operating-room-monitor-server/app/services/video/classification_handler.py

"""视觉分类结果处理：把 ``PredictionResult`` 转成自动记账 or 待人工确认。

从 ``CameraSessionManager`` 抽出，保持原先行为：
- 置信度低于 ``video_voice_confirm_min_confidence`` → 丢弃。
- 会话状态中候选清单为空 → 丢弃（开录时通常会由空请求解析为全量目录/模型类名）。
- 置信度 ≥ ``video_auto_confirm_confidence`` 且 Top1 在候选内 → 自动追加 vision 明细，并写消耗 TSV（记具体耗材）。
- 置信度 ≥ 自动阈值但 Top1 不在候选内 → 视 voice_confirmation_enabled 入 pending。
- 中等置信度 → 入 pending（若有可展示候选项）。

需医生确认时：消耗 TSV / 内存明细记「待确认」（不写模型 top1 商品名）；语音确认后**替换**该条 TSV 为最终耗材。停录时 TSV/终端汇总与查结果 API 同口径，由 details 经 ``build_consumption_summary`` 生成。
"""

from __future__ import annotations

import time

from app.baked import pipeline as bp
from app.services.consumable_vision_algorithm import (
    PredictionCandidate,
    PredictionResult,
)
from app.services.consumption_tsv_log import (
    append_consumption_pending_window,
    append_consumption_window,
    resolve_consumption_item_id,
)
from app.services.video.inference_aggregator import WindowInferenceReady
from app.services.video.session_registry import (
    SurgerySessionRegistry,
    SurgerySessionState,
    format_elapsed_mmss_since,
)


def rank_topk_for_candidates(
    topk: list[PredictionCandidate],
    ordered_candidates: list[str],
    *,
    limit: int = 5,
) -> list[PredictionCandidate]:
    if not topk:
        return []
    stripped_order = [c.strip() for c in ordered_candidates if c.strip()]
    if not stripped_order:
        return topk[:limit]
    order_index = {name: i for i, name in enumerate(stripped_order)}
    picked = [c for c in topk if c.label.strip() in order_index]
    picked.sort(key=lambda c: order_index[c.label.strip()])
    return picked[:limit]


class VisionClassificationHandler:
    """把分类结果转化为 registry 上的状态变更（追加明细 / 入队待确认）。"""

    def __init__(
        self,
        *,
        registry: SurgerySessionRegistry,
    ) -> None:
        self._registry = registry

    def _append_vision_consumption_window_if_ready(
        self,
        state: SurgerySessionState,
        ready: WindowInferenceReady | None,
        surgery_id: str,
        camera_id: str,
    ) -> None:
        if (
            ready is None
            or not surgery_id
            or not camera_id
            or (
                not bp.CONSUMPTION_TSV_LOG_ENABLED
                and not bp.CONSUMPTION_LOG_MARKDOWN_TERMINAL
            )
        ):
            return
        append_consumption_window(
            surgery_id=surgery_id,
            name_to_code=state.name_to_code,
            best=ready.best,
            doctor_id=bp.VIDEO_RESULT_DOCTOR_ID,
            camera_id=camera_id,
            wall_start_epoch=ready.wall_lo,
            wall_end_epoch=ready.wall_hi,
            since_recording_start=format_elapsed_mmss_since(
                state.surgery_started_wall,
                at_epoch=ready.wall_hi,
            ),
        )

    async def handle(
        self,
        *,
        state: SurgerySessionState,
        cls_res: PredictionResult,
        ready: WindowInferenceReady | None = None,
        surgery_id: str = "",
        camera_id: str = "",
    ) -> None:
        conf = cls_res.confidence
        label = (cls_res.label or "").strip()
        t1_pid = (ready.best.t1_pid if ready is not None else "")
        item_id = resolve_consumption_item_id(label, t1_pid, state.name_to_code)
        voice_floor = bp.VIDEO_VOICE_CONFIRM_MIN_CONFIDENCE
        if conf < voice_floor:
            return

        cand_order = [c.strip() for c in state.candidate_consumables if c.strip()]
        if not cand_order:
            return

        cand_set = set(cand_order)
        ranked = rank_topk_for_candidates(cls_res.topk, cand_order)
        auto_th = bp.VIDEO_AUTO_CONFIRM_CONFIDENCE

        def in_allowed(name: str) -> bool:
            return name in cand_set

        if conf >= auto_th and in_allowed(label):
            self._append_vision_consumption_window_if_ready(
                state, ready, surgery_id, camera_id
            )
            await self._registry.append_confirmed_detail(
                state=state,
                item_id=item_id or "unknown",
                item_name=label or "unknown",
                doctor_id=bp.VIDEO_RESULT_DOCTOR_ID,
                source="vision",
            )
            return

        if conf >= auto_th and not in_allowed(label):
            if ranked and bp.VOICE_CONFIRMATION_ENABLED:
                await self._enqueue(
                    state,
                    ranked,
                    label,
                    conf,
                    ready=ready,
                    surgery_id=surgery_id,
                    camera_id=camera_id,
                )
            return

        if not bp.VOICE_CONFIRMATION_ENABLED:
            return

        if ranked:
            await self._enqueue(
                state,
                ranked,
                label,
                conf,
                ready=ready,
                surgery_id=surgery_id,
                camera_id=camera_id,
            )
        elif in_allowed(label):
            await self._enqueue(
                state,
                [PredictionCandidate(label=label, confidence=conf)],
                label,
                conf,
                ready=ready,
                surgery_id=surgery_id,
                camera_id=camera_id,
            )

    async def _enqueue(
        self,
        state: SurgerySessionState,
        ranked: list[PredictionCandidate],
        top_key: str,
        top_confidence: float,
        *,
        ready: WindowInferenceReady | None = None,
        surgery_id: str = "",
        camera_id: str = "",
    ) -> None:
        cid = await self._registry.enqueue_pending_confirmation(
            state,
            ranked,
            top_key=top_key,
            top_confidence=top_confidence,
        )
        if cid is None:
            return
        at_ep = ready.wall_hi if ready is not None else time.time()
        if ready is not None and surgery_id and camera_id and (
            bp.CONSUMPTION_TSV_LOG_ENABLED
            or bp.CONSUMPTION_LOG_MARKDOWN_TERMINAL
        ):
            append_consumption_pending_window(
                surgery_id=surgery_id,
                confirmation_id=cid,
                model_snap=ready.best,
                doctor_id=bp.VIDEO_RESULT_DOCTOR_ID,
                camera_id=camera_id,
                wall_start_epoch=ready.wall_lo,
                wall_end_epoch=ready.wall_hi,
                tsv_enabled=bp.CONSUMPTION_TSV_LOG_ENABLED,
                markdown_terminal=bp.CONSUMPTION_LOG_MARKDOWN_TERMINAL,
                since_recording_start=format_elapsed_mmss_since(
                    state.surgery_started_wall,
                    at_epoch=at_ep,
                ),
            )
        await self._registry.append_pending_consumption_detail(
            state=state,
            confirmation_id=cid,
            doctor_id=bp.VIDEO_RESULT_DOCTOR_ID,
        )