A1: corp_brand_resolver JWT 자동 통합 (회귀 fix)

backend/src/main.py

@@ -47,7 +47,7 @@
 # LangSmith 트레이싱: langchain import 전에 os.environ 주입 필수
 # (langchain SDK는 import 시점에 LANGCHAIN_TRACING_V2를 읽으므로 순서가 중요)
 from dotenv import load_dotenv
-from fastapi import FastAPI, HTTPException, Request, Response
+from fastapi import Depends, FastAPI, HTTPException, Request, Response
 from fastapi.concurrency import run_in_threadpool
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse
@@ -78,6 +78,7 @@
 from src.services.auth import AuthService
 from src.services.biz_mapper import BizMapper
 from src.services.corp_brand_resolver import resolve_brand_for_industry
+from src.services.jwt_auth import UserContext, get_optional_user
 
 from models.explainability.shap_analysis import explain_tcn_prediction
 from models.explainability.simulation import (
@@ -226,28 +227,56 @@ def _pipeline_key(input_data: Any) -> str:
     return f"{input_data.target_district}:{input_data.business_type}:{input_data.brand_name}:{rent}:{area}:{radius}:{pop_w}"
 
 
-def _validate_and_resolve_brand(input_data: SimulationInput) -> None:
+def _resolve_user_biz_number(user: UserContext | None) -> str | None:
+    """JWT user → users.biz_number 조회. master 는 본인, manager 는 owner 의 biz_number."""
+    if user is None:
+        return None
+    target_id = user.owner_id if user.role == "manager" else user.user_id
+    if not target_id:
+        return None
+    try:
+        import sqlalchemy as sa
+
+        engine = sa.create_engine(settings.postgres_url)
+        with engine.connect() as conn:
+            row = conn.execute(
+                sa.text("SELECT biz_number FROM users WHERE id = :id"),
+                {"id": target_id},
+            ).first()
+        return row._mapping["biz_number"] if row else None
+    except Exception as ex:
+        logger.warning(f"[brand_resolver] biz_number 조회 실패: {ex}")
+        return None
+
+
+def _validate_and_resolve_brand(
+    input_data: SimulationInput,
+    current_user: UserContext | None = None,
+) -> None:
     """biz_number 입력 시 corp 검증 + 다업종 corp 의 brand auto-resolve.
 
-    동작 (input_data.biz_number 가 입력됐을 때만):
+    biz_number 우선순위:
+    1. ``input_data.biz_number`` (frontend 명시 입력)
+    2. JWT ``current_user`` 토큰에서 자동 추출 (master.user_id 또는 manager.owner_id)
+    3. 없으면 검증 skip (개인사업자 / 비회원 호환)
+
+    동작:
     1. business_type 이 사용자 corp 의 운영 업종인지 검증.
     2. 운영 외 업종 → HTTPException(400) + 운영 가능 업종 list 응답.
     3. 운영 내 업종 + corp 의 해당 업종 brand 가 다른 brand 면 brand_name override.
-
-    biz_number 미입력 (개인사업자 / 비회원) → 검증 skip, 사용자 brand_name 그대로.
-    FTC 미등록 corp → 검증 skip + 경고 로그.
     """
-    if not input_data.biz_number:
+    biz_number = input_data.biz_number or _resolve_user_biz_number(current_user)
+    if not biz_number:
         return
 
-    result = resolve_brand_for_industry(input_data.biz_number, input_data.business_type)
+    result = resolve_brand_for_industry(biz_number, input_data.business_type)
 
     if result.get("error") == "INDUSTRY_NOT_OPERATED":
         raise HTTPException(status_code=400, detail=result)
 
     if result.get("error") in {"USER_NOT_FOUND", "CORP_NOT_IN_FTC", "INVALID_COMPANY_NAME"}:
         # 비회원 / FTC 미등록 → 검증 skip, 사용자 brand_name 그대로
-        logger.warning(f"[brand_resolver] {result['error']} biz={input_data.biz_number} — fallback to input.brand_name")
+        logger.warning(f"[brand_resolver] {result['error']} biz={biz_number} — fallback to input.brand_name")
         return
 
     # 성공: brand_name override (사용자가 다른 brand 입력했어도 corp 정합 brand 로 교체)
@@ -963,14 +992,18 @@ async def get_status(job_id: str):
 
 
 @app.post("/analyze")
-async def analyze_location(input_data: SimulationInput, response: Response):
+async def analyze_location(
+    input_data: SimulationInput,
+    response: Response,
+    current_user: UserContext | None = Depends(get_optional_user),
+):
     """[DEPRECATED] 풀파이프 상권 분석 — 전환 기간 동안만 유지.
 
