o2o-plagiarism-ai/app/api/routes.py

from __future__ import annotations

from datetime import datetime, timezone

from fastapi import APIRouter, BackgroundTasks, File, Form, HTTPException, Query, Request, UploadFile, status
from starlette.concurrency import run_in_threadpool

from app.api.schemas import (
    BatchCreatedResponse,
    BatchRequest,
    BatchStatusResponse,
    CorpusItem,
    CorpusListResponse,
    CorpusUploadRequest,
    CorpusUploadResponse,
    CopyrightAiItem,
    CopyrightCountItem,
    CopyrightLegalJudgment,
    CopyrightReviewResponse,
    CopyrightScoreCard,
    DetectRequest,
    DetectResponse,
    HealthResponse,
    PrecedentItem,
    PrecedentListResponse,
    SummaryRequest,
    SummaryResponse,
    TaxonomyResponse,
)
from app.core.config import get_settings
from app.engine.corpus import add_document, delete_document, list_documents
from app.engine.detector import PlagiarismDetector
from app.engine.summarizer import get_summarizer
from app.jobs.store import JobStore

router = APIRouter(prefix="/v1")


def _detector(request: Request) -> PlagiarismDetector:
    return request.app.state.detector


def _job_store(request: Request) -> JobStore:
    return request.app.state.job_store


@router.get("/health", response_model=HealthResponse, tags=["meta"])
async def health(request: Request) -> HealthResponse:
    settings = get_settings()
    det: PlagiarismDetector = request.app.state.detector
    taxonomy_version = None
    if det.taxonomy:
        taxonomy_version = f"meta_tags_{det.taxonomy.meta_tags_version}, cases_{det.taxonomy.cases_version}"
    return HealthResponse(
        status="ok",
        engine_version=settings.engine_version,
        corpus_size=det.corpus_size,
        taxonomy_version=taxonomy_version,
        autobiography_mode=settings.autobiography_mode,
        corpus_documents=det.corpus_document_count,
        index_backend=det.index_backend,
        ai_model_ready=getattr(det, "ai_model_ready", False),
        precedent_count=det.precedent_count,
    )


@router.get("/taxonomy", response_model=TaxonomyResponse, tags=["meta"])
async def taxonomy(request: Request) -> TaxonomyResponse:
    """분류체계 조회 - 컴북스/바이칼이 동일 라벨링 공유용."""
    det: PlagiarismDetector = request.app.state.detector
    if not det.taxonomy:
        raise HTTPException(status_code=503, detail="Taxonomy not loaded")
    return TaxonomyResponse(
        meta_tags_version=det.taxonomy.meta_tags_version,
        cases_version=det.taxonomy.cases_version,
        meta_tags=[
            {"id": t.id, "label_ko": t.label_ko, "category": t.category,
             "law_ref": t.law_ref, "scope": t.scope, "description": t.description}
            for t in det.taxonomy.meta_tags
        ],
        cases=[
            {"case_id": c.case_id, "old_no": c.old_no, "subgroup": c.subgroup,
             "handling": c.handling,
             "publication_verdict": c.publication_verdict,
             "representative_precedents": [
                 {"case_id": p.case_id, "in_runtime_corpus": p.in_runtime_corpus}
                 for p in c.representative_precedents
             ],
             "representative_precedent_note": c.representative_precedent_note,
             "title": c.title, "actor": c.actor,
             "primary_tags": list(c.primary_tags), "secondary_tags": list(c.secondary_tags),
             "detectable_internal": c.detectable_internal, "high_risk": c.high_risk,
             "note": c.note}
            for c in det.taxonomy.cases
        ],
    )


@router.post(
    "/plagiarism/detect",
    response_model=DetectResponse,
    tags=["plagiarism"],
)
async def detect(req: DetectRequest, request: Request) -> DetectResponse:
    # 형태소/임베딩/행렬 연산으로 event loop가 막히지 않도록 worker thread에서 실행.
    return await run_in_threadpool(_detector(request).detect_request, req)


