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OrderedDict from lxml import etree import xmljson +from jsf import JSF from label_studio_sdk._legacy.exceptions import ( LSConfigParseException, @@ -770,7 +771,7 @@ def validate_region(self, region) -> bool: return False # type of the region should match the tag name - if control.tag.lower() != region["type"]: + if control.tag.lower() != region["type"].lower(): return False # make sure that in config it connects to the same tag as @@ -839,9 +840,67 @@ def generate_sample_task(self, mode="upload", secure_mode=False): return task - def generate_sample_annotation(self): - """ """ - raise NotImplemented() + def _generate_sample_regions(self): + """ Generate an example of each control tag's JSON schema and validate it as a region""" + return self.create_regions({ + control.name: JSF(control.to_json_schema()).generate() + for control in self.controls + }) + + def generate_sample_prediction(self) -> Optional[dict]: + """Generates a sample prediction that is valid for this label config. + + Example: + {'model_version': 'sample model version', + 'score': 0.0, + 'result': [{'id': 'e7bd76e6-4e88-4eb3-b433-55e03661bf5d', + 'from_name': 'sentiment', + 'to_name': 'text', + 'type': 'choices', + 'value': {'choices': ['Neutral']}}]} + + NOTE: `id` field in result is not required when importing predictions; it will be generated automatically. + NOTE: for each control tag, depends on tag.to_json_schema() being implemented correctly + """ + prediction = PredictionValue( + model_version='sample model version', + result=self._generate_sample_regions() + ) + prediction_dct = prediction.model_dump() + if self.validate_prediction(prediction_dct): + return prediction_dct + else: + logger.debug(f'Sample prediction {prediction_dct} failed validation for label config {self.config}') + return None + + def generate_sample_annotation(self) -> Optional[dict]: + """Generates a sample annotation that is valid for this label config. + + Example: + {'was_cancelled': False, + 'ground_truth': False, + 'lead_time': 0.0, + 'result_count': 0, + 'completed_by': -1, + 'result': [{'id': 'b05da11d-3ffc-4657-8b8d-f5bc37cd59ac', + 'from_name': 'sentiment', + 'to_name': 'text', + 'type': 'choices', + 'value': {'choices': ['Negative']}}]} + + NOTE: `id` field in result is not required when importing predictions; it will be generated automatically. + NOTE: for each control tag, depends on tag.to_json_schema() being implemented correctly + """ + annotation = AnnotationValue( + completed_by=-1, # annotator's user id + result=self._generate_sample_regions() + ) + annotation_dct = annotation.model_dump() + if self.validate_annotation(annotation_dct): + return annotation_dct + else: + logger.debug(f'Sample annotation {annotation_dct} failed validation for label config {self.config}') + return None ##### ##### COMPATIBILITY LAYER diff --git a/src/label_studio_sdk/label_interface/region.py b/src/label_studio_sdk/label_interface/region.py index 431bcfd36..260f57fc5 100644 --- a/src/label_studio_sdk/label_interface/region.py +++ b/src/label_studio_sdk/label_interface/region.py @@ -10,16 +10,7 @@ class Region(BaseModel): """ - Class for Region Tag - - Attributes: - ----------- - id: str - The unique identifier of the region - x: int - The x coordinate of the region - y: int - + A Region is an item in the `result` list of a PredictionValue or AnnotationValue. """ id: str = Field(default_factory=lambda: str(uuid4()))