From e56aa35170780b389e52eb6da7c5a61b389bf224 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=EA=B9=80=EC=84=B1=EA=B2=BD?= <skkim@o2o.kr>
Date: Tue, 2 Jun 2026 10:20:43 +0900
Subject: [PATCH] =?UTF-8?q?=EC=A7=81=EC=A0=91=EC=9E=85=EB=A0=A5=20?=
 =?UTF-8?q?=EA=B8=B0=EB=8A=A5?=
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---
 app/home/api/routers/v1/home.py | 195 +++++++++++++++++++-------------
 app/home/schemas/home_schema.py |  32 +++++-
 2 files changed, 148 insertions(+), 79 deletions(-)

diff --git a/app/home/api/routers/v1/home.py b/app/home/api/routers/v1/home.py
index baa6bf9..03fac5d 100644
--- a/app/home/api/routers/v1/home.py
+++ b/app/home/api/routers/v1/home.py
@@ -25,6 +25,7 @@ from app.home.schemas.home_schema import (
     ImageUploadResponse,
     ImageUploadResultItem,
     ImageUrlItem,
+    ManualMarketingRequest,
     ProcessedInfo,
     # MarketingAnalysis,
 )
@@ -133,31 +134,32 @@ METRO_CITY_MAP = {
 }
 
 
-def _extract_region_from_address(road_address: str | None) -> str:
-    """roadAddress에서 시/군 이름 추출
+def _extract_region_from_address(road_address: str | None, jibun_address: str | None = None) -> str:
+    """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:
+    def _parse(address: str) -> str:
+        token_set = set(address.split())
+        for city in KOREAN_CITIES:
+            if city in token_set:           # 완전 일치 (토큰 단위)
+                return city
+            if city[:-1] in token_set:      # 접미사 생략 일치 (토큰 단위)
+                return city
+        tokens = 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 ""
 
-    for city in KOREAN_CITIES:
-        if city in road_address:
-            return city
-        if city[:-1] in road_address:
-            return city
+    if road_address:
+        result = _parse(road_address)
+        if result:
+            return result
 
-    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], "")
+    if jibun_address:
+        return _parse(jibun_address)
 
     return ""
 
@@ -290,14 +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
@@ -310,70 +313,29 @@ async def _crawling_logic(
         logger.info("[crawling] Step 3: ChatGPT 마케팅 분석 시작...")
 
         try:
-            # Step 3-1: ChatGPT 서비스 초기화
-            step3_1_start = time.perf_counter()
+            # Step 3-1: ChatGPT 서비스 초기화 및 입력 데이터 구성
             chatgpt_service = ChatgptService()
-            step3_1_elapsed = (time.perf_counter() - step3_1_start) * 1000
-            logger.debug(
-                f"[crawling]   Step 3-1: 서비스 초기화 완료 ({step3_1_elapsed:.1f}ms)"
-            )
-
-            # Step 3-2: 프롬프트 생성
-            # step3_2_start = time.perf_counter()
             input_marketing_data = {
                 "customer_name": customer_name,
                 "region": region,
                 "detail_region_info": road_address or "",
             }
-            # prompt = chatgpt_service.build_market_analysis_prompt()
-            # prompt1 = marketing_prompt.build_prompt(input_marketing_data)
-            # step3_2_elapsed = (time.perf_counter() - step3_2_start) * 1000
 
-            # Step 3-3: GPT API 호출
-            step3_3_start = time.perf_counter()
-            structured_report = await chatgpt_service.generate_structured_output(
+            # Step 3-2: GPT API 호출 → 구조화된 마케팅 분석 결과 반환
+            marketing_analysis = await chatgpt_service.generate_structured_output(
                 marketing_prompt, input_marketing_data
-            ) 
-            marketing_intelligence = MarketingIntel(
-                place_id = scraper.place_id,
-                intel_result = structured_report.model_dump()
             )
-            session.add(marketing_intelligence)
+
+            # 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_intelligence)
-            m_id = marketing_intelligence.id
-            logger.debug(f"[MarketingPrompt] INSERT placeid {marketing_intelligence.place_id}")
-            step3_3_elapsed = (time.perf_counter() - step3_3_start) * 1000
-            logger.info(
-                f"[crawling]   Step 3-3: GPT API 호출 완료 - ({step3_3_elapsed:.1f}ms)"
-            )
-            logger.debug(
-                f"[crawling]   Step 3-3: GPT API 호출 완료 - ({step3_3_elapsed:.1f}ms)"
-            )
-
-            # Step 3-4: 응답 파싱 (크롤링에서 가져온 facility_info 전달)
-            step3_4_start = time.perf_counter()
-            logger.debug(
-                f"[crawling]   Step 3-4: 응답 파싱 시작 - facility_info: {scraper.facility_info}"
-            )
-
-            # 요약 Deprecated / 20250115 / Selling points를 첫 prompt에서 추출 중
-            # parsed = await chatgpt_service.parse_marketing_analysis(
-            #     structured_report, facility_info=scraper.facility_info
-            # )
-
-            # marketing_analysis = MarketingAnalysis(**parsed)
-
-            logger.debug(
-                f"structured_report = {structured_report.model_dump()}"
-            )
-
-            marketing_analysis = structured_report
-            
-            step3_4_elapsed = (time.perf_counter() - step3_4_start) * 1000
-            logger.debug(
-                f"[crawling]   Step 3-4: 응답 파싱 완료 ({step3_4_elapsed:.1f}ms)"
-            )
+            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(
@@ -426,6 +388,83 @@ async def _crawling_logic(
     }
 
