@@ -1573,47 +1573,6 @@ def create_binary_detector( # noqa: PLR0913 # pylint: disable=too-many-argument
15731573 obj = self .detectors_api .create_detector (detector_creation_input , _request_timeout = DEFAULT_REQUEST_TIMEOUT )
15741574 return obj
15751575
1576- def create_binary_detector ( # noqa: PLR0913 # pylint: disable=too-many-arguments, too-many-locals
1577- self ,
1578- name : str ,
1579- query : str ,
1580- * ,
1581- group_name : Optional [str ] = None ,
1582- confidence_threshold : Optional [float ] = None ,
1583- patience_time : Optional [float ] = None ,
1584- pipeline_config : Optional [str ] = None ,
1585- metadata : Union [dict , str , None ] = None ,
1586- ) -> Detector :
1587- """
1588- Creates a binary detector with the given name and query.
1589-
1590- **Example usage**::
1591-
1592- gl = Groundlight()
1593-
1594- # Create a binary detector for a door
1595- detector = gl.create_binary_detector(
1596- name="door_detector",
1597- query="Is there a door in the image?",
1598- confidence_threshold=0.9,
1599- patience_time=30.0
1600- )
1601-
1602- # Use the detector to classify a door
1603- image_query = gl.ask_ml(detector, "path/to/image.jpg")
1604- """
1605- detector_creation_input = self ._prep_create_detector (
1606- name = name ,
1607- query = query ,
1608- group_name = group_name ,
1609- confidence_threshold = confidence_threshold ,
1610- patience_time = patience_time ,
1611- pipeline_config = pipeline_config ,
1612- metadata = metadata ,
1613- )
1614- obj = self .detectors_api .create_detector (detector_creation_input , _request_timeout = DEFAULT_REQUEST_TIMEOUT )
1615- return Detector .parse_obj (obj .to_dict ())
1616-
16171576 def create_multiclass_detector ( # noqa: PLR0913 # pylint: disable=too-many-arguments, too-many-locals
16181577 self ,
16191578 name : str ,
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