Merge branch 'main' into update_detector

generated/.openapi-generator/FILES

@@ -112,6 +112,4 @@ setup.cfg
 setup.py
 test-requirements.txt
 test/__init__.py
-test/test_detector_reset_api.py
-test/test_patched_detector_request.py
 tox.ini

generated/docs/ImageQuery.md

@@ -16,6 +16,7 @@ Name | Type | Description | Notes
 **patience_time** | **float** | How long to wait for a confident response. | [readonly] 
 **confidence_threshold** | **float** | Min confidence needed to accept the response of the image query. | [readonly] 
 **rois** | [**[ROI], none_type**](ROI.md) | An array of regions of interest (bounding boxes) collected on image | [readonly] 
+**text** | **str, none_type** | A text field on image query. | [readonly] 
 **any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional]
 
 [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)

generated/docs/LabelValue.md

@@ -10,6 +10,7 @@ Name | Type | Description | Notes
 **created_at** | **datetime** |  | [readonly] 
 **detector_id** | **int, none_type** |  | [readonly] 
 **source** | **bool, date, datetime, dict, float, int, list, str, none_type** |  | [readonly] 
+**text** | **str, none_type** | Text annotations | [readonly] 
 **rois** | [**[ROI], none_type**](ROI.md) |  | [optional] 
 **any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional]
 

generated/docs/ResultTypeEnum.md

@@ -4,7 +4,7 @@
 ## Properties
 Name | Type | Description | Notes
 ------------ | ------------- | ------------- | -------------
-**value** | **str** |  |  must be one of ["binary_classification", "counting", ]
+**value** | **str** |  |  must be one of ["binary_classification", "counting", "multi_classification", ]
 
 [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)
 

generated/groundlight_openapi_client/model/image_query.py

@@ -151,6 +151,10 @@ def openapi_types():
                 [ROI],
                 none_type,
             ),  # noqa: E501
+            "text": (
+                str,
+                none_type,
+            ),  # noqa: E501
         }
 
     @cached_property
@@ -169,6 +173,7 @@ def discriminator():
         "patience_time": "patience_time",  # noqa: E501
         "confidence_threshold": "confidence_threshold",  # noqa: E501
         "rois": "rois",  # noqa: E501
+        "text": "text",  # noqa: E501
     }
 
     read_only_vars = {
@@ -183,6 +188,7 @@ def discriminator():
         "patience_time",  # noqa: E501
         "confidence_threshold",  # noqa: E501
         "rois",  # noqa: E501
+        "text",  # noqa: E501
     }
 
     _composed_schemas = {}
@@ -202,6 +208,7 @@ def _from_openapi_data(
         patience_time,
         confidence_threshold,
         rois,
+        text,
         *args,
         **kwargs,
     ):  # noqa: E501
@@ -219,6 +226,7 @@ def _from_openapi_data(
             patience_time (float): How long to wait for a confident response.
             confidence_threshold (float): Min confidence needed to accept the response of the image query.
             rois ([ROI], none_type): An array of regions of interest (bounding boxes) collected on image
+            text (str, none_type): A text field on image query.
 
         Keyword Args:
             _check_type (bool): if True, values for parameters in openapi_types
@@ -290,6 +298,7 @@ def _from_openapi_data(
         self.patience_time = patience_time
         self.confidence_threshold = confidence_threshold
         self.rois = rois
+        self.text = text
         for var_name, var_value in kwargs.items():
             if (
                 var_name not in self.attribute_map

generated/groundlight_openapi_client/model/label_value.py

@@ -125,6 +125,10 @@ def openapi_types():
                 str,
                 none_type,
             ),  # noqa: E501
+            "text": (
+                str,
+                none_type,
+            ),  # noqa: E501
             "rois": (
                 [ROI],
                 none_type,
@@ -142,6 +146,7 @@ def discriminator():
         "created_at": "created_at",  # noqa: E501
         "detector_id": "detector_id",  # noqa: E501
         "source": "source",  # noqa: E501
+        "text": "text",  # noqa: E501
         "rois": "rois",  # noqa: E501
     }
 
@@ -152,14 +157,15 @@ def discriminator():
         "created_at",  # noqa: E501
         "detector_id",  # noqa: E501
         "source",  # noqa: E501
+        "text",  # noqa: E501
     }
 
     _composed_schemas = {}
 
     @classmethod
     @convert_js_args_to_python_args
     def _from_openapi_data(
-        cls, confidence, class_name, annotations_requested, created_at, detector_id, source, *args, **kwargs
+        cls, confidence, class_name, annotations_requested, created_at, detector_id, source, text, *args, **kwargs
     ):  # noqa: E501
         """LabelValue - a model defined in OpenAPI
 