     IM3-259로 endpoint를 분리(/predict + /analyze/llm)했으므로 신규 호출은
     그쪽으로 옮길 것. 이 endpoint는 기존 프론트/테스트 호환을 위해 유지하다가
     충분히 검증되면 제거 예정.
     """
-    _validate_and_resolve_brand(input_data)
+    _validate_and_resolve_brand(input_data, current_user)
     from src.config.constants import MAPO_DISTRICTS
 
     # IM3-259: deprecation 헤더 — 클라이언트가 /predict + /analyze/llm 으로 옮길 것을 알림
@@ -1021,12 +1054,15 @@ async def analyze_location(input_data: SimulationInput, response: Response):
 
 
 @app.post("/analyze/llm")
-async def analyze_llm(input_data: SimulationInput):
+async def analyze_llm(
+    input_data: SimulationInput,
+    current_user: UserContext | None = Depends(get_optional_user),
+):
     """AI 분석 전용 endpoint — slow_graph 실행 (~80-140초).
 
     /predict와 독립 병렬 호출 가능. winner는 ranking 단계에서 자체 결정.
     """
-    _validate_and_resolve_brand(input_data)
+    _validate_and_resolve_brand(input_data, current_user)
     from src.config.constants import MAPO_DISTRICTS
     from src.schemas.simulation_output import AnalysisOutput
 
@@ -1094,9 +1130,12 @@ async def analyze_llm(input_data: SimulationInput):
 
 
 @app.post("/analyze/llm/async")
-async def analyze_llm_async(input_data: SimulationInput) -> dict[str, Any]:
+async def analyze_llm_async(
+    input_data: SimulationInput,
+    current_user: UserContext | None = Depends(get_optional_user),
+) -> dict[str, Any]:
     """AI 분석 비동기 시작 — 즉시 job_id 반환. LangGraph 노드별 진행률 추적."""
-    _validate_and_resolve_brand(input_data)
+    _validate_and_resolve_brand(input_data, current_user)
     from src.config.constants import MAPO_DISTRICTS
     from src.schemas.simulation_output import AnalysisOutput
     from src.services.job_progress_store import (
@@ -1208,7 +1247,10 @@ async def analyze_llm_job_status(job_id: str) -> dict[str, Any]:
 
 
 @app.post("/analyze/quick")
-async def analyze_quick(input_data: SimulationInput):
+async def analyze_quick(
+    input_data: SimulationInput,
+    current_user: UserContext | None = Depends(get_optional_user),
+):
     """
     LLM 없는 경량 랭킹 엔드포인트 (district_ranking 에이전트만 실행).
 
@@ -1217,7 +1259,7 @@ async def analyze_quick(input_data: SimulationInput):
 
     응답: { district_rankings, winner_district, top_3_candidates }
     """
-    _validate_and_resolve_brand(input_data)
+    _validate_and_resolve_brand(input_data, current_user)
     from src.agents.nodes.district_ranking import district_ranking_node
     from src.agents.nodes.market_analyst import db_client
 
@@ -1692,15 +1734,18 @@ def _mock_simulation_response(target_district: str, request_id: str) -> dict:
 
 
 @app.post("/predict")
-async def predict_districts(input_data: SimulationInput):
+async def predict_districts(
+    input_data: SimulationInput,
+    current_user: UserContext | None = Depends(get_optional_user),
+):
     """
     선택 동 1~4개 ML 예측 전용 엔드포인트 (LangGraph 미사용)
 
     - district_ranking, winner 로직 없음
     - target_districts 전체에 대해 TCN/BEP/폐업률/폐업위험도/SHAP 병렬 실행
     - 응답: 동별 예측 결과 리스트 (프론트 멀티라인 차트용)
     """
-    _validate_and_resolve_brand(input_data)
+    _validate_and_resolve_brand(input_data, current_user)
     from src.config.constants import MAPO_DISTRICTS
 
     target_districts = getattr(input_data, "target_districts", None) or [input_data.target_district]
@@ -1762,9 +1807,12 @@ async def predict_districts(input_data: SimulationInput):
 # 단계: 동별 _predict_single_district 가 끝날 때마다 progress = done/total.
 # ---------------------------------------------------------------------------
 @app.post("/predict/async")
-async def predict_districts_async(input_data: SimulationInput) -> dict[str, Any]:
+async def predict_districts_async(
+    input_data: SimulationInput,
+    current_user: UserContext | None = Depends(get_optional_user),
+) -> dict[str, Any]:
     """ML 예측 비동기 시작 — 즉시 job_id 반환. 진행률은 status endpoint 폴링."""
-    _validate_and_resolve_brand(input_data)
+    _validate_and_resolve_brand(input_data, current_user)
     from src.config.constants import MAPO_DISTRICTS
     from src.services.job_progress_store import (
         create_job,
@@ -1878,9 +1926,13 @@ async def predict_job_status(job_id: str) -> dict[str, Any]:
 
 
 @app.post("/simulate", deprecated=True)
-async def run_simulation(input_data: SimulationInput, response: Response):
+async def run_simulation(
+    input_data: SimulationInput,
+    response: Response,
+    current_user: UserContext | None = Depends(get_optional_user),
+):
     """기본 시뮬레이션 엔드포인트"""
-    _validate_and_resolve_brand(input_data)
+    _validate_and_resolve_brand(input_data, current_user)
     response.headers["Deprecation"] = "true"
     response.headers["Link"] = '</predict>; rel="successor-version", </analyze/llm>; rel="successor-version"'