"""평가 라우트 — 교수자/관리자용 평가 조회 + 재평가 트리거 (Features: evaluator).

services/evaluator.py 의 2-loop 평가(fast/deep)를 교수자(TEACHER)·관리자(ADMIN)에게 노출한다.
학습자(LEARNER)에겐 평가 결과가 직접 노출되지 않는다(설계서 §4.1 2-레이어: RBAC × AIView).
이 라우터는 RBAC(레이어2)만 강제한다 — require_role(TEACHER, ADMIN). 평가 AI 는 전부 봐도 되므로
(레이어1 AIView.EVALUATOR) CCD/정답 누설 걱정은 client AI 쪽 책임이고 여기선 무관.

엔드포인트:
  GET  /eval/health                       — 헬스(소유 트랙 전환 확인)
  POST /eval/sessions/{id}/turn           — 단일 턴 fast-loop 재평가 트리거
  POST /eval/sessions/{id}/reevaluate     — 회기 deep-loop 재평가 트리거(전체 축어록)
  GET  /eval/sessions/{id}/evaluation     — 회기 평가 조회(분포 + 최근 deep 결과)

평가 결과는 session_persistence 의 DB-backed evaluation 저장소를 사용한다. DB 미가용 시
in-proc cache/session fallback 은 local dev 에서만 허용한다.
"""

from __future__ import annotations

import logging
from typing import Annotated, Any, Optional

from fastapi import APIRouter, Depends, HTTPException, status
from pydantic import BaseModel, Field

from .. import session_persistence
from ..deps import Principal, Role, require_role
from ..engine_client import EngineError, engine_client
from ..runtime_policy import runtime_fallback_allowed
from ..session_read_model import StageLabel, stage_label_or_none
from ..services import evaluator
from ..services.evaluator import SessionEvaluation, TurnEvaluation
from ..store import InProcSession
from ..store import store

router = APIRouter(prefix="/eval", tags=["eval"])
logger = logging.getLogger(__name__)

# 교수자/관리자만 평가 조회·트리거 (학습자 비노출)
TeacherOrAdmin = Annotated[Principal, Depends(require_role(Role.TEACHER, Role.ADMIN))]


# ── 요청/응답 모델 ──────────────────────────────────────
class ReevaluateRequest(BaseModel):
    scope: str = Field("session_end", description="'session_end' | 'stage_transition'")


class TurnReevaluateRequest(BaseModel):
    turn_seq: int = Field(..., ge=0, description="재평가할 상담자 발화의 turn_seq")


class EvaluationSummary(BaseModel):
    """회기 평가 조회 응답(분포 + deep 결과 합본)."""

    session_id: str
    stage: StageLabel | None = None
    status: str | None = None
    error: str | None = None
    durable: bool = False
    deep: Optional[dict[str, Any]] = None
    distribution: dict[str, Any] = Field(default_factory=dict)


class TurnEvaluationResponse(TurnEvaluation):
    stage: StageLabel


class SessionEvaluationResponse(SessionEvaluation):
    stage: StageLabel


async def _load_session_or_404(session_id: str, principal: Principal) -> InProcSession:
    sess = await session_persistence.load_session(session_id, principal, allow_ended=True)
    if sess is not None:
        store.put(sess)
    elif runtime_fallback_allowed():
        sess = store.get(session_id)
    if sess is None:
        raise HTTPException(status.HTTP_404_NOT_FOUND, detail="session not found")
    return sess


def _theory_mode_of(sess) -> Optional[str]:
    # 회기에서 학습자가 명시 선택한 이론 모드가 최우선이다.
    sess_theory = str(getattr(sess, "theory_mode", "") or "").strip()
    if sess_theory:
        return sess_theory
    tt = getattr(sess.persona, "theory_target", None)
    if isinstance(tt, (list, tuple)) and tt:
        return ", ".join(str(x) for x in tt)
    return None


def _summary_stage(value: object) -> StageLabel | None:
    return stage_label_or_none(value)


@router.get("/health")
async def eval_health() -> dict[str, str]:
    """평가 라우터 헬스 — Features:evaluator 로 전환됨."""
    return {"status": "ok", "owner": "features:evaluator", "loops": "fast,deep"}


def _session_evaluation_error_status(error: str) -> int:
    if error.startswith("engine_error"):
        return status.HTTP_503_SERVICE_UNAVAILABLE
    return status.HTTP_502_BAD_GATEWAY


# ════════════════════════════════════════════════════════════════════════════
# 회기 deep-loop 재평가 트리거 (교수자/관리자)
# ════════════════════════════════════════════════════════════════════════════
@router.post("/sessions/{session_id}/reevaluate", response_model=SessionEvaluationResponse)
async def reevaluate_session(
    session_id: str,
    body: ReevaluateRequest,
    principal: TeacherOrAdmin,
) -> SessionEvaluation:
    """회기 전체 deep-loop 재평가(슈퍼바이저 rationale/critique + 개선점 + 대안발화).

