Switch Human Review Query Parameter from Boolean to String

generated/docs/ImageQueriesApi.md

@@ -205,7 +205,7 @@ with openapi_client.ApiClient(configuration) as api_client:
     # Create an instance of the API class
     api_instance = image_queries_api.ImageQueriesApi(api_client)
     detector_id = "detector_id_example" # str | Choose a detector by its ID.
-    human_review = True # bool | Allow image queries to be marked for no human review. (optional)
+    human_review = "human_review_example" # str | If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review if the ML model is not confident). If set to `ALWAYS`, always send the image query for human review even if the ML model is confident. If set to `NEVER`, never send the image query for human review even if the ML model is not confident.  (optional)
     patience_time = 3.14 # float | How long to wait for a confident response. (optional)
     body = open('@path/to/image.jpeg', 'rb') # file_type |  (optional)
 
@@ -231,7 +231,7 @@ with openapi_client.ApiClient(configuration) as api_client:
 Name | Type | Description  | Notes
 ------------- | ------------- | ------------- | -------------
  **detector_id** | **str**| Choose a detector by its ID. |
- **human_review** | **bool**| Allow image queries to be marked for no human review. | [optional]
+ **human_review** | **str**| If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review if the ML model is not confident). If set to `ALWAYS`, always send the image query for human review even if the ML model is confident. If set to `NEVER`, never send the image query for human review even if the ML model is not confident.  | [optional]
  **patience_time** | **float**| How long to wait for a confident response. | [optional]
  **body** | **file_type**|  | [optional]
 

generated/model.py

@@ -1,6 +1,6 @@
 # generated by datamodel-codegen:
 #   filename:  public-api.yaml
-#   timestamp: 2023-08-01T00:09:22+00:00
+#   timestamp: 2023-08-09T20:46:11+00:00
 
 from __future__ import annotations
 

generated/openapi_client/api/image_queries_api.py

@@ -147,7 +147,7 @@ def __init__(self, api_client=None):
                 "allowed_values": {},
                 "openapi_types": {
                     "detector_id": (str,),
-                    "human_review": (bool,),
+                    "human_review": (str,),
                     "patience_time": (float,),
                     "body": (file_type,),
                 },
@@ -297,7 +297,7 @@ def submit_image_query(self, detector_id, **kwargs):
             detector_id (str): Choose a detector by its ID.
 
         Keyword Args:
-            human_review (bool): If set to `False` then unconfident ML predictions will not be escalated to human review.  [optional, defaults `True`]
+            human_review (str): If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review if the ML model is not confident). If set to `ALWAYS`, always send the image query for human review even if the ML model is confident. If set to `NEVER`, never send the image query for human review even if the ML model is not confident. . [optional]
             patience_time (float): How long to wait for a confident response.. [optional]
             body (file_type): [optional]
             _return_http_data_only (bool): response data without head status

spec/public-api.yaml

@@ -135,8 +135,11 @@ paths:
       - in: query 
         name: human_review 
         schema: 
-          type: boolean 
-        description: If set to `False` then unconfident ML predictions will not be escalated to human review.  (Defaults `True`) 
+          type: string 
+        description: > 
+          If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review if the ML model is not confident).
+          If set to `ALWAYS`, always send the image query for human review even if the ML model is confident.
+          If set to `NEVER`, never send the image query for human review even if the ML model is not confident.
         required: false 
       - in: query 
         name: patience_time 

