104 lines
2.8 KiB
Python
104 lines
2.8 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 GraphNode(BaseModel):
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"""A pipeline stage node."""
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id: str
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status: str = "idle"
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items_in: int = 0
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items_out: int = 0
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class GraphEdge(BaseModel):
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"""An edge between pipeline stages."""
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source: str
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target: str
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throughput: int = 0
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class BoundingBoxEvent(BaseModel):
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"""Bounding box in SSE event payloads."""
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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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resolved_brand: Optional[str] = None
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source: Optional[str] = None
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class BrandSummary(BaseModel):
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"""Per-brand stats in the final report."""
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brand: str
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total_appearances: int = 0
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total_screen_time: float = 0.0
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avg_confidence: float = 0.0
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first_seen: float = 0.0
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last_seen: float = 0.0
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class GraphUpdate(BaseModel):
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"""Pipeline node state transition. SSE event: graph_update"""
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nodes: List[GraphNode] = Field(default_factory=list)
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edges: List[GraphEdge] = Field(default_factory=list)
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active_path: List[str] = Field(default_factory=list)
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class StatsUpdate(BaseModel):
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"""Funnel statistics snapshot. SSE event: stats_update"""
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frames_extracted: int = 0
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frames_after_scene_filter: int = 0
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regions_detected: int = 0
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regions_resolved_by_ocr: int = 0
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regions_escalated_to_local_vlm: int = 0
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regions_escalated_to_cloud_llm: int = 0
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cloud_llm_calls: int = 0
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processing_time_seconds: float = 0.0
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estimated_cloud_cost_usd: float = 0.0
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class FrameUpdate(BaseModel):
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"""Current frame being processed. SSE event: frame_update"""
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frame_ref: int
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timestamp: float
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jpeg_b64: str
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boxes: List[BoundingBoxEvent] = Field(default_factory=list)
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class Detection(BaseModel):
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"""A confirmed brand detection. SSE event: detection"""
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brand: str
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timestamp: float
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duration: float
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confidence: float
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source: str
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content_type: str
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bbox: Optional[BoundingBoxEvent] = None
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frame_ref: Optional[int] = None
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class LogEvent(BaseModel):
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"""Pipeline log line. SSE event: log"""
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level: str
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stage: str
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msg: str
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ts: str
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trace_id: Optional[str] = None
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class DetectionReportSummary(BaseModel):
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"""Final detection report summary."""
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video_source: str
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content_type: str
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duration_seconds: float
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total_detections: int = 0
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brands: List[BrandSummary] = Field(default_factory=list)
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stats: Optional[StatsUpdate] = None
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class JobComplete(BaseModel):
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"""Final report when pipeline finishes. SSE event: job_complete"""
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job_id: str
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report: Optional[DetectionReportSummary] = None
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