@router.post(
    "/plagiarism/review",
    response_model=CopyrightReviewResponse,
    tags=["plagiarism"],
    summary="저작권 탭 경량 검사",
)
async def copyright_review(
    req: DetectRequest, request: Request,
) -> CopyrightReviewResponse:
    """상세 탐지는 수행하되 저작권 탭에 필요한 필드만 작게 반환한다."""
    result = await run_in_threadpool(_detector(request).detect_request, req)
    review = result.review_summary
    legal = result.legal_risk
    if review is None or legal is None:
        raise HTTPException(status_code=503, detail="Copyright review is unavailable")

    ai_labels = {
        "low": "낮음",
        "medium": "중간",
        "high": "높음",
        "unknown": "확인 불가",
    }
    if legal.llm_verdict == "likely":
        legal_status, legal_label = "suspected", "판례에 비추어 저작권 침해 의심"
    elif (
        legal.llm_verdict == "unlikely"
        or legal.status == "no_registered_corpus_match"
    ):
        legal_status, legal_label = "low", "등록 판례 기준 침해 의심 낮음"
    elif legal.status == "insufficient_precedent_data":
        legal_status, legal_label = "unavailable", "판례 검토 불가"
    else:
        legal_status, legal_label = "review_required", "관련 판례에 따른 추가 검토 필요"

    precedent_ids = list(legal.llm_matched_precedent_ids or legal.precedent_ids)
    suspicion_text = (
        "표절 의심 구간이 확인되었습니다."
        if review.has_suspicion
        else "표절 의심 구간이 없습니다."
    )
    judgment_summary = legal.judgment_summary
    if req.options.audience == "author":
        legal_status, legal_label = "review_required", "확인이 필요한 부분"
        precedent_ids = []
        judgment_summary = ("일치 구간을 확인해 주세요." if review.has_suspicion
                            else "등록된 비교 자료에서 일치 구간을 찾지 못했습니다.")
        suspicion_text = judgment_summary
    return CopyrightReviewResponse(
        doc_id=result.doc_id,
        copyright=CopyrightScoreCard(
            originality_percent=review.originality_percent,
            similarity_percent=review.similarity_percent,
            compared_count=review.compared_count,
            has_suspicion=review.has_suspicion,
            description=(
                f"등록 원문 검색 세그먼트 {review.compared_count:,}건과 대조한 결과 "
                f"{suspicion_text}"
            ),
        ),
        similar_sentences=CopyrightCountItem(
            count=review.similar_sentence_count,
            label=f"{review.similar_sentence_count}건",
        ),
        ai_generation_suspicion=CopyrightAiItem(
            level=review.ai_suspicion_level,
            label=ai_labels[review.ai_suspicion_level],
        ),
        legal_judgment=CopyrightLegalJudgment(
            status=legal_status,
            label=legal_label,
            summary=judgment_summary,
            precedent_ids=precedent_ids,
        ),
        analyzed_at=result.analyzed_at,
    )


@router.post(
    "/summary",
    response_model=SummaryResponse,
    tags=["summary"],
)
async def summarize(req: SummaryRequest) -> SummaryResponse:
    """스토리 요약/분석 (계획서 과제2 ②). 추출적 요약은 외부 의존 0으로 동작.

    use_abstractive=True 이고 LLM 키가 있으면 통합(hybrid) 요약, 아니면 추출적 요약 반환.
    """
    settings = get_settings()
    result = get_summarizer(settings).summarize(
        req.text,
        ratio=req.ratio,
        max_sentences=req.max_sentences,
        use_abstractive=req.use_abstractive,
        detail=req.detail,
        emphasis=req.emphasis,
    )
    return SummaryResponse(
        extractive=result.extractive,
        abstractive=result.abstractive,
        final=result.final,
        mode=result.mode,
        selected_indices=result.selected_indices,
        num_sentences_in=result.num_sentences_in,
        num_sentences_out=result.num_sentences_out,
        detail=req.detail,
        emphasis=req.emphasis,
        engine_version=settings.engine_version,
    )


@router.post(
    "/plagiarism/batch",
    response_model=BatchCreatedResponse,
    status_code=status.HTTP_202_ACCEPTED,
    tags=["plagiarism"],
)
async def batch_create(
    req: BatchRequest,
    request: Request,
    background_tasks: BackgroundTasks,
) -> BatchCreatedResponse:
    store = _job_store(request)
    detector = _detector(request)
    job = store.create(total=len(req.items))
    background_tasks.add_task(_run_batch, store, detector, job.job_id, req)
    return BatchCreatedResponse(
        job_id=job.job_id,
        status=job.status,
        total=job.total,
        created_at=job.created_at,
    )


