feat: display response in predictions

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="CVAT wrapper",
-    version="0.0.10",
+    version="0.0.11",
     author="antwxne",
     author_email="[email protected]",
     description="Python wrapper for CVAT API",

src/CVAT/Predictions/Foodvisor.py

@@ -12,7 +12,7 @@ def __init__(self, prediction: list[dict], frame_map: dict, label_map: dict):
             asset["externalId"] in frame_map
         ]
 
-    def export(self) -> LabeledData:
+    def export(self, display_response: bool = False) -> LabeledData:
         shapes: list[dict] = []
         tags: list[dict] = []
         for asset in self.assets:
@@ -44,27 +44,29 @@ def to_shapes(self) -> list[dict]:
             """
             return [annotation.to_json() for annotation in self.annotations]
 
-        def to_tags(self) -> list[dict]:
+        def to_tags(self, display_response: bool = False) -> list[dict]:
             """
             > The function takes a `Question` object as input and returns a list of dictionaries, each of which represents a
             tag
 
             Returns:
               A list of dictionaries.
             """
-            tags = [{
-                "frame": self.image.frame_number,
-                "label_id": self.choice.id,
-                "group": 0,
-                "source": "manual",
-                "attributes": [
-                    {
-                        "spec_id": self.choice.attributes[0]["id"],
-                        "value": "Je ne sais pas"
-                    }
-                ]
-            }]
-            if True:
+            tags: list[dict] = []
+            if not display_response:
+                tags.append({
+                    "frame": self.image.frame_number,
+                    "label_id": self.choice.id,
+                    "group": 0,
+                    "source": "manual",
+                    "attributes": [
+                        {
+                            "spec_id": self.choice.attributes[0]["id"],
+                            "value": "Je ne sais pas"
+                        }
+                    ]
+                })
+            else:
                 tags.append(
                     {
                         "frame": self.image.frame_number,

src/CVAT/Predictions/Interface.py

@@ -23,8 +23,11 @@ def __init__(self, prediction: Union[list[dict], dict], frame_map: dict, label_m
         raise NotImplementedError
 
     @abc.abstractmethod
-    def export(self) -> LabeledData:
+    def export(self, display_response: bool = False) -> LabeledData:
         """
-        > This function takes in a `self` object and returns a `LabeledData` object
+        > This function takes in a boolean value and returns a LabeledData object
+
+        Args:
+          display_response (bool): If True, the response will be displayed in the output. Defaults to False
         """
         raise NotImplementedError

src/CVAT/_put.py

@@ -14,11 +14,19 @@ class Put:
     def __init__(self):
         raise NotImplementedError
 
-    def upload_predictions(self, task: Task, prediction: IPrediction) -> None:
-        a = prediction.export().to_json()
+    def upload_predictions(self, task: Task, prediction: IPrediction, display_response: bool = False) -> None:
+        """
+        It takes a task and a prediction, converts the prediction to JSON, and sends it to the server
+
+        Args:
+          task (Task): Task - the task object that you want to upload the prediction to
+          prediction (IPrediction): IPrediction - the prediction object that you want to upload
+          display_response (bool): bool = False. Defaults to False
+        """
+        prediction_json: dict = prediction.export(display_response).to_json()
         response: Response = self.session.put(
             url=f'{self.url}/api/tasks/{task.id}/annotations',
-            json=a
+            json=prediction_json
         )
         if response.status_code != 200:
             raise Exception(response.content)