create and pull count detector/iq

generated/groundlight_openapi_client/model/detector_creation_input_request.py

@@ -30,15 +30,13 @@
 
 
 def lazy_import():
-    from groundlight_openapi_client.model.count_mode_configuration_serializer import CountModeConfigurationSerializer
+    from groundlight_openapi_client.model.count_mode_configuration import CountModeConfiguration
     from groundlight_openapi_client.model.mode_enum import ModeEnum
-    from groundlight_openapi_client.model.multi_class_mode_configuration_serializer import (
-        MultiClassModeConfigurationSerializer,
-    )
+    from groundlight_openapi_client.model.multi_class_mode_configuration import MultiClassModeConfiguration
 
-    globals()["CountModeConfigurationSerializer"] = CountModeConfigurationSerializer
+    globals()["CountModeConfiguration"] = CountModeConfiguration
     globals()["ModeEnum"] = ModeEnum
-    globals()["MultiClassModeConfigurationSerializer"] = MultiClassModeConfigurationSerializer
+    globals()["MultiClassModeConfiguration"] = MultiClassModeConfiguration
 
 
 class DetectorCreationInputRequest(ModelNormal):

generated/model.py

@@ -1,6 +1,6 @@
 # generated by datamodel-codegen:
 #   filename:  public-api.yaml
-#   timestamp: 2024-10-05T01:13:27+00:00
+#   timestamp: 2024-10-05T01:53:23+00:00
 
 from __future__ import annotations
 
@@ -234,14 +234,6 @@ class ActionList(RootModel[List[Action]]):
     root: List[Action]
 
 
-class CountModeConfigurationSerializer(RootModel[Any]):
-    root: Any
-
-
-class MultiClassModeConfigurationSerializer(RootModel[Any]):
-    root: Any
-
-
 class AllNotes(BaseModel):
     """
     Serializes all notes for a given detector, grouped by type as listed in UserProfile.NoteCategoryChoices
@@ -331,7 +323,7 @@ class DetectorCreationInputRequest(BaseModel):
             " MULTI_CLASS"
         ),
     )
-    mode_configuration: Optional[Union[CountModeConfigurationSerializer, MultiClassModeConfigurationSerializer]] = None
+    mode_configuration: Optional[Union[CountModeConfiguration, MultiClassModeConfiguration]] = None
 
 
 class ImageQuery(BaseModel):

spec/public-api.yaml

@@ -819,8 +819,8 @@ components:
             * `MULTI_CLASS` - MULTI_CLASS
         mode_configuration:
           oneOf:
-          - $ref: '#/components/schemas/CountModeConfigurationSerializer'
-          - $ref: '#/components/schemas/MultiClassModeConfigurationSerializer'
+          - $ref: '#/components/schemas/CountModeConfiguration'
+          - $ref: '#/components/schemas/MultiClassModeConfiguration'
           nullable: true
       required:
       - name

src/groundlight/client.py

@@ -16,6 +16,7 @@
 from groundlight_openapi_client.model.label_value_request import LabelValueRequest
 from model import (
     ROI,
+    BinaryClassificationResult,
     Detector,
     ImageQuery,
     PaginatedDetectorList,
@@ -188,7 +189,7 @@ def _fixup_image_query(iq: ImageQuery) -> ImageQuery:
         # Note: This might go away once we clean up the mapping logic server-side.
 
         # we have to check that result is not None because the server will return a result of None if want_async=True
-        if iq.result is not None:
+        if isinstance(iq.result, BinaryClassificationResult):
             iq.result.label = convert_internal_label_to_display(iq, iq.result.label)
         return iq
 
@@ -245,15 +246,15 @@ def list_detectors(self, page: int = 1, page_size: int = 10) -> PaginatedDetecto
         return PaginatedDetectorList.parse_obj(obj.to_dict())
 
     def _prep_create_detector(
-            self,
-            name: str,
-            query: str,
-            *,
-            group_name: Optional[str] = None,
-            confidence_threshold: Optional[float] = None,
-            patience_time: Optional[float] = None,
-            pipeline_config: Optional[str] = None,
-            metadata: Union[dict, str, None] = None,
+        self,
+        name: str,
+        query: str,
+        *,
+        group_name: Optional[str] = None,
+        confidence_threshold: Optional[float] = None,
+        patience_time: Optional[float] = None,
+        pipeline_config: Optional[str] = None,
+        metadata: Union[dict, str, None] = None,
     ) -> Detector:
         """
         A helper function to prepare the input for creating a detector. Individual create_detector

src/groundlight/experimental_api.py

@@ -20,7 +20,7 @@
 from groundlight_openapi_client.model.b_box_geometry_request import BBoxGeometryRequest
 from groundlight_openapi_client.model.channel_enum import ChannelEnum
 from groundlight_openapi_client.model.condition_request import ConditionRequest
-from groundlight_openapi_client.model.count_mode_configuration_serializer import CountModeConfigurationSerializer
+from groundlight_openapi_client.model.count_mode_configuration import CountModeConfiguration
 from groundlight_openapi_client.model.detector_group_request import DetectorGroupRequest
 from groundlight_openapi_client.model.label_value_request import LabelValueRequest
 from groundlight_openapi_client.model.roi_request import ROIRequest
@@ -333,7 +333,10 @@ def create_counting_detector(
             metadata=metadata,
         )
         detector_creation_input.mode = ModeEnum.COUNT
-        mode_config = CountModeConfigurationSerializer(max_count=max_count)
+        # TODO: pull the BE defined default
+        if max_count is None:
+            max_count = 10
+        mode_config = CountModeConfiguration(max_count=max_count)
         detector_creation_input.mode_configuration = mode_config
         obj = self.detectors_api.create_detector(detector_creation_input, _request_timeout=DEFAULT_REQUEST_TIMEOUT)
-        return Detector.parse_obj(obj.to_dict())
+        return Detector.parse_obj(obj.to_dict())

test/unit/test_experimental.py

@@ -44,6 +44,7 @@ def test_submit_multiple_rois(gl_experimental: ExperimentalApi, image_query_no:
     roi = gl_experimental.create_roi(label_name, (0, 0), (0.5, 0.5))
     gl_experimental.add_label(image_query_no, "YES", [roi] * 3)
 
+
 def test_counting_detector(gl_experimental: ExperimentalApi):
     """
     verify that we can create and submit to a counting detector
@@ -52,4 +53,4 @@ def test_counting_detector(gl_experimental: ExperimentalApi):
     created_detector = gl_experimental.create_counting_detector(name, "How many dogs")
     assert created_detector is not None
     count_iq = gl_experimental.submit_image_query(created_detector, "test/assets/dog.jpeg")
-    assert count_iq.count is not None
+    assert count_iq.result.count is not None