    저장된 마스킹 축어록을 evaluator.evaluate_session 으로 평가한다.
    엔진 장애는 503 으로 변환(평가는 비치명적이지만 트리거는 사용자 명시 요청이라 에러 노출).
    """
    sess = await _load_session_or_404(session_id, principal)
    masked = sess.masked_turns()
    # 발화 seq 보강(deep 프롬프트 가독성 — store 가 seq 미포함이라 인덱스로 부여)
    enriched: list[dict[str, Any]] = []
    for i, t in enumerate(masked):
        item = dict(t)
        item.setdefault("seq", i)
        enriched.append(item)

    # 누적 기법 코드 — DB 미가용이라 fast 결과가 없으면 빈 분포(deep LLM 정성 평가는 그대로 유효).
    technique_codes: list[str] = []

    try:
        result = await evaluator.evaluate_session(
            session_id=session_id,
            stage=sess.state.stage.value,
            masked_turns=enriched,
            engine=engine_client,
            technique_codes=technique_codes,
            theory_mode=_theory_mode_of(sess),
            scope=body.scope if body.scope in ("session_end", "stage_transition") else "session_end",
            audit_hook=session_persistence.record_llm_call_audit,
        )
    except EngineError as e:
        detail = f"engine unavailable: {e}"
        write = session_persistence.SessionEvaluationWrite.from_error(
            session_id=session_id,
            learner_id=sess.learner_id,
            scope=body.scope if body.scope in ("session_end", "stage_transition") else "session_end",
            stage=sess.state.stage.value,
            error=detail,
        )
        saved = await session_persistence.save_session_evaluation(write)
        if not saved:
            logger.error(
                "session evaluation retry error record did not reach durable store: session_id=%s error=%s",
                session_id,
                write.error,
            )
        raise HTTPException(status.HTTP_503_SERVICE_UNAVAILABLE, detail=detail)

    write = session_persistence.SessionEvaluationWrite.from_result(
        session_id=session_id,
        learner_id=sess.learner_id,
        result=result,
    )
    saved = await session_persistence.save_session_evaluation(write)
    if not saved:
        detail = "session evaluation retry result was generated but could not be saved"
        logger.error("%s: session_id=%s status=%s", detail, session_id, write.status)
        raise HTTPException(status.HTTP_503_SERVICE_UNAVAILABLE, detail=detail)
    if result.error:
        raise HTTPException(_session_evaluation_error_status(result.error), detail=result.error)
    return result


# ════════════════════════════════════════════════════════════════════════════
# 단일 턴 fast-loop 재평가 트리거 (교수자/관리자)
# ════════════════════════════════════════════════════════════════════════════
@router.post("/sessions/{session_id}/turn", response_model=TurnEvaluationResponse)
async def reevaluate_turn(
    session_id: str,
    body: TurnReevaluateRequest,
    principal: TeacherOrAdmin,
) -> TurnEvaluation:
    """단일 상담자 발화 fast-loop 재평가(기법/내담자상태/적절성/의도이탈).

    저장된 축어록에서 해당 turn_seq 상담자 발화 + 직후 내담자 응답을 재구성해
    경량 TurnContext 로 evaluator.evaluate_turn 을 호출한다.
    """
    sess = await _load_session_or_404(session_id, principal)

    # 대상 상담자 발화 + 직후 내담자 응답 찾기
    target_idx: Optional[int] = None
    for i, tr in enumerate(sess.turns):
        if tr.speaker == "counselor" and tr.turn_seq == body.turn_seq:
            target_idx = i
            break
    if target_idx is None:
        raise HTTPException(
            status.HTTP_404_NOT_FOUND, detail=f"counselor turn_seq {body.turn_seq} not found"
        )

    learner = sess.turns[target_idx]
    client_reply = ""
    if target_idx + 1 < len(sess.turns) and sess.turns[target_idx + 1].speaker == "client":
        client_reply = sess.turns[target_idx + 1].text_masked

    # 평가용 경량 TurnContext 재구성(prepare_turn 의 결정론 산출과 동형). 엔진 호출 없음.
    from ..services.orchestrator import TurnContext, TurnMemory  # 지연 import(소유권 경계)

    recent = [
        {"speaker": tr.speaker, "text": tr.text_masked} for tr in sess.turns[max(0, target_idx - 4):target_idx]
    ]
    ctx = TurnContext(
        session_id=session_id,
        case_id=sess.case_id,
        persona=sess.persona,
        state_before=sess.state,
        learner_text_raw=learner.text,
        learner_text_masked=learner.text_masked,
        state_after=sess.state,  # 조회 시점 상태(정밀 재현은 DB 스냅샷 도입 시)
        memory=TurnMemory(recent_turns=recent),
        theory_mode=_theory_mode_of(sess),
    )

    result = await evaluator.evaluate_turn(
        ctx,
        client_reply,
        engine=engine_client,
        audit_hook=session_persistence.record_llm_call_audit,
    )
    if result.error and result.error.startswith("engine_error"):
        raise HTTPException(status.HTTP_503_SERVICE_UNAVAILABLE, detail=result.error)
    return result


# ════════════════════════════════════════════════════════════════════════════
# 회기 평가 조회 (교수자/관리자) — 마지막 deep 결과 + 분포
# ════════════════════════════════════════════════════════════════════════════
@router.get("/sessions/{session_id}/evaluation", response_model=EvaluationSummary)
async def get_session_evaluation(
    session_id: str,
    principal: TeacherOrAdmin,
) -> EvaluationSummary:
    """회기 평가 조회(읽기) — 저장된 마지막 deep 재평가 결과 + 기법 분포.

    아직 평가 트리거가 없었다면 deep=None + 빈 분포.
    """
    await _load_session_or_404(session_id, principal)
    record, durable = await session_persistence.load_session_evaluation(session_id, principal)
    if record is None:
        return EvaluationSummary(
            session_id=session_id,
            stage=None,
            status=None,
            error=None,
            durable=durable,
            deep=None,
            distribution={},
        )
    payload = record.get("payload")
    deep = payload if isinstance(payload, dict) else {}
    distribution = deep.get("distribution")
    return EvaluationSummary(
        session_id=session_id,
        stage=_summary_stage(record.get("stage") or deep.get("stage")),
        status=str(record.get("status") or "") or None,
        error=str(record.get("error") or "") or None,
        durable=durable,
        deep=deep,
        distribution=distribution if isinstance(distribution, dict) else {},
    )