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- 接入 SAM2 视频传播能力：新增 /api/ai/propagate，支持用当前帧 mask/polygon/bbox 作为 seed，通过 SAM2 video predictor 向前、向后或双向传播，并可保存为真实 annotation。 - 接入 SAM3 video tracker：通过独立 Python 3.12 external worker 调用 SAM3 video predictor/tracker，使用本地 checkpoint 与 bbox seed 执行视频级跟踪，并在模型状态中标记 video_track 能力。 - 完善 SAM 模型分发：sam_registry 按 model_id 明确区分 sam2 propagation 与 sam3 video_track，避免两个模型链路混用。 - 打通前端“传播片段”：VideoWorkspace 使用当前选中 mask 和当前 AI 模型调用后端传播接口，传播结果回写并刷新工作区已保存标注。 - 增强 SAM3 本地 checkpoint 配置：新增 sam3_checkpoint_path 配置和 .env.example 示例，状态检查改为基于本地 checkpoint/独立环境/模型包可用性。 - 完善视频拆帧参数：/api/media/parse 支持 parse_fps、max_frames、target_width，后端任务保存帧时间戳、源帧号和 frame_sequence 元数据。 - 增加运行时 schema 兼容处理：启动时为旧 frames 表补充 timestamp_ms 和 source_frame_number 列，避免旧库升级后缺字段。 - 强化 Canvas 标注编辑：补齐多边形闭合、点工具、顶点拖拽、边中点插入、Delete/Backspace 删除、区域合并和重叠去除等交互。 - 增强语义分类联动：选中 mask 后可通过右侧语义分类树更新标签、颜色和 class metadata，并同步到保存/导出链路。 - 增加关键帧时间轴体验：FrameTimeline 显示具体时间信息，并支持键盘左右方向键切换关键帧。 - 完善 AI 交互分割参数：前端保留正向点、反向点、框选和 interactive prompt 的调用状态，支持 SAM2 细化候选区域与 SAM3 bbox 入口。 - 扩展后端/前端 API 类型：新增 propagateMasks、传播请求/响应 schema，并补齐 annotation、导出、模型状态和任务接口的测试覆盖。 - 更新项目文档：同步 README、AGENTS、接口契约、需求冻结、设计冻结、前端元素审计、实施计划和测试计划，标明真实功能边界与剩余风险。 - 增加测试覆盖：补充 SAM2/SAM3 传播、SAM3 状态、媒体拆帧参数、Canvas 编辑、语义标签切换、时间轴、工作区传播和 API 合约测试。 - 加强仓库安全边界：将 sam3权重/ 加入 .gitignore，避免本地模型权重被误提交。 验证：npm run test:run；pytest backend/tests；npm run lint；npm run build；python -m py_compile；git diff --check。
263 lines
6.8 KiB
Python
263 lines
6.8 KiB
Python
"""Pydantic schemas for request/response validation."""
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from datetime import datetime
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from typing import Optional, Any
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from pydantic import BaseModel, ConfigDict
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# ---------------------------------------------------------------------------
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# Project schemas
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# ---------------------------------------------------------------------------
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class ProjectBase(BaseModel):
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name: str
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description: Optional[str] = None
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video_path: Optional[str] = None
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thumbnail_url: Optional[str] = None
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status: Optional[str] = "pending"
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source_type: Optional[str] = "video"
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original_fps: Optional[float] = None
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parse_fps: Optional[float] = 30.0
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class ProjectCreate(ProjectBase):
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pass
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class ProjectUpdate(BaseModel):
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name: Optional[str] = None
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description: Optional[str] = None
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video_path: Optional[str] = None
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thumbnail_url: Optional[str] = None
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status: Optional[str] = None
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source_type: Optional[str] = None
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original_fps: Optional[float] = None
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parse_fps: Optional[float] = None
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class ProjectOut(ProjectBase):
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model_config = ConfigDict(from_attributes=True)
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id: int
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created_at: datetime
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updated_at: datetime
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frame_count: int = 0
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# ---------------------------------------------------------------------------
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# Frame schemas
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# ---------------------------------------------------------------------------
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class FrameBase(BaseModel):
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frame_index: int
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image_url: str
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width: Optional[int] = None
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height: Optional[int] = None
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timestamp_ms: Optional[float] = None
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source_frame_number: Optional[int] = None
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class FrameCreate(FrameBase):
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project_id: int
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class FrameOut(FrameBase):
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model_config = ConfigDict(from_attributes=True)
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id: int
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project_id: int
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created_at: datetime
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# ---------------------------------------------------------------------------
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# Template schemas
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# ---------------------------------------------------------------------------
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class TemplateBase(BaseModel):
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name: str
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description: Optional[str] = None
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color: str
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z_index: int = 0
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mapping_rules: Optional[dict[str, Any]] = None
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classes: Optional[list[dict[str, Any]]] = None
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rules: Optional[list[dict[str, Any]]] = None
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class TemplateCreate(TemplateBase):
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pass
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class TemplateUpdate(BaseModel):
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name: Optional[str] = None
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description: Optional[str] = None
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color: Optional[str] = None
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z_index: Optional[int] = None
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mapping_rules: Optional[dict[str, Any]] = None
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classes: Optional[list[dict[str, Any]]] = None
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rules: Optional[list[dict[str, Any]]] = None
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class TemplateOut(TemplateBase):
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model_config = ConfigDict(from_attributes=True)
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id: int
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created_at: datetime
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# ---------------------------------------------------------------------------
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# Annotation schemas
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# ---------------------------------------------------------------------------
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class AnnotationBase(BaseModel):
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project_id: int
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frame_id: Optional[int] = None
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template_id: Optional[int] = None
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mask_data: Optional[dict[str, Any]] = None
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points: Optional[list[list[float]]] = None
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bbox: Optional[list[float]] = None
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class AnnotationCreate(AnnotationBase):
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pass
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class AnnotationUpdate(BaseModel):
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mask_data: Optional[dict[str, Any]] = None
