Method sync_import

label_studio/data_import/api.py:283–372

(self, request, project, preannotated_from_fields, commit_to_project, return_task_ids)

        return task_instances, serializer

    def sync_import(self, request, project, preannotated_from_fields, commit_to_project, return_task_ids):
        start = time.time()
        tasks = None
        # upload files from request, and parse all tasks
        # TODO: Stop passing request to load_tasks function, make all validation before
        parsed_data, file_upload_ids, could_be_tasks_list, found_formats, data_columns = load_tasks(request, project)

        if preannotated_from_fields:
            # turn flat task JSONs {"column1": value, "column2": value} into {"data": {"column1"..}, "predictions": [{..."column2"}]
            raise_errors = flag_set('fflag_feat_utc_210_prediction_validation_15082025', user='auto')
            logger.info(f'Reformatting predictions with raise_errors: {raise_errors}')
            parsed_data = reformat_predictions(parsed_data, preannotated_from_fields, project, raise_errors)

        # Conditionally validate predictions: skip when label config is default during project creation
        if project.label_config_is_not_default:
            validation_errors = []
            li = LabelInterface(project.label_config)

            for i, task in enumerate(parsed_data):
                if 'predictions' in task:
                    for j, prediction in enumerate(task['predictions']):
                        try:
                            validation_errors_list = li.validate_prediction(prediction, return_errors=True)
                            if validation_errors_list:
                                for error in validation_errors_list:
                                    validation_errors.append(f'Task {i}, prediction {j}: {error}')
                        except Exception as e:
                            error_msg = f'Task {i}, prediction {j}: Error validating prediction - {extract_message(e)}'
                            validation_errors.append(error_msg)

            if validation_errors:
                error_message = f'Prediction validation failed ({len(validation_errors)} errors):\n'
                for error in validation_errors:
                    error_message += f'- {error}\n'

                if flag_set('fflag_feat_utc_210_prediction_validation_15082025', user='auto'):
                    raise ValidationError({'predictions': [error_message]})
                else:
                    logger.error(
                        f'Prediction validation failed, not raising error - ({len(validation_errors)} errors):\n{error_message}'
                    )

        if commit_to_project:
            # Immediately create project tasks and update project states and counters
            tasks, serializer = self._save(parsed_data)
            task_count = len(tasks)
            annotation_count = len(serializer.db_annotations)
            prediction_count = len(serializer.db_predictions)

            recalculate_stats_counts = {
                'task_count': task_count,
                'annotation_count': annotation_count,
                'prediction_count': prediction_count,
            }

            # Update counters (like total_annotations) for new tasks and after bulk update tasks stats. It should be a
            # single operation as counters affect bulk is_labeled update
            project.update_tasks_counters_and_task_states(