f37c477a0a
- FastAPI app with SQLite DB (projects, pages, issues, feedback) - OpenSeadragon DZI viewer with inline SVG overlays - Dashboard: upload, project list, tiling toggle, review mode - Pipeline integration: tiling OCR → layout → elements → rules QC → DZI → DB - Feedback collection: true_positive / false_positive / not_sure per issue
138 lines
3.7 KiB
Python
138 lines
3.7 KiB
Python
# app/schemas.py
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from pydantic import BaseModel
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from typing import List, Optional, Dict, Any
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from datetime import datetime
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# ---------- Project ----------
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class ProjectBase(BaseModel):
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name: Optional[str] = None
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class ProjectCreate(ProjectBase):
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pdf_filename: str
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class ProjectResponse(ProjectBase):
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id: int
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pdf_filename: str
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status: str
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created_at: datetime
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completed_at: Optional[datetime] = None
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error_message: Optional[str] = None
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class Config:
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from_attributes = True
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class ProjectDetail(ProjectResponse):
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pages: List["PageResponse"] = []
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issues: List["IssueResponse"] = []
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class Config:
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from_attributes = True
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# ---------- Page ----------
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class PageBase(BaseModel):
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page_number: int
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width: Optional[int] = None
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height: Optional[int] = None
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class PageResponse(PageBase):
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id: int
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png_path: Optional[str] = None
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vlm_description: Optional[str] = None
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ocr_data: Optional[Dict[str, Any]] = None
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issue_count: int = 0
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class Config:
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from_attributes = True
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# ---------- Issue ----------
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class IssueBase(BaseModel):
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issue_type: str
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severity: str
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message: str
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page_number: Optional[int] = None
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class IssueCreate(IssueBase):
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bbox_x1: Optional[float] = None
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bbox_y1: Optional[float] = None
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bbox_x2: Optional[float] = None
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bbox_y2: Optional[float] = None
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dimension_text: Optional[str] = None
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confidence: Optional[float] = None
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extra_data: Optional[Dict[str, Any]] = None
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class IssueResponse(IssueBase):
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id: int
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project_id: int
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bbox_x1: Optional[float] = None
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bbox_y1: Optional[float] = None
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bbox_x2: Optional[float] = None
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bbox_y2: Optional[float] = None
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dimension_text: Optional[str] = None
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confidence: Optional[float] = None
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source: Optional[str] = None
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created_at: datetime
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feedback: Optional["FeedbackResponse"] = None
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page_id: Optional[int] = None
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class Config:
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from_attributes = True
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# ---------- Feedback ----------
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class FeedbackCreate(BaseModel):
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issue_id: int
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is_true_positive: Optional[bool] = None
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comment: Optional[str] = None
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action_taken: Optional[str] = None # fixed / ignored / not_sure
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class FeedbackResponse(BaseModel):
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id: int
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issue_id: int
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is_true_positive: Optional[bool] = None
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comment: Optional[str] = None
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action_taken: Optional[str] = None
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created_at: datetime
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class Config:
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from_attributes = True
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# ---------- Stats ----------
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class StatsResponse(BaseModel):
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total_projects: int
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total_issues: int
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issues_by_type: Dict[str, int]
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feedback_stats: Dict[str, int] # true_positive, false_positive, unreviewed
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accuracy_estimate: Optional[float] = None # Доля правильных срабатываний
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# ---------- Training Data ----------
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class TrainingSample(BaseModel):
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"""Один пример для обучения ML-модели."""
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issue_id: int
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issue_type: str
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severity: str
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message: str
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bbox: Dict[str, float] # x1, y1, x2, y2
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dimension_text: Optional[str] = None
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confidence: Optional[float] = None
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page_number: int
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is_true_positive: Optional[bool] = None
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# Для обучения детектора (YOLO)
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image_path: Optional[str] = None # Путь к PNG страницы
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crop_path: Optional[str] = None # Вырезанный фрагмент bbox
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label: Optional[str] = None # "good_dimension" / "bad_placement" / etc.
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class TrainingDataExport(BaseModel):
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total_samples: int
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labeled_samples: int # Есть is_true_positive
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samples: List[TrainingSample]
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export_format: str # json / yolo / csv
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class Config:
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from_attributes = True
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