Merge branch 'main' into list_iqs_filter_by_det_id

.pylintrc

@@ -87,10 +87,10 @@ disable=raw-checker-failed,
         missing-class-docstring,
         missing-function-docstring,
         invalid-name,
-        too-few-public-methods,
         line-too-long,
         import-error, # we only install linter dependencies in CI/CD
         wrong-import-order, # we use ruff to enforce import order
+        duplicate-code, # pylint has a tendancy to capture docstrings as duplicate code
 
 
 # Enable the message, report, category or checker with the given id(s). You can

generated/model.py

@@ -1,6 +1,6 @@
 # generated by datamodel-codegen:
 #   filename:  public-api.yaml
-#   timestamp: 2024-12-13T20:04:02+00:00
+#   timestamp: 2024-12-13T20:10:31+00:00
 
 from __future__ import annotations
 

pyproject.toml

@@ -9,7 +9,7 @@ packages = [
     {include = "**/*.py", from = "src"},
 ]
 readme = "README.md"
-version = "0.21.0"
+version = "0.21.1"
 
 [tool.poetry.dependencies]
 # For certifi, use ">=" instead of "^" since it upgrades its "major version" every year, not really following semver

src/groundlight/binary_labels.py

@@ -53,25 +53,3 @@ def convert_internal_label_to_display(
 
     logger.warning(f"Unrecognized internal label {label} - leaving it alone as a string.")
     return label
-
-
-def convert_display_label_to_internal(
-    context: Union[ImageQuery, Detector, str],  # pylint: disable=unused-argument
-    label: Union[Label, str],
-) -> str:
-    """Convert a label that comes from the user into the label string that we send to the server. We
-    are strict here, and only allow YES/NO.
-
-    NOTE: We accept case-insensitive label strings from the user, but we send UPPERCASE labels to
-    the server. E.g., user inputs "yes" -> the label is returned as "YES".
-    """
-    # NOTE: In the future we should validate against actually supported labels for the detector
-    if not isinstance(label, str):
-        raise ValueError(f"Expected a string label, but got {label} of type {type(label)}")
-    upper = label.upper()
-    if upper == Label.YES:
-        return DeprecatedLabel.PASS.value
-    if upper == Label.NO:
-        return DeprecatedLabel.FAIL.value
-
-    raise ValueError(f"Invalid label string '{label}'.  Must be one of '{Label.YES.value}','{Label.NO.value}'.")

src/groundlight/client.py

@@ -13,9 +13,11 @@
 from groundlight_openapi_client.api.labels_api import LabelsApi
 from groundlight_openapi_client.api.user_api import UserApi
 from groundlight_openapi_client.exceptions import NotFoundException, UnauthorizedException
+from groundlight_openapi_client.model.b_box_geometry_request import BBoxGeometryRequest
 from groundlight_openapi_client.model.detector_creation_input_request import DetectorCreationInputRequest
 from groundlight_openapi_client.model.label_value_request import LabelValueRequest
 from groundlight_openapi_client.model.patched_detector_request import PatchedDetectorRequest
+from groundlight_openapi_client.model.roi_request import ROIRequest
 from model import (
     ROI,
     BinaryClassificationResult,
@@ -26,7 +28,7 @@
 )
 from urllib3.exceptions import InsecureRequestWarning
 
-from groundlight.binary_labels import Label, convert_display_label_to_internal, convert_internal_label_to_display
+from groundlight.binary_labels import Label, convert_internal_label_to_display
 from groundlight.config import API_TOKEN_MISSING_HELP_MESSAGE, API_TOKEN_VARIABLE_NAME, DISABLE_TLS_VARIABLE_NAME
 from groundlight.encodings import url_encode_dict
 from groundlight.images import ByteStreamWrapper, parse_supported_image_types
@@ -1073,7 +1075,10 @@ def _wait_for_result(
         return image_query
 
     def add_label(
-        self, image_query: Union[ImageQuery, str], label: Union[Label, str], rois: Union[List[ROI], str, None] = None
+        self,
+        image_query: Union[ImageQuery, str],
+        label: Union[Label, int, str],
+        rois: Union[List[ROI], str, None] = None,
     ):
         """
         Provide a new label (annotation) for an image query. This is used to provide ground-truth labels
@@ -1094,27 +1099,45 @@ def add_label(
             rois = [ROI(x=100, y=100, width=50, height=50)]
             gl.add_label(image_query, "YES", rois=rois)
 
-        :param image_query: Either an ImageQuery object (returned from methods like :meth:`ask_ml`) or an image query ID
-            string starting with "iq_".
-        :param label: The label value to assign, typically "YES" or "NO" for binary classification detectors.
-            For multi-class detectors, use one of the defined class names.
-        :param rois: Optional list of ROI objects defining regions of interest in the image.
-            Each ROI specifies a bounding box with x, y coordinates and width, height.
+        :param image_query: Either an ImageQuery object (returned from methods like
+                          `ask_ml`) or an image query ID string starting with "iq_".
+
+        :param label: The label value to assign, typically "YES" or "NO" for binary
+                     classification detectors. For multi-class detectors, use one of
+                     the defined class names.
+
+        :param rois: Optional list of ROI objects defining regions of interest in the
+                    image. Each ROI specifies a bounding box with x, y coordinates
+                    and width, height.
 