src/groundlight/client.py

@@ -170,7 +170,7 @@ def submit_image_query(
         detector: Union[Detector, str],
         image: Union[str, bytes, Image.Image, BytesIO, BufferedReader, np.ndarray],
         wait: Optional[float] = None,
-        human_review: Optional[bool] = True,
+        human_review: Optional[str] = None,
     ) -> ImageQuery:
         """Evaluates an image with Groundlight.
         :param detector: the Detector object, or string id of a detector like `det_12345`
@@ -183,17 +183,27 @@ def submit_image_query(
           Any binary format must be JPEG-encoded already.  Any pixel format will get
           converted to JPEG at high quality before sending to service.
         :param wait: How long to wait (in seconds) for a confident answer.
-        :param human_review: If set to False, do not escalate for human review
+        :param human_review: If `None` or `DEFAULT`, send the image query for human review
+            only if the ML prediction is not confident.
+            If set to `ALWAYS`, always send the image query for human review.
+            If set to `NEVER`, never send the image query for human review.
         """
         if wait is None:
             wait = self.DEFAULT_WAIT
         detector_id = detector.id if isinstance(detector, Detector) else detector
 
         image_bytesio: ByteStreamWrapper = parse_supported_image_types(image)
 
-        raw_image_query = self.image_queries_api.submit_image_query(
-            detector_id=detector_id, patience_time=wait, human_review=human_review, body=image_bytesio
-        )
+        params = {"detector_id": detector_id, "body": image_bytesio}
+        if wait == 0:
+            params["patience_time"] = self.DEFAULT_WAIT
+        else:
+            params["patience_time"] = wait
+
+        if human_review is not None:
+            params["human_review"] = human_review
+
+        raw_image_query = self.image_queries_api.submit_image_query(**params)
         image_query = ImageQuery.parse_obj(raw_image_query.to_dict())
         if wait:
             threshold = self.get_detector(detector).confidence_threshold

test/integration/test_groundlight.py

@@ -35,7 +35,7 @@ def is_valid_display_label(label: str) -> bool:
 def fixture_gl() -> Groundlight:
     """Creates a Groundlight client object for testing."""
     _gl = Groundlight()
-    _gl.DEFAULT_WAIT = 0.1
+    _gl.DEFAULT_WAIT = 10
     return _gl
 
 
@@ -163,8 +163,7 @@ def test_get_detector_by_name(gl: Groundlight, detector: Detector):
 
 
 def test_submit_image_query_blocking(gl: Groundlight, detector: Detector):
-    # Ask for a trivially small wait so it never has time to update, but uses the code path
-    _image_query = gl.submit_image_query(detector=detector.id, image="test/assets/dog.jpeg", wait=2)
+    _image_query = gl.submit_image_query(detector=detector.id, image="test/assets/dog.jpeg", wait=10)
     assert str(_image_query)
     assert isinstance(_image_query, ImageQuery)
     assert is_valid_display_result(_image_query.result)
@@ -173,7 +172,7 @@ def test_submit_image_query_blocking(gl: Groundlight, detector: Detector):
 def test_submit_image_query_returns_yes(gl: Groundlight):
     # We use the "never-review" pipeline to guarantee a confident "yes" answer.
     detector = gl.get_or_create_detector(name="Always a dog", query="Is there a dog?", pipeline_config="never-review")
-    image_query = gl.submit_image_query(detector=detector, image="test/assets/dog.jpeg", wait=2)
+    image_query = gl.submit_image_query(detector=detector, image="test/assets/dog.jpeg", wait=10)
     assert image_query.result.label == Label.YES
 
 
@@ -184,6 +183,17 @@ def test_submit_image_query_filename(gl: Groundlight, detector: Detector):
     assert is_valid_display_result(_image_query.result)
 
 
+def test_submit_image_query_with_human_review_param(gl: Groundlight, detector: Detector):
+    # For now, this just tests that the image query is submitted successfully.
+    # There should probably be a better way to check whether the image query was escalated for human review.
+
+    for human_review_value in ("DEFAULT", "ALWAYS", "NEVER"):
+        _image_query = gl.submit_image_query(
+            detector=detector.id, image="test/assets/dog.jpeg", human_review=human_review_value
+        )
+        assert is_valid_display_result(_image_query.result)
+
+
 def test_submit_image_query_jpeg_bytes(gl: Groundlight, detector: Detector):
     jpeg = open("test/assets/dog.jpeg", "rb").read()
     _image_query = gl.submit_image_query(detector=detector.id, image=jpeg)