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: str = "DEFAULT",
     ) -> ImageQuery:
         """Evaluates an image with Groundlight.
         :param detector: the Detector object, or string id of a detector like `det_12345`
@@ -183,7 +183,10 @@ 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 set to `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

test/integration/test_groundlight.py

@@ -184,6 +184,15 @@ def test_submit_image_query_filename(gl: Groundlight, detector: Detector):
     assert is_valid_display_result(_image_query.result)
 
 
+def test_submit_image_query_with_no_human_review(gl: Groundlight, detector: Detector):
+    # For now, this just tests that the image query is created successfully. There should probably be a better
+    # way to check that the image query is never escalated for human review.
+    _image_query = gl.submit_image_query(detector=detector.id, image="test/assets/dog.jpeg", human_review="NEVER")
+    assert str(_image_query)
+    assert isinstance(_image_query, ImageQuery)
+    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)