@@ -170,6 +176,7 @@ def _from_openapi_data(
             created_at (datetime):
             detector_id (int, none_type):
             source (bool, date, datetime, dict, float, int, list, str, none_type):
+            text (str, none_type): Text annotations
 
         Keyword Args:
             _check_type (bool): if True, values for parameters in openapi_types
@@ -237,6 +244,7 @@ def _from_openapi_data(
         self.created_at = created_at
         self.detector_id = detector_id
         self.source = source
+        self.text = text
         for var_name, var_value in kwargs.items():
             if (
                 var_name not in self.attribute_map

generated/groundlight_openapi_client/model/result_type_enum.py

@@ -53,6 +53,7 @@ class ResultTypeEnum(ModelSimple):
         ("value",): {
             "BINARY_CLASSIFICATION": "binary_classification",
             "COUNTING": "counting",
+            "MULTI_CLASSIFICATION": "multi_classification",
         },
     }
 
@@ -102,10 +103,10 @@ def __init__(self, *args, **kwargs):
         Note that value can be passed either in args or in kwargs, but not in both.
 
         Args:
-            args[0] (str):, must be one of ["binary_classification", "counting", ]  # noqa: E501
+            args[0] (str):, must be one of ["binary_classification", "counting", "multi_classification", ]  # noqa: E501
 
         Keyword Args:
-            value (str):, must be one of ["binary_classification", "counting", ]  # noqa: E501
+            value (str):, must be one of ["binary_classification", "counting", "multi_classification", ]  # noqa: E501
             _check_type (bool): if True, values for parameters in openapi_types
                                 will be type checked and a TypeError will be
                                 raised if the wrong type is input.
@@ -194,10 +195,10 @@ def _from_openapi_data(cls, *args, **kwargs):
         Note that value can be passed either in args or in kwargs, but not in both.
 
         Args:
-            args[0] (str):, must be one of ["binary_classification", "counting", ]  # noqa: E501
+            args[0] (str):, must be one of ["binary_classification", "counting", "multi_classification", ]  # noqa: E501
 
         Keyword Args:
-            value (str):, must be one of ["binary_classification", "counting", ]  # noqa: E501
+            value (str):, must be one of ["binary_classification", "counting", "multi_classification", ]  # noqa: E501
             _check_type (bool): if True, values for parameters in openapi_types
                                 will be type checked and a TypeError will be
                                 raised if the wrong type is input.

generated/model.py

@@ -1,6 +1,6 @@
 # generated by datamodel-codegen:
 #   filename:  public-api.yaml
-#   timestamp: 2024-08-27T20:38:52+00:00
+#   timestamp: 2024-10-01T16:56:32+00:00
 
 from __future__ import annotations
 
@@ -124,6 +124,7 @@ class ROIRequest(BaseModel):
 class ResultTypeEnum(Enum):
     binary_classification = "binary_classification"
     counting = "counting"
+    multi_classification = "multi_classification"
 
 
 class SnoozeTimeUnitEnum(Enum):
@@ -322,6 +323,7 @@ class ImageQuery(BaseModel):
     rois: Optional[List[ROI]] = Field(
         ..., description="An array of regions of interest (bounding boxes) collected on image"
     )
+    text: Optional[str] = Field(..., description="A text field on image query.")
 
 
 class LabelValue(BaseModel):
@@ -334,6 +336,7 @@ class LabelValue(BaseModel):
     created_at: datetime
     detector_id: Optional[int] = Field(...)
     source: SourceEnum
+    text: Optional[str] = Field(..., description="Text annotations")
 
 
 class LabelValueRequest(BaseModel):

pyproject.toml

@@ -9,7 +9,7 @@ packages = [
     {include = "**/*.py", from = "src"},
 ]
 readme = "README.md"
-version = "0.17.4"
+version = "0.17.6"
 
 [tool.poetry.dependencies]
 # For certifi, use ">=" instead of "^" since it upgrades its "major version" every year, not really following semver

spec/public-api.yaml

@@ -931,6 +931,11 @@ components:
           nullable: true
           description: An array of regions of interest (bounding boxes) collected
             on image
+        text:
+          type: string
+          nullable: true
+          readOnly: true
+          description: A text field on image query.
       required:
       - confidence_threshold
       - created_at
@@ -942,6 +947,7 @@ components:
       - result
       - result_type
       - rois
+      - text
       - type
       x-internal: true
     ImageQueryTypeEnum:
@@ -989,13 +995,19 @@ components:
           allOf:
           - $ref: '#/components/schemas/SourceEnum'
           readOnly: true
+        text:
+          type: string
+          readOnly: true
+          nullable: true
+          description: Text annotations
       required:
       - annotations_requested
       - class_name
       - confidence
       - created_at
       - detector_id
       - source
+      - text
     LabelValueRequest:
       type: object
       properties:
@@ -1188,6 +1200,7 @@ components:
       enum:
       - binary_classification
       - counting
+      - multi_classification
       type: string
     Rule:
       type: object

