temporary fix for how iqs can be returned

src/groundlight/internalapi.py

@@ -65,7 +65,7 @@ def iq_is_confident(iq: ImageQuery, confidence_threshold: float) -> bool:
     The only subtlety here is that currently confidence of None means
     human label, which is treated as confident.
     """
-    if not iq.result:
+    if not iq.result or not iq.result.confidence:
         return False
     return iq.result.confidence >= confidence_threshold  # type: ignore
 
@@ -74,7 +74,7 @@ def iq_is_answered(iq: ImageQuery) -> bool:
     """Returns True if the image query has a ML or human label.
     Placeholder and special labels (out of domain) have confidences exactly 0.5
     """
-    if not iq.result:
+    if not iq.result or not iq.result.source:
         return False
     if (iq.result.source == Source.STILL_PROCESSING) or (iq.result.source is None):  # Should never be None
         return False

test/unit/conftest.py

@@ -2,7 +2,7 @@
 
 import pytest
 from groundlight import ExperimentalApi, Groundlight
-from model import Detector, ImageQuery
+from model import Detector, ImageQuery, ImageQueryTypeEnum, ResultTypeEnum
 
 
 def pytest_configure(config):
@@ -46,3 +46,21 @@ def fixture_image_query_no(gl: Groundlight, detector: Detector) -> ImageQuery:
 @pytest.fixture(name="gl_experimental")
 def _gl() -> ExperimentalApi:
     return ExperimentalApi()
+
+
+@pytest.fixture(name="initial_iq")
+def fixture_initial_iq() -> ImageQuery:
+    return ImageQuery(
+        metadata=None,
+        id="iq_fakeidhere",
+        type=ImageQueryTypeEnum.image_query,
+        created_at=datetime.utcnow(),
+        query="Is there a dog?",
+        detector_id="det_fakeidhere",
+        result_type=ResultTypeEnum.binary_classification,
+        result=None,
+        patience_time=30,
+        confidence_threshold=0.9,
+        rois=None,
+        text=None,
+    )

test/unit/test_internalapi.py

@@ -1,14 +1,19 @@
 from groundlight import ExperimentalApi
 from groundlight.internalapi import iq_is_answered, iq_is_confident
+from model import ImageQuery
 
 
-def test_iq_is_confident(gl_experimental: ExperimentalApi):
+def test_iq_is_confident(gl_experimental: ExperimentalApi, initial_iq: ImageQuery):
     det = gl_experimental.get_or_create_detector("Test", "test_query")
     iq = gl_experimental.ask_async(det, image="test/assets/dog.jpeg")
     assert not iq_is_confident(iq, 0.9)
 
+    assert not iq_is_confident(initial_iq, 0.9)
 
-def test_iq_is_answered(gl_experimental: ExperimentalApi):
+
+def test_iq_is_answered(gl_experimental: ExperimentalApi, initial_iq: ImageQuery):
     det = gl_experimental.get_or_create_detector("Test", "test_query")
     iq = gl_experimental.ask_async(det, image="test/assets/dog.jpeg")
     assert not iq_is_answered(iq)
+
+    assert not iq_is_answered(initial_iq)