"""
LangGraph state definition for the detection pipeline.

This TypedDict flows through all graph nodes. Each node reads what
it needs and writes its outputs. LangGraph manages the state transitions.
"""

from __future__ import annotations

from typing import TypedDict

from core.detect.models import BoundingBox, BrandDetection, DetectionReport, Frame, PipelineStats, TextCandidate


class DetectState(TypedDict, total=False):
    # Input
    video_path: str
    job_id: str
    profile_name: str
    source_asset_id: str  # UUID of the source MediaAsset

    # Stage outputs
    frames: list[Frame]
    filtered_frames: list[Frame]
    field_masks: dict  # {seq: np.ndarray} — pitch mask per frame
    field_boundaries: dict  # {seq: [(x,y), ...]} — pitch boundary per frame
    field_coverage: dict  # {seq: float} — pitch coverage ratio per frame
    edge_regions_by_frame: dict[int, list[BoundingBox]]
    boxes_by_frame: dict[int, list[BoundingBox]]
    preprocessed_crops: dict  # "{frame_seq}_{box_idx}" → np.ndarray
    text_candidates: list[TextCandidate]
    unresolved_candidates: list[TextCandidate]
    detections: list[BrandDetection]
    report: DetectionReport

    # Session brands (accumulated during the run, persisted to DB)
    session_brands: dict  # {normalized_name: canonical_name}

    # Running stats (updated by each stage)
    stats: PipelineStats

    # Config overrides for replay (merged into profile configs dict)
    config_overrides: dict