         :return: None
         """
         if isinstance(rois, str):
             raise TypeError("rois must be a list of ROI objects. CLI support is not implemented")
+
+        # NOTE: bool is a subclass of int
+        if type(label) == int:  # noqa: E721 pylint: disable=unidiomatic-typecheck
+            label = str(label)
+        elif not isinstance(label, (str, Label)):
+            raise TypeError("label must be a string or integer")
+
         if isinstance(image_query, ImageQuery):
             image_query_id = image_query.id
         else:
             image_query_id = str(image_query)
             # Some old imagequery id's started with "chk_"
             if not image_query_id.startswith(("chk_", "iq_")):
                 raise ValueError(f"Invalid image query id {image_query_id}")
-        api_label = convert_display_label_to_internal(image_query_id, label)
-        rois_json = [roi.dict() for roi in rois] if rois else None
-        request_params = LabelValueRequest(label=api_label, image_query_id=image_query_id, rois=rois_json)
+        geometry_requests = [BBoxGeometryRequest(**roi.geometry.dict()) for roi in rois] if rois else None
+        roi_requests = (
+            [
+                ROIRequest(label=roi.label, score=roi.score, geometry=geometry)
+                for roi, geometry in zip(rois, geometry_requests)
+            ]
+            if rois and geometry_requests
+            else None
+        )
+        request_params = LabelValueRequest(label=label, image_query_id=image_query_id, rois=roi_requests)
         self.labels_api.create_label(request_params)
 
     def start_inspection(self) -> str:

src/groundlight/experimental_api.py

@@ -17,22 +17,18 @@
 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
-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 import CountModeConfiguration
 from groundlight_openapi_client.model.detector_group_request import DetectorGroupRequest
 from groundlight_openapi_client.model.escalation_type_enum import EscalationTypeEnum
-from groundlight_openapi_client.model.label_value_request import LabelValueRequest
 from groundlight_openapi_client.model.multi_class_mode_configuration import MultiClassModeConfiguration
 from groundlight_openapi_client.model.patched_detector_request import PatchedDetectorRequest
-from groundlight_openapi_client.model.roi_request import ROIRequest
 from groundlight_openapi_client.model.rule_request import RuleRequest
 from groundlight_openapi_client.model.status_enum import StatusEnum
 from groundlight_openapi_client.model.verb_enum import VerbEnum
-from model import ROI, BBoxGeometry, Detector, DetectorGroup, ImageQuery, ModeEnum, PaginatedRuleList, Rule
+from model import ROI, BBoxGeometry, Detector, DetectorGroup, ModeEnum, PaginatedRuleList, Rule
 
-from groundlight.binary_labels import Label, convert_display_label_to_internal
 from groundlight.images import parse_supported_image_types
 from groundlight.optional_imports import Image, np
 
@@ -499,66 +495,6 @@ def create_roi(self, label: str, top_left: Tuple[float, float], bottom_right: Tu
             ),
         )
 
-    # TODO: remove duplicate method on subclass
-    # pylint: disable=duplicate-code
-    def add_label(
-        self, image_query: Union[ImageQuery, str], label: Union[Label, str], rois: Union[List[ROI], str, None] = None
-    ):
-        """
-        Provide a new label (annotation) for an image query. This is used to provide ground-truth labels
-        for training detectors, or to correct the results of detectors.
-
-        **Example usage**::
-
-            gl = ExperimentalApi()
-
-            # Using an ImageQuery object
-            image_query = gl.ask_ml(detector_id, image_data)
-            gl.add_label(image_query, "YES")
-
-            # Using an image query ID string directly
-            gl.add_label("iq_abc123", "NO")
-
-            # With regions of interest (ROIs)
-            rois = [ROI(x=100, y=100, width=50, height=50)]
-            gl.add_label(image_query, "YES", rois=rois)
-
-        :param image_query: Either an ImageQuery object (returned from methods like
-                          `ask_ml`) or an image query ID string starting with "iq_".
-
-        :param label: The label value to assign, typically "YES" or "NO" for binary
-                     classification detectors. For multi-class detectors, use one of
-                     the defined class names.
-
-        :param rois: Optional list of ROI objects defining regions of interest in the
-                    image. Each ROI specifies a bounding box with x, y coordinates
-                    and width, height.
-
-        :return: None
-        """
-        if isinstance(rois, str):
-            raise TypeError("rois must be a list of ROI objects. CLI support is not implemented")
-        if isinstance(image_query, ImageQuery):
-            image_query_id = image_query.id
-        else:
-            image_query_id = str(image_query)
-            # Some old imagequery id's started with "chk_"
-            # TODO: handle iqe_ for image_queries returned from edge endpoints
-            if not image_query_id.startswith(("chk_", "iq_")):
-                raise ValueError(f"Invalid image query id {image_query_id}")
-        api_label = convert_display_label_to_internal(image_query_id, label)
-        geometry_requests = [BBoxGeometryRequest(**roi.geometry.dict()) for roi in rois] if rois else None
-        roi_requests = (
-            [
-                ROIRequest(label=roi.label, score=roi.score, geometry=geometry)
-                for roi, geometry in zip(rois, geometry_requests)
-            ]
-            if rois and geometry_requests
-            else None
-        )
-        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 and training data for the given detector. This effectively resets

src/groundlight/internalapi.py

@@ -65,13 +65,17 @@ 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:
+        return False
     return iq.result.confidence >= confidence_threshold  # type: ignore
 