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points: Optional[list[list[float]]] = None
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bbox: Optional[list[float]] = None
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template_id: Optional[int] = None
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class AnnotationOut(AnnotationBase):
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model_config = ConfigDict(from_attributes=True)
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id: int
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created_at: datetime
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updated_at: datetime
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# ---------------------------------------------------------------------------
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# Mask schemas
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# ---------------------------------------------------------------------------
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class MaskBase(BaseModel):
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annotation_id: int
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mask_url: str
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format: str = "png"
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class MaskCreate(MaskBase):
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pass
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class MaskOut(MaskBase):
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model_config = ConfigDict(from_attributes=True)
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id: int
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created_at: datetime
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# ---------------------------------------------------------------------------
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# Processing task schemas
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# ---------------------------------------------------------------------------
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class ProcessingTaskOut(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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id: int
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task_type: str
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status: str
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progress: int
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message: Optional[str] = None
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project_id: Optional[int] = None
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celery_task_id: Optional[str] = None
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payload: Optional[dict[str, Any]] = None
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result: Optional[dict[str, Any]] = None
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error: Optional[str] = None
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created_at: datetime
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started_at: Optional[datetime] = None
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finished_at: Optional[datetime] = None
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updated_at: datetime
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# ---------------------------------------------------------------------------
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# AI schemas
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# ---------------------------------------------------------------------------
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class PredictRequest(BaseModel):
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image_id: int
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prompt_type: str # point / box / semantic
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prompt_data: Any
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model: Optional[str] = None
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options: Optional[dict[str, Any]] = None
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class PredictResponse(BaseModel):
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polygons: list[list[list[float]]]
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scores: Optional[list[float]] = None
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class PropagationSeed(BaseModel):
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polygons: Optional[list[list[list[float]]]] = None
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bbox: Optional[list[float]] = None
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points: Optional[list[list[float]]] = None
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labels: Optional[list[int]] = None
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label: Optional[str] = None
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color: Optional[str] = None
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class_metadata: Optional[dict[str, Any]] = None
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template_id: Optional[int] = None
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class PropagateRequest(BaseModel):
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project_id: int
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frame_id: int
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model: Optional[str] = "sam2"
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seed: PropagationSeed
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direction: str = "forward"
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max_frames: int = 30
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include_source: bool = False
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save_annotations: bool = True
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class PropagateResponse(BaseModel):
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model: str
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direction: str
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source_frame_id: int
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processed_frame_count: int
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created_annotation_count: int
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annotations: list[AnnotationOut]
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class AiModelStatus(BaseModel):
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id: str
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label: str
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available: bool
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loaded: bool = False
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device: str
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supports: list[str]
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message: str
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package_available: bool = False
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checkpoint_exists: bool = False
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checkpoint_path: Optional[str] = None
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python_ok: bool = True
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torch_ok: bool = True
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cuda_required: bool = False
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external_available: bool = False
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external_python: Optional[str] = None
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class GpuStatus(BaseModel):
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available: bool
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device: str
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name: Optional[str] = None
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torch_available: bool
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torch_version: Optional[str] = None
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cuda_version: Optional[str] = None
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class AiRuntimeStatus(BaseModel):
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selected_model: str
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gpu: GpuStatus
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models: list[AiModelStatus]
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# ---------------------------------------------------------------------------
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# Export schemas
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# ---------------------------------------------------------------------------
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class ExportStatus(BaseModel):
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url: str
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format: str
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