Merge branch 'feature-BizInput' of https://gitea.o2o.kr/castad/o2o-castad-backend into feature-BizInput

app/home/api/routers/v1/home.py

@@ -26,7 +26,6 @@ from app.home.schemas.home_schema import (
     ImageUploadResultItem,
     ImageUrlItem,
     ManualMarketingRequest,
-    MarketingAnalysisResponse,
     ProcessedInfo,
     # MarketingAnalysis,
 )
@@ -135,31 +134,32 @@ METRO_CITY_MAP = {
 }
 
 
-def _extract_region_from_address(road_address: str | None) -> str:
+def _extract_region_from_address(road_address: str | None, jibun_address: str | None = None) -> str:
-    """roadAddress에서 시/군 이름 추출
+    """주소에서 시/군 이름 추출 — 도로명 우선, 실패 시 지번으로 재시도"""
-
-    매칭 우선순위:
-    1. KOREAN_CITIES 직접 매칭 (시/군 접미사 포함)
-    2. KOREAN_CITIES 접미사 생략 매칭
-    3. 주소 두 번째 토큰이 시/군으로 끝나는 경우 (예: "전북 군산시 ...")
-    4. 주소 두 번째 토큰이 구/동인 경우 → 첫 번째 토큰으로 광역시 매핑 (예: "서울 강남구 ...")
-    """
-    if not road_address:
-        return ""
 
+    def _parse(address: str) -> str:
+        token_set = set(address.split())
         for city in KOREAN_CITIES:
-        if city in road_address:
+            if city in token_set:           # 완전 일치 (토큰 단위)
                 return city
-        if city[:-1] in road_address:
+            if city[:-1] in token_set:      # 접미사 생략 일치 (토큰 단위)
                 return city
-
+        tokens = address.split()
-    tokens = road_address.split()
         if len(tokens) >= 2:
             second = tokens[1]
             if second.endswith("시") or second.endswith("군"):
                 return second
             if second.endswith("구") or second.endswith("동"):
                 return METRO_CITY_MAP.get(tokens[0], "")
+        return ""
+
+    if road_address:
+        result = _parse(road_address)
+        if result:
+            return result
+
+    if jibun_address:
+        return _parse(jibun_address)
 
     return ""
 
@@ -292,14 +292,15 @@ async def _crawling_logic(
     marketing_analysis = None
 
     if scraper.base_info:
-        road_address = scraper.base_info.get("roadAddress", "") or scraper.base_info.get("address", "")
+        road_address = scraper.base_info.get("roadAddress", "")
+        jibun_address = scraper.base_info.get("address", "")
         customer_name = scraper.base_info.get("name", "")
-        region = _extract_region_from_address(road_address)
+        region = _extract_region_from_address(road_address, jibun_address)
 
         processed_info = ProcessedInfo(
             customer_name=customer_name,
             region=region,
-            detail_region_info=road_address or "",
+            detail_region_info=road_address or jibun_address or "",
         )
 
         step2_elapsed = (time.perf_counter() - step2_start) * 1000
@@ -312,13 +313,30 @@ async def _crawling_logic(
         logger.info("[crawling] Step 3: ChatGPT 마케팅 분석 시작...")
 
         try:
-            marketing_analysis, m_id = await _run_marketing_analysis(
+            # Step 3-1: ChatGPT 서비스 초기화 및 입력 데이터 구성
-                customer_name=customer_name,
+            chatgpt_service = ChatgptService()
-                region=region,
+            input_marketing_data = {
-                detail_region_info=road_address or "",
+                "customer_name": customer_name,
-                place_id=scraper.place_id,
+                "region": region,
-                session=session,
+                "detail_region_info": road_address or "",
+            }
+
+            # Step 3-2: GPT API 호출 → 구조화된 마케팅 분석 결과 반환
+            marketing_analysis = await chatgpt_service.generate_structured_output(
+                marketing_prompt, input_marketing_data
             )
+
+            # Step 3-3: 분석 결과 DB 저장
+            marketing_intel = MarketingIntel(
+                place_id=scraper.place_id,
+                intel_result=marketing_analysis.model_dump(),
+            )
+            session.add(marketing_intel)
+            await session.commit()
+            await session.refresh(marketing_intel)
+            m_id = marketing_intel.id
+            logger.debug(f"[MarketingPrompt] INSERT place_id={marketing_intel.place_id} id={marketing_intel.id}")
+
             step3_elapsed = (time.perf_counter() - step3_start) * 1000
             logger.info(
                 f"[crawling] Step 3 완료 - 마케팅 분석 성공 ({step3_elapsed:.1f}ms)"
@@ -367,33 +385,6 @@ async def _crawling_logic(
     }
 