@router.get(
    "/plagiarism/batch/{job_id}",
    response_model=BatchStatusResponse,
    tags=["plagiarism"],
)
async def batch_status(job_id: str, request: Request) -> BatchStatusResponse:
    job = _job_store(request).get(job_id)
    if not job:
        raise HTTPException(status_code=404, detail="Job not found")
    return BatchStatusResponse(
        job_id=job.job_id,
        status=job.status,
        total=job.total,
        processed=job.processed,
        created_at=job.created_at,
        finished_at=job.finished_at,
        results=job.results if job.status == "completed" else None,
        error=job.error,
    )


# ---------- 판례 조회 ----------

@router.get(
    "/precedents",
    response_model=PrecedentListResponse,
    tags=["precedents"],
)
async def precedent_list(
    request: Request,
    q: str = Query(default="", max_length=200),
    grade: str | None = Query(default=None, pattern="^(A|B|C|unreviewed)$"),
    work_type: str | None = Query(default=None, max_length=30),
    offset: int = Query(default=0, ge=0),
    limit: int = Query(default=25, ge=1, le=100),
) -> PrecedentListResponse:
    """엔진이 검색 후보로 사용하는 전체 판례를 조회한다."""
    precedents = list(_detector(request).precedents)
    needle = q.strip().lower()

    def matches(p) -> bool:
        if grade == "unreviewed" and p.grade is not None:
            return False
        if grade in {"A", "B", "C"} and p.grade != grade:
            return False
        if work_type and p.work_types and work_type not in p.work_types:
            return False
        if needle:
            haystack = " ".join([
                p.case_id, p.title, *p.work_types, *p.legal_tags,
                *p.criteria, p.holding_summary,
            ]).lower()
            if needle not in haystack:
                return False
        return True

    filtered = [p for p in precedents if matches(p)]
    page = filtered[offset:offset + limit]
    return PrecedentListResponse(
        total=len(filtered),
        loaded_total=len(precedents),
        graded_total=sum(p.grade is not None for p in precedents),
        offset=offset,
        limit=limit,
        items=[
            PrecedentItem(
                case_id=p.case_id,
                title=p.title,
                source_url=p.source_url,
                work_types=list(p.work_types),
                legal_tags=list(p.legal_tags),
                criteria=list(p.criteria),
                grade=p.grade,
                holding_excerpt=(
                    p.holding_summary
                    if len(p.holding_summary) <= 800
                    else p.holding_summary[:800].rstrip() + "…"
                ),
            )
            for p in page
        ],
    )


# ---------- 코퍼스 관리 ----------

def _rebuild(request: Request) -> int:
    from app.main import rebuild_detector
    return rebuild_detector(request.app)


def _persistent_add_locked(request: Request, detector, doc_id, title, text) -> str:
    with request.app.state.detector_lock:
        return detector.add_persistent_document(doc_id, title, text)


def _persistent_delete_locked(request: Request, detector, doc_id: str) -> bool:
    with request.app.state.detector_lock:
        return detector.delete_persistent_document(doc_id)


@router.get(
    "/corpus",
    response_model=CorpusListResponse,
    tags=["corpus"],
)
async def corpus_list(request: Request) -> CorpusListResponse:
    settings = get_settings()
    detector = _detector(request)
    if detector.uses_persistent_index:
        docs = [
            {
                "doc_id": d["document_id"], "title": d["title"],
                "size_bytes": d["characters"], "filename": d["source_path"],
            }
            for d in detector.list_persistent_documents()
        ]
    else:
        docs = list_documents(settings.corpus_path)
    return CorpusListResponse(
        total=len(docs),
        docs=[CorpusItem(**d) for d in docs],
    )