 
 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:
+        return False
     if (iq.result.source == Source.STILL_PROCESSING) or (iq.result.source is None):  # Should never be None
         return False
     return True
@@ -87,7 +91,7 @@ def __init__(self, status=None, reason=None, http_resp=None):
         super().__init__(status, reason, http_resp)
 
 
-class RequestsRetryDecorator:
+class RequestsRetryDecorator:  # pylint: disable=too-few-public-methods
     """
     Decorate a function to retry sending HTTP requests.
 

test/integration/test_groundlight.py

@@ -10,7 +10,7 @@
 
 import pytest
 from groundlight import Groundlight
-from groundlight.binary_labels import VALID_DISPLAY_LABELS, DeprecatedLabel, Label, convert_internal_label_to_display
+from groundlight.binary_labels import VALID_DISPLAY_LABELS, Label, convert_internal_label_to_display
 from groundlight.internalapi import ApiException, InternalApiError, NotFoundError
 from groundlight.optional_imports import *
 from groundlight.status_codes import is_user_error
@@ -20,6 +20,7 @@
     CountingResult,
     Detector,
     ImageQuery,
+    MultiClassificationResult,
     PaginatedDetectorList,
     PaginatedImageQueryList,
 )
@@ -30,7 +31,11 @@
 
 def is_valid_display_result(result: Any) -> bool:
     """Is the image query result valid to display to the user?."""
-    if not isinstance(result, BinaryClassificationResult) and not isinstance(result, CountingResult):
+    if (
+        not isinstance(result, BinaryClassificationResult)
+        and not isinstance(result, CountingResult)
+        and not isinstance(result, MultiClassificationResult)
+    ):
         return False
     if not is_valid_display_label(result.label):
         return False
@@ -636,44 +641,15 @@ def test_add_label_names(gl: Groundlight, image_query_yes: ImageQuery, image_que
     gl.add_label(iqid_no, "NO")
     gl.add_label(iqid_no, "no")
 
-    # Invalid labels
-    with pytest.raises(ValueError):
-        gl.add_label(iqid_yes, "PASS")
-    with pytest.raises(ValueError):
-        gl.add_label(iqid_yes, "FAIL")
-    with pytest.raises(ValueError):
-        gl.add_label(iqid_yes, DeprecatedLabel.PASS)
-    with pytest.raises(ValueError):
-        gl.add_label(iqid_yes, DeprecatedLabel.FAIL)
-    with pytest.raises(ValueError):
-        gl.add_label(iqid_yes, "sorta")
-    with pytest.raises(ValueError):
-        gl.add_label(iqid_yes, "YES ")
-    with pytest.raises(ValueError):
-        gl.add_label(iqid_yes, " YES")
-    with pytest.raises(ValueError):
-        gl.add_label(iqid_yes, "0")
-    with pytest.raises(ValueError):
-        gl.add_label(iqid_yes, "1")
-
-    # We technically don't allow these in the type signature, but users might do it anyway
-    with pytest.raises(ValueError):
-        gl.add_label(iqid_yes, 0)  # type: ignore
-    with pytest.raises(ValueError):
-        gl.add_label(iqid_yes, 1)  # type: ignore
-    with pytest.raises(ValueError):
+    with pytest.raises(TypeError):
         gl.add_label(iqid_yes, None)  # type: ignore
-    with pytest.raises(ValueError):
+    with pytest.raises(TypeError):
         gl.add_label(iqid_yes, True)  # type: ignore
-    with pytest.raises(ValueError):
+    with pytest.raises(TypeError):
         gl.add_label(iqid_yes, False)  # type: ignore
-    with pytest.raises(ValueError):
+    with pytest.raises(TypeError):
         gl.add_label(iqid_yes, b"YES")  # type: ignore
 
-    # We may want to support something like this in the future, but not yet
-    with pytest.raises(ValueError):
-        gl.add_label(iqid_yes, Label.UNCLEAR)
-
 
 def test_label_conversion_produces_strings():
     # In our code, it's easier to work with enums, but we allow users to pass in strings or enums

test/unit/test_internalapi.py

@@ -0,0 +1,14 @@
+from groundlight import ExperimentalApi
+from groundlight.internalapi import iq_is_answered, iq_is_confident
+
+
+def test_iq_is_confident(gl_experimental: ExperimentalApi):
+    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)
+
+
+def test_iq_is_answered(gl_experimental: ExperimentalApi):
+    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)