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pythonannotation-tooldatasetsactive-learningtext-annotationdatasetnatural-language-processingdata-labelingmachine-learning
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147 lines
4.0 KiB
147 lines
4.0 KiB
from rest_framework import serializers
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from rest_polymorphic.serializers import PolymorphicSerializer
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from .models import (
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BoundingBoxProject,
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ImageCaptioningProject,
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ImageClassificationProject,
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IntentDetectionAndSlotFillingProject,
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Member,
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Project,
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SegmentationProject,
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Seq2seqProject,
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SequenceLabelingProject,
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Speech2textProject,
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Tag,
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TextClassificationProject,
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)
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class MemberSerializer(serializers.ModelSerializer):
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username = serializers.SerializerMethodField()
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rolename = serializers.SerializerMethodField()
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@classmethod
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def get_username(cls, instance):
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user = instance.user
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return user.username if user else None
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@classmethod
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def get_rolename(cls, instance):
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role = instance.role
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return role.name if role else None
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class Meta:
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model = Member
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fields = ("id", "user", "role", "username", "rolename")
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class TagSerializer(serializers.ModelSerializer):
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class Meta:
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model = Tag
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fields = (
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"id",
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"project",
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"text",
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)
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read_only_fields = ("id", "project")
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class ProjectSerializer(serializers.ModelSerializer):
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tags = TagSerializer(many=True, required=False)
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author = serializers.SerializerMethodField()
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@classmethod
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def get_author(cls, instance):
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if instance.created_by:
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return instance.created_by.username
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return ""
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class Meta:
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model = Project
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fields = [
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"id",
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"name",
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"description",
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"guideline",
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"project_type",
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"created_at",
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"updated_at",
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"random_order",
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"author",
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"collaborative_annotation",
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"single_class_classification",
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"is_text_project",
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"tags",
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]
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read_only_fields = (
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"created_at",
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"updated_at",
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"author",
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"is_text_project",
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)
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def create(self, validated_data):
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tags = TagSerializer(data=validated_data.pop("tags", []), many=True)
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project = self.Meta.model.objects.create(**validated_data)
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tags.is_valid()
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tags.save(project=project)
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return project
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def update(self, instance, validated_data):
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# Don't update tags. Please use TagAPI.
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validated_data.pop("tags", None)
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return super().update(instance, validated_data)
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class TextClassificationProjectSerializer(ProjectSerializer):
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class Meta(ProjectSerializer.Meta):
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model = TextClassificationProject
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class SequenceLabelingProjectSerializer(ProjectSerializer):
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class Meta(ProjectSerializer.Meta):
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model = SequenceLabelingProject
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fields = ProjectSerializer.Meta.fields + ["allow_overlapping", "grapheme_mode", "use_relation"]
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class Seq2seqProjectSerializer(ProjectSerializer):
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class Meta(ProjectSerializer.Meta):
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model = Seq2seqProject
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class IntentDetectionAndSlotFillingProjectSerializer(ProjectSerializer):
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class Meta(ProjectSerializer.Meta):
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model = IntentDetectionAndSlotFillingProject
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class Speech2textProjectSerializer(ProjectSerializer):
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class Meta(ProjectSerializer.Meta):
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model = Speech2textProject
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class ImageClassificationProjectSerializer(ProjectSerializer):
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class Meta(ProjectSerializer.Meta):
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model = ImageClassificationProject
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class BoundingBoxProjectSerializer(ProjectSerializer):
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class Meta(ProjectSerializer.Meta):
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model = BoundingBoxProject
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class SegmentationProjectSerializer(ProjectSerializer):
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class Meta(ProjectSerializer.Meta):
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model = SegmentationProject
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class ImageCaptioningProjectSerializer(ProjectSerializer):
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class Meta(ProjectSerializer.Meta):
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model = ImageCaptioningProject
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class ProjectPolymorphicSerializer(PolymorphicSerializer):
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model_serializer_mapping = {
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Project: ProjectSerializer,
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**{cls.Meta.model: cls for cls in ProjectSerializer.__subclasses__()},
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}
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