src/groundlight/experimental_api.py

@@ -13,6 +13,7 @@
 import requests
 from groundlight_openapi_client.api.actions_api import ActionsApi
 from groundlight_openapi_client.api.detector_groups_api import DetectorGroupsApi
+from groundlight_openapi_client.api.detector_reset_api import DetectorResetApi
 from groundlight_openapi_client.api.image_queries_api import ImageQueriesApi
 from groundlight_openapi_client.api.notes_api import NotesApi
 from groundlight_openapi_client.model.action_request import ActionRequest
@@ -48,6 +49,7 @@ def __init__(self, endpoint: Union[str, None] = None, api_token: Union[str, None
         self.images_api = ImageQueriesApi(self.api_client)
         self.notes_api = NotesApi(self.api_client)
         self.detector_group_api = DetectorGroupsApi(self.api_client)
+        self.detector_reset_api = DetectorResetApi(self.api_client)
 
     ITEMS_PER_PAGE = 100
 
@@ -210,7 +212,7 @@ def create_note(
         data = {"content": note}
         params = {"detector_id": det_id}
         headers = {"x-api-token": self.configuration.api_key["ApiToken"]}
-        requests.post(url, headers=headers, data=data, files=files, params=params, timeout=60)  # type: ignore
+        requests.post(url, headers=headers, data=data, files=files, params=params)  # type: ignore
 
     def create_detector_group(self, name: str) -> DetectorGroup:
         """
@@ -297,6 +299,16 @@ def add_label(
         request_params = LabelValueRequest(label=api_label, image_query_id=image_query_id, rois=roi_requests)
         self.labels_api.create_label(request_params)
 
+    def reset_detector(self, detector: Union[str, Detector]) -> None:
+        """
+        Removes all image queries for the given detector
+
+        :param detector_id: the id of the detector to reset
+        """
+        if isinstance(detector, Detector):
+            detector = detector.id
+        self.detector_reset_api.reset_detector(detector)
+
     def update_detector_name(self, detector: Union[str, Detector], name: str) -> None:
         """
         Updates the name of the given detector

test/integration/test_groundlight.py

@@ -297,6 +297,15 @@ def test_submit_image_query_returns_yes(gl: Groundlight):
     assert image_query.result.label == Label.YES
 
 
+def test_submit_image_query_returns_text(gl: Groundlight):
+    # We use the "never-review" pipeline to guarantee a confident "yes" answer.
+    detector = gl.get_or_create_detector(
+        name="Always same text", query="Is there a dog?", pipeline_config="constant-text"
+    )
+    image_query = gl.submit_image_query(detector=detector, image="test/assets/dog.jpeg", wait=10, human_review="NEVER")
+    assert isinstance(image_query.text, str)
+
+
 def test_submit_image_query_filename(gl: Groundlight, detector: Detector):
     _image_query = gl.submit_image_query(detector=detector.id, image="test/assets/dog.jpeg", human_review="NEVER")
     assert str(_image_query)

test/unit/test_detector_reset.py

@@ -0,0 +1,34 @@
+import time
+from datetime import datetime
+
+import pytest
+from groundlight import ExperimentalApi
+from groundlight_openapi_client.exceptions import NotFoundException
+
+
+def test_reset_retry(gl_experimental: ExperimentalApi):
+    # Reset the detector, retrying in case the reset is still ongoing
+    det = gl_experimental.create_detector(f"Test {datetime.utcnow()}", "test_query")
+    iq = gl_experimental.submit_image_query(det, "test/assets/cat.jpeg")
+    gl_experimental.reset_detector(det.id)
+    success = False
+    for _ in range(60):
+        try:
+            gl_experimental.get_image_query(iq.id)
+        except NotFoundException:
+            with pytest.raises(NotFoundException):
+                gl_experimental.get_image_query(iq.id)
+            success = True
+            break
+        time.sleep(10)
+    if not success:
+        raise Exception("Failed to reset detector")
+
+
+def test_reset_training(gl_experimental: ExperimentalApi):
+    # If we reset a detector, we should have low confidence after the reset
+    low_confidence_threshold = 0.6
+    det = gl_experimental.create_detector(f"Test {datetime.utcnow()}", "is this a cat")
+    gl_experimental.reset_detector(det.id)
+    iq = gl_experimental.submit_image_query(det, "test/assets/cat.jpeg", human_review="NEVER")
+    assert iq.result.confidence < low_confidence_threshold