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* feat: Enhance annotation module with template management and validation - Added DatasetMappingCreateRequest and DatasetMappingUpdateRequest schemas to handle dataset mapping requests with camelCase and snake_case support. - Introduced Annotation Template schemas including CreateAnnotationTemplateRequest, UpdateAnnotationTemplateRequest, and AnnotationTemplateResponse for managing annotation templates. - Implemented AnnotationTemplateService for creating, updating, retrieving, and deleting annotation templates, including validation of configurations and XML generation. - Added utility class LabelStudioConfigValidator for validating Label Studio configurations and XML formats. - Updated database schema for annotation templates and labeling projects to include new fields and constraints. - Seeded initial annotation templates for various use cases including image classification, object detection, and text classification. * feat: Enhance TemplateForm with improved validation and dynamic field rendering; update LabelStudio config validation for camelCase support * feat: Update docker-compose.yml to mark datamate dataset volume and network as external
56 lines
2.7 KiB
Python
56 lines
2.7 KiB
Python
from pydantic import Field, BaseModel
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from typing import Optional
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from datetime import datetime
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from app.module.shared.schema import BaseResponseModel
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from app.module.shared.schema import StandardResponse
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class DatasetMappingCreateRequest(BaseModel):
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"""数据集映射 创建 请求模型
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Accept both snake_case and camelCase field names from frontend JSON by
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declaring explicit aliases. Frontend sends `datasetId`, `name`,
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`description`, `templateId` (camelCase), so provide aliases so pydantic will map them
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to the internal attributes used in the service code (dataset_id, name,
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description, template_id).
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"""
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dataset_id: str = Field(..., alias="datasetId", description="源数据集ID")
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name: Optional[str] = Field(None, alias="name", description="标注项目名称")
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description: Optional[str] = Field(None, alias="description", description="标注项目描述")
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template_id: Optional[str] = Field(None, alias="templateId", description="标注模板ID")
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class Config:
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# allow population by field name when constructing model programmatically
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validate_by_name = True
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class DatasetMappingCreateResponse(BaseResponseModel):
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"""数据集映射 创建 响应模型"""
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id: str = Field(..., description="映射UUID")
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labeling_project_id: str = Field(..., description="Label Studio项目ID")
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labeling_project_name: str = Field(..., description="Label Studio项目名称")
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class DatasetMappingUpdateRequest(BaseResponseModel):
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"""数据集映射 更新 请求模型"""
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dataset_id: Optional[str] = Field(None, description="源数据集ID")
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class DatasetMappingResponse(BaseModel):
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"""数据集映射 查询 响应模型"""
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id: str = Field(..., description="映射UUID")
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dataset_id: str = Field(..., alias="datasetId", description="源数据集ID")
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dataset_name: Optional[str] = Field(None, alias="datasetName", description="数据集名称")
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labeling_project_id: str = Field(..., alias="labelingProjectId", description="标注项目ID")
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name: Optional[str] = Field(None, description="标注项目名称")
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description: Optional[str] = Field(None, description="标注项目描述")
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created_at: datetime = Field(..., alias="createdAt", description="创建时间")
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updated_at: Optional[datetime] = Field(None, alias="updatedAt", description="更新时间")
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deleted_at: Optional[datetime] = Field(None, alias="deletedAt", description="删除时间")
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class Config:
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from_attributes = True
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populate_by_name = True
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class DeleteDatasetResponse(BaseResponseModel):
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"""删除数据集响应模型"""
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id: str = Field(..., description="映射UUID")
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status: str = Field(..., description="删除状态") |