 
-async def _run_marketing_analysis(
-    customer_name: str,
-    region: str,
-    detail_region_info: str,
-    place_id: Optional[str],
-    session: AsyncSession,
-):
-    """ChatGPT 마케팅 분석 실행 → MarketingIntel 저장 → (MarketingPromptOutput, m_id) 반환"""
-    chatgpt_service = ChatgptService()
-    input_marketing_data = {
-        "customer_name": customer_name,
-        "region": region,
-        "detail_region_info": detail_region_info,
-    }
-    structured_report = await chatgpt_service.generate_structured_output(
-        marketing_prompt, input_marketing_data
-    )
-    marketing_intel = MarketingIntel(
-        place_id=place_id,
-        intel_result=structured_report.model_dump(),
-    )
-    session.add(marketing_intel)
-    await session.commit()
-    await session.refresh(marketing_intel)
-    logger.debug(f"[MarketingPrompt] INSERT place_id={marketing_intel.place_id} id={marketing_intel.id}")
-    return structured_report, marketing_intel.id
-
 
 @router.post(
     "/marketing",
@@ -410,13 +401,14 @@ async def _run_marketing_analysis(
 - **marketing_analysis**: ChatGPT 마케팅 분석 결과
 - **m_id**: 마케팅 분석 결과 ID (이후 영상생성 파이프라인에 사용)
     """,
-    response_model=MarketingAnalysisResponse,
+    response_model=CrawlingResponse,
     tags=["Marketing"],
 )
 async def manual_marketing(
     request_body: ManualMarketingRequest,
     session: AsyncSession = Depends(get_session),
 ):
+    # Step 1: 주소에서 지역명 추출 및 processed_info 구성
     region = _extract_region_from_address(request_body.address)
     processed_info = ProcessedInfo(
         customer_name=request_body.store_name,
@@ -424,13 +416,28 @@ async def manual_marketing(
         detail_region_info=request_body.address,
     )
     try:
-        marketing_analysis, m_id = await _run_marketing_analysis(
+        # Step 2: GPT API 호출 → 마케팅 분석 결과 생성
-            customer_name=request_body.store_name,
+        # place_id 없이 업체명+주소만으로 분석 (크롤링 없이 직접 입력된 경우)
-            region=region,
+        chatgpt_service = ChatgptService()
-            detail_region_info=request_body.address,
+        input_marketing_data = {
-            place_id=None,
+            "customer_name": request_body.store_name,
-            session=session,
+            "region": region,
+            "detail_region_info": request_body.address,
+        }
+        marketing_analysis = await chatgpt_service.generate_structured_output(
+            marketing_prompt, input_marketing_data
         )
+
+        # Step 3: 분석 결과 DB 저장 (place_id=None — 네이버 장소와 연결되지 않음)
+        marketing_intel = MarketingIntel(
+            place_id=None,
+            intel_result=marketing_analysis.model_dump(),
+        )
+        session.add(marketing_intel)
+        await session.commit()
+        await session.refresh(marketing_intel)
+        m_id = marketing_intel.id
+        logger.debug(f"[MarketingPrompt] INSERT id={marketing_intel.id}")
     except ChatGPTResponseError as e:
         logger.error(
             f"[marketing] ChatGPT Error: status={e.status}, "
@@ -447,7 +454,7 @@ async def manual_marketing(
             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             detail="마케팅 분석 중 오류가 발생했습니다.",
         )
-    return MarketingAnalysisResponse(
+    return CrawlingResponse(
         status="completed",
         processed_info=processed_info,
         marketing_analysis=marketing_analysis,

app/home/schemas/home_schema.py

@@ -234,7 +234,7 @@ class CrawlingResponse(BaseModel):
         description="처리 상태 (completed: 성공, failed: ChatGPT 분석 실패)"
     )
     image_list: Optional[list[str]] = Field(None, description="이미지 URL 목록")
-    image_count: int = Field(..., description="이미지 개수")
+    image_count: Optional[int] = Field(None, description="이미지 개수")
     processed_info: Optional[ProcessedInfo] = Field(
         None, description="가공된 장소 정보 (customer_name, region, detail_region_info)"
     )