Generating files

generated/docs/ClassificationResult.md

@@ -7,6 +7,7 @@ Name | Type | Description | Notes
 ------------ | ------------- | ------------- | -------------
 **confidence** | **float** | On a scale of 0 to 1, how confident are we in the predicted label? | 
 **label** | **str** | What is the predicted label? | 
+**source** | **str** | Where did this classification result come from? | [optional] 
 **any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional]
 
 [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)

generated/docs/ImageQuery.md

@@ -12,6 +12,7 @@ Name | Type | Description | Notes
 **detector_id** | **str** | Which detector was used on this image query? | [readonly] 
 **result_type** | **bool, date, datetime, dict, float, int, list, str, none_type** | What type of result are we returning? | [readonly] 
 **result** | **bool, date, datetime, dict, float, int, list, str, none_type** |  | [optional] [readonly] 
+**confidence_threshold** | **float** |  | [optional] [readonly] 
 **metadata** | **{str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type** | A dictionary of custom key/value metadata to associate with the image query (limited to 1KB). | [optional] [readonly] 
 **any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional]
 

generated/groundlight_openapi_client/model/classification_result.py

@@ -96,6 +96,7 @@ def openapi_types():
         return {
             "confidence": (float,),  # noqa: E501
             "label": (str,),  # noqa: E501
+            "source": (str,),  # noqa: E501
         }
 
     @cached_property
@@ -105,6 +106,7 @@ def discriminator():
     attribute_map = {
         "confidence": "confidence",  # noqa: E501
         "label": "label",  # noqa: E501
+        "source": "source",  # noqa: E501
     }
 
     read_only_vars = {}
@@ -151,6 +153,7 @@ def _from_openapi_data(cls, confidence, label, *args, **kwargs):  # noqa: E501
                                 Animal class but this time we won't travel
                                 through its discriminator because we passed in
                                 _visited_composed_classes = (Animal,)
+            source (str): Where did this classification result come from?. [optional]  # noqa: E501
         """
 
         _check_type = kwargs.pop("_check_type", True)
@@ -243,6 +246,7 @@ def __init__(self, confidence, label, *args, **kwargs):  # noqa: E501
                                 Animal class but this time we won't travel
                                 through its discriminator because we passed in
                                 _visited_composed_classes = (Animal,)
+            source (str): Where did this classification result come from?. [optional]  # noqa: E501
         """
 
         _check_type = kwargs.pop("_check_type", True)

generated/groundlight_openapi_client/model/image_query.py

@@ -138,6 +138,7 @@ def openapi_types():
                 str,
                 none_type,
             ),  # noqa: E501
+            "confidence_threshold": (float,),  # noqa: E501
             "metadata": (
                 {str: (bool, date, datetime, dict, float, int, list, str, none_type)},
                 none_type,
@@ -156,6 +157,7 @@ def discriminator():
         "detector_id": "detector_id",  # noqa: E501
         "result_type": "result_type",  # noqa: E501
         "result": "result",  # noqa: E501
+        "confidence_threshold": "confidence_threshold",  # noqa: E501
         "metadata": "metadata",  # noqa: E501
     }
 
@@ -167,6 +169,7 @@ def discriminator():
         "detector_id",  # noqa: E501
         "result_type",  # noqa: E501
         "result",  # noqa: E501
+        "confidence_threshold",  # noqa: E501
         "metadata",  # noqa: E501
     }
 
@@ -217,6 +220,7 @@ def _from_openapi_data(cls, id, type, created_at, query, detector_id, result_typ
                                 through its discriminator because we passed in
                                 _visited_composed_classes = (Animal,)
             result (bool, date, datetime, dict, float, int, list, str, none_type): [optional]  # noqa: E501
+            confidence_threshold (float): [optional]  # noqa: E501
             metadata ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): A dictionary of custom key/value metadata to associate with the image query (limited to 1KB).. [optional]  # noqa: E501
         """
 
@@ -311,6 +315,7 @@ def __init__(self, *args, **kwargs):  # noqa: E501
                                 through its discriminator because we passed in
                                 _visited_composed_classes = (Animal,)
             result (bool, date, datetime, dict, float, int, list, str, none_type): [optional]  # noqa: E501
+            confidence_threshold (float): [optional]  # noqa: E501
             metadata ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): A dictionary of custom key/value metadata to associate with the image query (limited to 1KB).. [optional]  # noqa: E501
         """
 

generated/model.py

@@ -1,6 +1,6 @@
 # generated by datamodel-codegen:
 #   filename:  public-api.yaml
-#   timestamp: 2024-04-11T21:16:33+00:00
+#   timestamp: 2024-04-17T23:39:40+00:00
 
 from __future__ import annotations
 
@@ -20,6 +20,7 @@ class ClassificationResult(BaseModel):
         ..., description="On a scale of 0 to 1, how confident are we in the predicted label?"
     )
     label: str = Field(..., description="What is the predicted label?")
+    source: Optional[str] = Field(None, description="Where did this classification result come from?")
 
 
 class DetectorCreationInput(BaseModel):
@@ -155,6 +156,7 @@ class ImageQuery(BaseModel):
     detector_id: str = Field(..., description="Which detector was used on this image query?")
     result_type: ResultTypeEnum = Field(..., description="What type of result are we returning?")
     result: Optional[ClassificationResult] = None
+    confidence_threshold: Optional[float] = None
     metadata: Optional[Dict[str, Any]] = Field(
         None,
         description="A dictionary of custom key/value metadata to associate with the image query (limited to 1KB).",