phase 4

core/detect/sse.py

@@ -0,0 +1,163 @@
+"""
+Pydantic Models - GENERATED FILE
+
+Do not edit directly. Regenerate using modelgen.
+"""
+
+from datetime import datetime
+from enum import Enum
+from typing import Any, Dict, List, Optional
+from uuid import UUID
+
+from pydantic import BaseModel, Field
+
+class GraphNode(BaseModel):
+    """A pipeline stage node."""
+    id: str
+    status: str = "idle"
+    items_in: int = 0
+    items_out: int = 0
+
+class GraphEdge(BaseModel):
+    """An edge between pipeline stages."""
+    source: str
+    target: str
+    throughput: int = 0
+
+class BoundingBoxEvent(BaseModel):
+    """Bounding box in SSE event payloads."""
+    x: int
+    y: int
+    w: int
+    h: int
+    confidence: float
+    label: str
+    resolved_brand: Optional[str] = None
+    source: Optional[str] = None
+    stage: Optional[str] = None
+
+class BrandSummary(BaseModel):
+    """Per-brand stats in the final report."""
+    brand: str
+    total_appearances: int = 0
+    total_screen_time: float = 0.0
+    avg_confidence: float = 0.0
+    first_seen: float = 0.0
+    last_seen: float = 0.0
+
+class GraphUpdate(BaseModel):
+    """Pipeline node state transition. SSE event: graph_update"""
+    nodes: List[GraphNode] = Field(default_factory=list)
+    edges: List[GraphEdge] = Field(default_factory=list)
+    active_path: List[str] = Field(default_factory=list)
+
+class StatsUpdate(BaseModel):
+    """Funnel statistics snapshot. SSE event: stats_update"""
+    frames_extracted: int = 0
+    frames_after_scene_filter: int = 0
+    cv_regions_detected: int = 0
+    regions_detected: int = 0
+    regions_resolved_by_ocr: int = 0
+    regions_escalated_to_local_vlm: int = 0
+    regions_escalated_to_cloud_llm: int = 0
+    cloud_llm_calls: int = 0
+    processing_time_seconds: float = 0.0
+    estimated_cloud_cost_usd: float = 0.0
+    run_id: Optional[str] = None
+    parent_job_id: Optional[str] = None
+    run_type: str = "initial"
+
+class FrameUpdate(BaseModel):
+    """Current frame being processed. SSE event: frame_update"""
+    frame_ref: int
+    timestamp: float
+    jpeg_b64: str
+    boxes: List[BoundingBoxEvent] = Field(default_factory=list)
+
+class Detection(BaseModel):
+    """A confirmed brand detection. SSE event: detection"""
+    brand: str
+    timestamp: float
+    duration: float
+    confidence: float
+    source: str
+    content_type: str
+    bbox: Optional[BoundingBoxEvent] = None
+    frame_ref: Optional[int] = None
+
+class LogEvent(BaseModel):
+    """Pipeline log line. SSE event: log"""
+    level: str
+    stage: str
+    msg: str
+    ts: str
+    trace_id: Optional[str] = None
+
+class DetectionReportSummary(BaseModel):
+    """Final detection report summary."""
+    video_source: str
+    content_type: str
+    duration_seconds: float
+    total_detections: int = 0
+    brands: List[BrandSummary] = Field(default_factory=list)
+    stats: Optional[StatsUpdate] = None
+
+class JobComplete(BaseModel):
+    """Final report when pipeline finishes. SSE event: job_complete"""
+    job_id: str
+    report: Optional[DetectionReportSummary] = None
+
+class RunContext(BaseModel):
+    """Run context injected into all SSE events for grouping."""
+    run_id: str
+    parent_job_id: str
+    run_type: str = "initial"
+
+class CheckpointInfo(BaseModel):
+    """Available checkpoint for a stage."""
+    stage: str
+    is_scenario: bool = False
+    scenario_label: str = ""
+
+class ReplayRequest(BaseModel):
+    """Request to replay pipeline from a specific stage."""
+    job_id: str
+    start_stage: str
+    config_overrides: Optional[Dict[str, Any]] = None
+
+class ReplayResponse(BaseModel):
+    """Result of a replay invocation."""
+    status: str
+    job_id: str
+    start_stage: str
+    detections: int = 0
+    brands_found: int = 0
+
+class RetryRequest(BaseModel):
+    """Request to queue async retry with different config."""
+    job_id: str
+    config_overrides: Optional[Dict[str, Any]] = None
+    start_stage: str = "escalate_vlm"
+    schedule_seconds: Optional[float] = None
+
+class RetryResponse(BaseModel):
+    """Result of queueing a retry task."""
+    status: str
+    task_id: str
+    job_id: str
+
+class RunRequest(BaseModel):
+    """Request body for launching a detection pipeline run."""
+    video_path: str
+    profile_name: str = "soccer_broadcast"
+    source_asset_id: str = ""
+    checkpoint: bool = True
+    skip_vlm: bool = False
+    skip_cloud: bool = False
+    log_level: str = "INFO"
+
+class RunResponse(BaseModel):
+    """Response after starting a pipeline run."""
+    status: str
+    job_id: str
+    video_path: str