"""Route for real-time single-sample RAGAS scoring (Dify external Tool endpoint)."""

from __future__ import annotations

import time
from typing import Annotated

from fastapi import APIRouter, Header, HTTPException

from rag_eval.metrics.weights import compute_weighted_score
from rag_eval.settings import EvaluationSettings
from webapp.models import ScoreRequest, ScoreResponse
from webapp.services.inline_scorer import inline_scorer

router = APIRouter(prefix="/api/score", tags=["score"])


def _get_settings() -> EvaluationSettings:
    """Return a fresh EvaluationSettings instance (overridable in tests)."""
    return EvaluationSettings()


def _check_auth(authorization: str | None, token: str) -> None:
    """Raise 401 if Bearer token does not match the configured token."""
    if authorization is None:
        raise HTTPException(status_code=401, detail="Missing Authorization header.")
    parts = authorization.split(" ", 1)
    if len(parts) != 2 or parts[0].lower() != "bearer" or parts[1] != token:
        raise HTTPException(status_code=401, detail="Invalid Bearer token.")


@router.post(
    "",
    response_model=ScoreResponse,
    summary="单题实时评分（Dify 外部 Tool）",
    responses={
        200: {"description": "各指标得分和加权综合得分。"},
        401: {"description": "配置了 SCORE_API_TOKEN 但未提供有效 Bearer token。"},
        422: {"description": "请求参数校验失败。"},
    },
)
def score_sample(
    request: ScoreRequest,
    authorization: Annotated[str | None, Header()] = None,
) -> ScoreResponse:
    """Accept one QA sample, run RAGAS metrics synchronously, and return scores."""
    settings = _get_settings()

    # Require Bearer auth only when the deployment configured a shared token.
    if settings.score_api_token:
        _check_auth(authorization, settings.score_api_token)

    judge_model = request.judge_model or settings.ragas_judge_model
    embedding_model = request.embedding_model or settings.ragas_embedding_model
    effective = request.effective_metrics()
    requested = set(request.metrics)
    skipped = sorted(requested - set(effective))

    if not effective:
        return ScoreResponse(
            scores={metric_name: None for metric_name in request.metrics},
            weighted_score=None,
            latency_ms=0,
            skipped_metrics=skipped,
        )

    t0 = time.monotonic()
    try:
        raw_scores = inline_scorer.score(
            question=request.question,
            answer=request.answer,
            contexts=request.contexts_as_list(),
            ground_truth=request.ground_truth,
            metrics=effective,
            judge_model=judge_model,
            embedding_model=embedding_model,
            settings=settings,
        )
    except Exception as exc:  # noqa: BLE001
        latency_ms = int((time.monotonic() - t0) * 1000)
        return ScoreResponse(
            scores={},
            weighted_score=None,
            latency_ms=latency_ms,
            skipped_metrics=skipped,
            error=f"{type(exc).__name__}: {exc}",
        )

    latency_ms = int((time.monotonic() - t0) * 1000)

    # Keep skipped metrics visible to callers by emitting them as null scores.
    all_scores: dict[str, float | None] = {metric_name: None for metric_name in request.metrics}
    all_scores.update(raw_scores)

    weighted = compute_weighted_score(
        {key: value for key, value in raw_scores.items() if value is not None},
        {},
    )

    return ScoreResponse(
        scores=all_scores,
        weighted_score=round(weighted, 4) if weighted is not None else None,
        latency_ms=latency_ms,
        skipped_metrics=skipped,
    )