buenosairesam
ddb4f17faa
_get_list_inner (Pydantic) and _resolve_ts_list (TypeScript) collapsed nested generics to "str" / "string[]" — so List[List[int]] became List[str] and List[Dict[str,Any]] became List[str]. Fix recurses on list-of-list and falls back to Dict[str,Any] / Record<string,unknown> for list-of-dict. Regenerated outputs: - core/gpu/models/models.py: SegmentFieldResponse.boundary now List[List[int]], unblocking /segment_field which was 500ing on every request with pydantic ValidationError - core/db/models.py + ui/common/types/generated.ts: Brand.airings now matches its source schema (List[Dict[str,Any]] / Record<string,unknown>[])
137 lines
3.6 KiB
Python
137 lines
3.6 KiB
Python
"""
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Pydantic Models - GENERATED FILE
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Do not edit directly. Regenerate using modelgen.
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"""
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from datetime import datetime
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from enum import Enum
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from typing import Any, Dict, List, Optional
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from uuid import UUID
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from pydantic import BaseModel, Field
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class DetectRequest(BaseModel):
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"""Request body for object detection."""
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image: str
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model: Optional[str] = None
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confidence: Optional[float] = None
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target_classes: Optional[List[str]] = None
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class BBox(BaseModel):
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"""A detected bounding box."""
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x: int
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y: int
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w: int
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h: int
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confidence: float
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label: str
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class DetectResponse(BaseModel):
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"""Response from object detection."""
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detections: List[BBox] = Field(default_factory=list)
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class OCRRequest(BaseModel):
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"""Request body for OCR."""
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image: str
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languages: Optional[List[str]] = None
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class OCRTextResult(BaseModel):
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"""A single OCR text extraction result."""
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text: str
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confidence: float
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bbox: List[int] = Field(default_factory=list)
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class OCRResponse(BaseModel):
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"""Response from OCR."""
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results: List[OCRTextResult] = Field(default_factory=list)
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class PreprocessRequest(BaseModel):
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"""Request body for image preprocessing."""
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image: str
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binarize: bool = False
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deskew: bool = False
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contrast: bool = True
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class PreprocessResponse(BaseModel):
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"""Response from preprocessing."""
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image: str
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class VLMRequest(BaseModel):
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"""Request body for visual language model query."""
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image: str
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prompt: str
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model: Optional[str] = None
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class VLMResponse(BaseModel):
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"""Response from VLM."""
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brand: str
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confidence: float
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reasoning: str
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class AnalyzeRegionsRequest(BaseModel):
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"""Request body for CV region analysis."""
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image: str
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edge_canny_low: int = 50
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edge_canny_high: int = 150
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edge_hough_threshold: int = 80
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edge_hough_min_length: int = 100
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edge_hough_max_gap: int = 10
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edge_pair_max_distance: int = 200
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edge_pair_min_distance: int = 15
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class RegionBox(BaseModel):
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"""A candidate region from CV analysis."""
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x: int
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y: int
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w: int
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h: int
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confidence: float
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label: str
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class AnalyzeRegionsResponse(BaseModel):
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"""Response from CV region analysis."""
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regions: List[RegionBox] = Field(default_factory=list)
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class AnalyzeRegionsDebugResponse(BaseModel):
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"""Response from CV region analysis with debug overlays."""
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regions: List[RegionBox] = Field(default_factory=list)
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edge_overlay_b64: str = ""
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lines_overlay_b64: str = ""
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horizontal_count: int = 0
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pair_count: int = 0
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class SegmentFieldRequest(BaseModel):
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"""Request body for field segmentation."""
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image: str
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hue_low: int = 30
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hue_high: int = 85
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sat_low: int = 30
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sat_high: int = 255
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val_low: int = 30
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val_high: int = 255
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morph_kernel: int = 15
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min_area_ratio: float = 0.05
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class SegmentFieldResponse(BaseModel):
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"""Response from field segmentation."""
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boundary: List[List[int]] = Field(default_factory=list)
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coverage: float = 0.0
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mask_b64: str = ""
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class SegmentFieldDebugResponse(BaseModel):
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"""Response from field segmentation with debug overlay."""
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boundary: List[List[int]] = Field(default_factory=list)
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coverage: float = 0.0
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mask_overlay_b64: str = ""
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class ConfigUpdate(BaseModel):
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"""Request body for updating server configuration."""
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device: Optional[str] = None
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yolo_model: Optional[str] = None
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yolo_confidence: Optional[float] = None
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vram_budget_mb: Optional[int] = None
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strategy: Optional[str] = None
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ocr_languages: Optional[List[str]] = None
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ocr_min_confidence: Optional[float] = None
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