No longer allows null confidence values

generated/docs/ClassificationResult.md

@@ -6,7 +6,7 @@ Our classification result. This result can come from the detector, or a human re
 Name | Type | Description | Notes
 ------------ | ------------- | ------------- | -------------
 **label** | **str** | What is the predicted label? | 
-**confidence** | **float, none_type** | On a scale of 0 to 1, how confident are we in the predicted label? | [optional] 
+**confidence** | **float** | On a scale of 0 to 1, how confident are we in the predicted label? | [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/groundlight_openapi_client/model/classification_result.py

@@ -95,10 +95,7 @@ def openapi_types():
         """
         return {
             "label": (str,),  # noqa: E501
-            "confidence": (
-                float,
-                none_type,
-            ),  # noqa: E501
+            "confidence": (float,),  # noqa: E501
         }
 
     @cached_property
@@ -153,7 +150,7 @@ def _from_openapi_data(cls, 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,)
-            confidence (float, none_type): On a scale of 0 to 1, how confident are we in the predicted label?. [optional]  # noqa: E501
+            confidence (float): On a scale of 0 to 1, how confident are we in the predicted label?. [optional]  # noqa: E501
         """
 
         _check_type = kwargs.pop("_check_type", True)
@@ -244,7 +241,7 @@ def __init__(self, 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,)
-            confidence (float, none_type): On a scale of 0 to 1, how confident are we in the predicted label?. [optional]  # noqa: E501
+            confidence (float): On a scale of 0 to 1, how confident are we in the predicted label?. [optional]  # noqa: E501
         """
 
         _check_type = kwargs.pop("_check_type", True)

generated/model.py

@@ -1,6 +1,6 @@
 # generated by datamodel-codegen:
 #   filename:  public-api.yaml
-#   timestamp: 2024-04-04T00:11:29+00:00
+#   timestamp: 2024-04-11T20:35:47+00:00
 
 from __future__ import annotations
 

spec/public-api.yaml

@@ -420,7 +420,6 @@ components:
           type: number
           maximum: 1
           minimum: 0
-          nullable: true
           description:
             On a scale of 0 to 1, how confident are we in the predicted
             label?