@router.post(
    "/corpus",
    response_model=CorpusUploadResponse,
    status_code=status.HTTP_201_CREATED,
    tags=["corpus"],
)
async def corpus_upload_json(req: CorpusUploadRequest, request: Request) -> CorpusUploadResponse:
    """JSON으로 자서전 1건 업로드. 인덱스 자동 재빌드."""
    settings = get_settings()
    detector = _detector(request)
    if detector.uses_persistent_index:
        try:
            document_id = await run_in_threadpool(
                _persistent_add_locked, request, detector, req.doc_id, req.title, req.text
            )
        except FileExistsError as e:
            raise HTTPException(status_code=409, detail=str(e))
        except ValueError as e:
            raise HTTPException(status_code=400, detail=str(e))
        return CorpusUploadResponse(
            doc_id=document_id, title=req.title,
            size_bytes=len(req.text.encode("utf-8")),
            corpus_size_after=detector.corpus_size, rebuilt=False,
        )
    try:
        doc = add_document(settings.corpus_path, req.doc_id, req.title, req.text)
    except FileExistsError as e:
        raise HTTPException(status_code=409, detail=str(e))
    except ValueError as e:
        raise HTTPException(status_code=400, detail=str(e))

    new_size = await run_in_threadpool(_rebuild, request)
    return CorpusUploadResponse(
        doc_id=doc.doc_id, title=doc.title,
        size_bytes=len(doc.text.encode("utf-8")),
        corpus_size_after=new_size, rebuilt=True,
    )


@router.post(
    "/corpus/file",
    response_model=CorpusUploadResponse,
    status_code=status.HTTP_201_CREATED,
    tags=["corpus"],
)
async def corpus_upload_file(
    request: Request,
    title: str = Form(..., description="자서전 제목"),
    doc_id: str | None = Form(default=None, description="비우면 자동 생성"),
    file: UploadFile = File(..., description=".txt 파일"),
) -> CorpusUploadResponse:
    """multipart로 .txt 파일 업로드 (큰 자서전 파일용)."""
    settings = get_settings()
    raw = await file.read()
    try:
        text = raw.decode("utf-8")
    except UnicodeDecodeError:
        raise HTTPException(status_code=400, detail="UTF-8 인코딩 텍스트 파일만 업로드 가능합니다.")

    detector = _detector(request)
    if detector.uses_persistent_index:
        try:
            document_id = await run_in_threadpool(
                _persistent_add_locked, request, detector, doc_id, title, text
            )
        except FileExistsError as e:
            raise HTTPException(status_code=409, detail=str(e))
        except ValueError as e:
            raise HTTPException(status_code=400, detail=str(e))
        return CorpusUploadResponse(
            doc_id=document_id, title=title, size_bytes=len(raw),
            corpus_size_after=detector.corpus_size, rebuilt=False,
        )

    try:
        doc = add_document(settings.corpus_path, doc_id, title, text)
    except FileExistsError as e:
        raise HTTPException(status_code=409, detail=str(e))
    except ValueError as e:
        raise HTTPException(status_code=400, detail=str(e))

    new_size = await run_in_threadpool(_rebuild, request)
    return CorpusUploadResponse(
        doc_id=doc.doc_id, title=doc.title,
        size_bytes=len(doc.text.encode("utf-8")),
        corpus_size_after=new_size, rebuilt=True,
    )


@router.delete(
    "/corpus/{doc_id}",
    status_code=status.HTTP_204_NO_CONTENT,
    tags=["corpus"],
)
async def corpus_delete(doc_id: str, request: Request) -> None:
    settings = get_settings()
    detector = _detector(request)
    if detector.uses_persistent_index:
        deleted = await run_in_threadpool(
            _persistent_delete_locked, request, detector, doc_id
        )
        if not deleted:
            raise HTTPException(status_code=404, detail=f"doc_id '{doc_id}' not found")
        return
    if not delete_document(settings.corpus_path, doc_id):
        raise HTTPException(status_code=404, detail=f"doc_id '{doc_id}' not found")
    await run_in_threadpool(_rebuild, request)


def _run_batch(store: JobStore, detector: PlagiarismDetector, job_id: str, req: BatchRequest) -> None:
    store.update(job_id, status="running")
    try:
        for item in req.items:
            result = detector.detect(
                doc_id=item.doc_id,
                text=item.text,
                metadata=item.metadata,
                options=req.options,
                include_ai_segments=False,
            )
            store.append_result(job_id, result)
        store.update(job_id, status="completed", finished_at=datetime.now(timezone.utc))
    except Exception as exc:
        store.update(
            job_id,
            status="failed",
            finished_at=datetime.now(timezone.utc),
            error=str(exc),
        )