 
+
+@router.post(
+    "/marketing",
+    summary="업체명+주소 직접 입력 마케팅 분석",
+    description="""
+네이버 크롤링 없이 업체명과 주소를 직접 입력받아 마케팅 분석을 수행합니다.
+
+## 요청 필드
+- **customer_name**: 업체명 / 브랜드명 (필수)
+- **address**: 도로명 또는 지번 주소 (필수)
+
+## 반환 정보
+- **processed_info**: 가공된 장소 정보 (customer_name, region, detail_region_info)
+- **marketing_analysis**: ChatGPT 마케팅 분석 결과
+- **m_id**: 마케팅 분석 결과 ID (이후 영상생성 파이프라인에 사용)
+    """,
+    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,
+        region=region,
+        detail_region_info=request_body.address,
+    )
+    try:
+        # Step 2: GPT API 호출 → 마케팅 분석 결과 생성
+        # place_id 없이 업체명+주소만으로 분석 (크롤링 없이 직접 입력된 경우)
+        chatgpt_service = ChatgptService()
+        input_marketing_data = {
+            "customer_name": request_body.store_name,
+            "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}, "
+            f"code={e.error_code}, message={e.error_message}"
+        )
+        raise HTTPException(
+            status_code=status.HTTP_502_BAD_GATEWAY,
+            detail=f"마케팅 분석 중 ChatGPT 오류가 발생했습니다: {e.error_message}",
+        )
+    except Exception as e:
+        logger.error(f"[marketing] 마케팅 분석 중 오류: {e}")
+        logger.exception("[marketing] 상세 오류:")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail="마케팅 분석 중 오류가 발생했습니다.",
+        )
+    return CrawlingResponse(
+        status="completed",
+        processed_info=processed_info,
+        marketing_analysis=marketing_analysis,
+        m_id=m_id,
+    )
+
+
 async def _autocomplete_logic(autocomplete_item:dict):
     step1_start = time.perf_counter()
     try:
diff --git a/app/home/schemas/home_schema.py b/app/home/schemas/home_schema.py
index e227da2..3c5d283 100644
--- a/app/home/schemas/home_schema.py
+++ b/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)"
     )
@@ -244,6 +244,36 @@ class CrawlingResponse(BaseModel):
     m_id : int = Field(..., description="마케팅 분석 결과 ID")
 
 
+class ManualMarketingRequest(BaseModel):
+    """업체명+주소 직접 입력 마케팅 분석 요청"""
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "store_name": "스테이 머뭄",
+                "address": "전북특별자치도 군산시 절골길 18",
+            }
+        }
+    )
+
+    store_name: str = Field(..., description="업체명 / 브랜드명")
+    address: str = Field(..., description="도로명 또는 지번 주소")
+
+
+class MarketingAnalysisResponse(BaseModel):
+    """업체명+주소 직접 입력 마케팅 분석 응답"""
+
+    status: str = Field(
+        default="completed",
+        description="처리 상태 (completed: 성공, failed: ChatGPT 분석 실패)",
+    )
+    processed_info: ProcessedInfo = Field(..., description="가공된 장소 정보")
+    marketing_analysis: Optional[MarketingPromptOutput] = Field(
+        None, description="마케팅 분석 결과. 실패 시 null"
+    )
+    m_id: int = Field(..., description="마케팅 분석 결과 ID")
+
+
 class ErrorResponse(BaseModel):
     """에러 응답 스키마"""