
+                    )
+                    time.sleep(5)
 
-            self._response.data = (success_images, failed_images)
+                self._response.data = (success_images, failed_images)
+            else:
+                self._response.errors = res.error
         return self._response
 
 

src/superannotate/lib/infrastructure/repositories.py

@@ -1,8 +1,4 @@
 import io
-from typing import List
-
-from lib.core.conditions import Condition
-from lib.core.entities import ProjectEntity
 from lib.core.entities import S3FileEntity
 from lib.core.repositories import BaseS3Repository
 
@@ -22,12 +18,3 @@ def insert(self, entity: S3FileEntity) -> S3FileEntity:
             data["Metadata"] = temp
         self.bucket.put_object(**data)
         return entity
-
-    def update(self, entity: ProjectEntity):
-        self._service.update_project(entity.to_dict())
-
-    def delete(self, uuid: int):
-        self._service.delete_project(uuid)
-
-    def get_all(self, condition: Condition = None) -> List[ProjectEntity]:
-        pass

src/superannotate/lib/infrastructure/serviceprovider.py

@@ -195,6 +195,7 @@ def run_prediction(
             self.URL_PREDICTION,
             "post",
             data={
+                "team_id": project.team_id,
                 "project_id": project.id,
                 "ml_model_id": ml_model_id,
                 "image_ids": image_ids,

tests/integration/test_ml_funcs.py

@@ -1,10 +1,10 @@
 import os
-import time
 from os.path import dirname
 
-import pytest
 from src.superannotate import SAClient
+from src.superannotate import AppException
 from tests.integration.base import BaseTestCase
+import pytest
 
 sa = SAClient()
 
@@ -23,17 +23,16 @@ def folder_path(self):
         return os.path.join(dirname(dirname(__file__)), self.TEST_FOLDER_PATH)
 
     def test_run_prediction_with_non_exist_images(self):
-        with pytest.raises(Exception) as e:
+        with self.assertRaisesRegexp(AppException, 'No valid image names were provided.'):
             sa.run_prediction(
-                self.PROJECT_NAME, ["NonExistantImage.jpg"], self.MODEL_NAME
+                self.PROJECT_NAME, ["NotExistingImage.jpg"], self.MODEL_NAME
             )
 
-    @pytest.mark.skip(reason="Need to adjust")
+    @pytest.mark.skip(reason="Test skipped due to long execution")
     def test_run_prediction_for_all_images(self):
         sa.upload_images_from_folder_to_project(
             project=self.PROJECT_NAME, folder_path=self.folder_path
         )
-        time.sleep(2)
         image_names_vector = [i["name"] for i in sa.search_items(self.PROJECT_NAME)]
         succeeded_images, failed_images = sa.run_prediction(
             self.PROJECT_NAME, image_names_vector, self.MODEL_NAME