main.py

from fastapi import FastAPI, Depends
from pydantic import BaseModel, Field
from typing import Dict, List, Optional
import models
from database import engine, SessionLocal
from sqlalchemy.orm import Session # 필수 import
from models import Article, AnalysisResult
from crossref_model import get_crossref_score_and_reason
from mismatch_model import calculate_mismatch_score  # <-- 팀원 함수 불러오기
from aggro_model import get_aggro_score

models.Base.metadata.create_all(bind=engine)
from database import SessionLocal # database.py에서 정의한 SessionLocal 가져오기
# DB 세션을 열고 닫는 Dependency 함수 정의
def get_db():
    db = SessionLocal()
    try:
        yield db
    finally:
        db.close()

# --- 1. API 명세서 (Request/Response) ---
# Pydantic 모델, API 명세 초안

class ArticleRequest(BaseModel):
    """프론트가 보낼 요청 형식"""
    article_title: str = Field(..., example="기사 제목이 여기 들어갑니다")
    article_body: str = Field(..., example="기사 본문 텍스트...")

class FoundURL(BaseModel):
    """SBERT가 검증한 URL 객체"""
    url: str
    similarity: float = Field(..., example=0.85)

class ScoreBreakdown(BaseModel):
    """개별 점수 상세내역 형식"""
    score: float = Field(..., example=0.95)
    reason: str = Field(..., example="'충격' 키워드 사용")
    recommendation: str = Field(..., example="중립적인 단어로 수정하세요.")
    found_urls: Optional[List[FoundURL]] = None

class AnalysisResponse(BaseModel):
    """백엔드->프론트 최종 응답 형식"""
    final_risk_score: float = Field(..., example=0.82)
    final_risk_level: str = Field(..., example="위험")
    breakdown: Dict[str, ScoreBreakdown]

# --- 2. FastAPI 앱 생성 ---
app = FastAPI()

# --- 3. API 엔드포인트 ---

@app.post("/api/v1/analyze", response_model=AnalysisResponse)
def analyze_article(request: ArticleRequest,db: Session = Depends(get_db)):
    """기사 분석 API"""

    # 1. AggroScore
    aggro_result = get_aggro_score(request.article_title)

    real_aggro = ScoreBreakdown(
        score=aggro_result["score"],
        reason=aggro_result["reason"],
        recommendation=aggro_result["recommendation"],
        found_urls=None
    )
    
    # 2. mismatch
    mismatch_result = calculate_mismatch_score(request.article_title, request.article_body)

    real_mismatch = ScoreBreakdown(
        score=mismatch_result["score"],
        reason=mismatch_result["reason"],
        recommendation=mismatch_result["recommendation"],
        found_urls=None
    )

    # 3. crossref
    real_crossref_data = get_crossref_score_and_reason(request.article_body)
    SIMILARITY_THRESHOLD = 0.7 # 70% 이상 일치하는 것만 보여주기
    # 유사도가 높은 순으로 정렬
    sorted_urls = sorted(
        real_crossref_data["paired_results"], 
        key=lambda x: x["similarity"], 
        reverse=True
    )
    # 임계값(THRESHOLD) 이상의 URL만 필터링
    filtered_urls = [
        FoundURL(url=item["url"], similarity=item["similarity"]) 
        for item in sorted_urls 
        if item["similarity"] >= SIMILARITY_THRESHOLD
    ]
    # 최종 CrossrefScore 객체 생성 (필터링된 URL 포함)
    final_crossref = ScoreBreakdown(
        score=real_crossref_data["score"],
        reason=real_crossref_data["reason"],
        recommendation=real_crossref_data["recommendation"],
        found_urls=filtered_urls
    )

    # 최종 위험도 계산
    w_aggro = 0.2
    w_mismatch = 0.5
    w_crossref = 0.3
    
    final_score = (real_aggro.score * w_aggro) + \
                  (real_mismatch.score * w_mismatch) + \
                  (final_crossref.score * w_crossref)
    
    final_level = "안전"
    if final_score >= 0.7:
        final_level = "위험"
    elif final_score >= 0.4:
        final_level = "주의"
# ------------------------------------------------
    # 🛑 [핵심 추가] DB 저장 로직 시작
    # ------------------------------------------------

    # 1. 'articles' 테이블에 기사 저장
    new_article = Article(
        title=request.article_title,
        body=request.article_body,
        source="Swagger UI Test" # 테스트용 출처 입력
    )
    db.add(new_article)
    
    # 2. article_id를 얻기 위해 Flush (아직 commit은 하지 않음)
    db.flush() 
    
    # 3. 'analysis_results' 테이블에 분석 결과 저장
    new_result = AnalysisResult(
        article_id=new_article.article_id, # 외래 키 연결
        aggro_score=real_aggro.score,
        mismatch_score=real_mismatch.score,
        crossref_score=final_crossref.score,
        final_risk=final_score,
    )
    db.add(new_result)
    
    # 4. 모든 변경 사항을 DB에 영구 저장 (트랜잭션 완료)
    db.commit()
    
    # ------------------------------------------------
    # API 명세서(AnalysisResponse) 형식에 맞춰서 반환
    return AnalysisResponse(
        final_risk_score=round(final_score, 4), # 소수점 4자리로 반올림
        final_risk_level=final_level,
        breakdown={
            "aggro_score": real_aggro,
            "mismatch_score": real_mismatch,
            "crossref_score": final_crossref
        }
    )

@app.get("/")
def read_root():
    return {"message": "AI 기사 분석 서버"}