대시보드 예외처리 변경

.gitignore

@@ -52,3 +52,4 @@ Dockerfile
 .dockerignore
 
 zzz/
+credentials/service_account.json

app/core/exceptions.py

@@ -314,7 +314,10 @@ def add_exception_handlers(app: FastAPI):
 
     @app.exception_handler(DashboardException)
     def dashboard_exception_handler(request: Request, exc: DashboardException) -> Response:
-        logger.debug(f"Handled DashboardException: {exc.__class__.__name__} - {exc.message}")
+        if exc.status_code < 500:
+            logger.warning(f"Handled DashboardException: {exc.__class__.__name__} - {exc.message}")
+        else:
+            logger.error(f"Handled DashboardException: {exc.__class__.__name__} - {exc.message}")
         return JSONResponse(
             status_code=exc.status_code,
             content={

app/dashboard/api/routers/v1/dashboard.py

@@ -4,43 +4,22 @@ Dashboard API 라우터
 YouTube Analytics 기반 대시보드 통계를 제공합니다.
 """
 
-import json
 import logging
-from datetime import date, datetime, timedelta
 from typing import Literal
 
 from fastapi import APIRouter, Depends, Query
-from sqlalchemy import func, select
 from sqlalchemy.ext.asyncio import AsyncSession
 
-from app.dashboard.exceptions import (
+from app.dashboard.utils.redis_cache import delete_cache_pattern
-    YouTubeAccountNotConnectedError,
-    YouTubeAccountNotFoundError,
-    YouTubeAccountSelectionRequiredError,
-    YouTubeTokenExpiredError,
-)
 from app.dashboard.schemas import (
-    AudienceData,
     CacheDeleteResponse,
-    ConnectedAccount,
     ConnectedAccountsResponse,
-    ContentMetric,
     DashboardResponse,
-    TopContent,
-)
-from app.dashboard.services import DataProcessor, YouTubeAnalyticsService
-from app.dashboard.redis_cache import (
-    delete_cache,
-    delete_cache_pattern,
-    get_cache,
-    set_cache,
 )
+from app.dashboard.services import DashboardService
 from app.database.session import get_session
-from app.dashboard.models import Dashboard
-from app.social.exceptions import TokenExpiredError
-from app.social.services import SocialAccountService
 from app.user.dependencies.auth import get_current_user
-from app.user.models import SocialAccount, User
+from app.user.models import User
 
 logger = logging.getLogger(__name__)
 
@@ -61,41 +40,8 @@ async def get_connected_accounts(
     current_user: User = Depends(get_current_user),
     session: AsyncSession = Depends(get_session),
 ) -> ConnectedAccountsResponse:
-    result = await session.execute(
+    service = DashboardService()
-        select(SocialAccount).where(
+    connected = await service.get_connected_accounts(current_user, session)
-            SocialAccount.user_uuid == current_user.user_uuid,
-            SocialAccount.platform == "youtube",
-            SocialAccount.is_active == True,  # noqa: E712
-        )
-    )
-    accounts_raw = result.scalars().all()
-
-    # platform_user_id 기준
-    seen_platform_ids: set[str] = set()
-    connected = []
-    for acc in sorted(
-        accounts_raw, key=lambda a: a.connected_at or datetime.min, reverse=True
-    ):
-        if acc.platform_user_id in seen_platform_ids:
-            continue
-        seen_platform_ids.add(acc.platform_user_id)
-        data = acc.platform_data if isinstance(acc.platform_data, dict) else {}
-        connected.append(
-            ConnectedAccount(
-                id=acc.id,
-                platform=acc.platform,
-                platform_username=acc.platform_username,
-                platform_user_id=acc.platform_user_id,
-                channel_title=data.get("channel_title"),
-                connected_at=acc.connected_at,
-                is_active=acc.is_active,
-            )
-        )
-
-    logger.info(
-        f"[ACCOUNTS] YouTube 계정 목록 조회 - "
-        f"user_uuid={current_user.user_uuid}, count={len(connected)}"
-    )
     return ConnectedAccountsResponse(accounts=connected)
 
 
@@ -142,328 +88,8 @@ async def get_dashboard_stats(
     current_user: User = Depends(get_current_user),
     session: AsyncSession = Depends(get_session),
 ) -> DashboardResponse:
-    """
+    service = DashboardService()
-    대시보드 통계 조회
+    return await service.get_stats(mode, platform_user_id, current_user, session)
-
-    Args:
-        mode: 조회 모드 (day: 최근 30일, month: 최근 12개월)
-        platform_user_id: 사용할 YouTube 채널 ID (여러 계정 연결 시 필수, 재연동해도 불변)
-        current_user: 현재 인증된 사용자
-        session: 데이터베이스 세션
-
-    Returns:
-        DashboardResponse: 대시보드 통계 데이터
-
-    Raises:
-        YouTubeAccountNotConnectedError: YouTube 계정이 연동되어 있지 않음
-        YouTubeAccountSelectionRequiredError: 여러 계정이 연결되어 있으나 계정 미선택
-        YouTubeAccountNotFoundError: 지정한 계정을 찾을 수 없음
-        YouTubeTokenExpiredError: YouTube 토큰 만료 (재연동 필요)
-        YouTubeAPIError: YouTube Analytics API 호출 실패
-    """
-    logger.info(
-        f"[DASHBOARD] 통계 조회 시작 - "
-        f"user_uuid={current_user.user_uuid}, mode={mode}, platform_user_id={platform_user_id}"
-    )
-
-    # 1. 모드별 날짜 자동 계산
-    today = date.today()
-
-    if mode == "day":
-        # 48시간 지연 적용: 오늘 기준 -2일을 end로 사용
-        # ex) 오늘 2/20 → end=2/18, start=1/20
-        end_dt = today - timedelta(days=2)
-        kpi_end_dt = end_dt
-        start_dt = end_dt - timedelta(days=29)
-        # 이전 30일 (YouTube API day_previous와 동일 기준)
-        prev_start_dt = start_dt - timedelta(days=30)
-        prev_kpi_end_dt = kpi_end_dt - timedelta(days=30)
-        period_desc = "최근 30일"
-    else:  # mode == "month"
-        # 월별 차트: dimensions=month API는 YYYY-MM-01 형식 필요
-        # ex) 오늘 2/24 → end=2026-02-01, start=2025-03-01 → 2025-03 ~ 2026-02 (12개월)
-        end_dt = today.replace(day=1)
-        # KPI 등 집계형 API: 48시간 지연 적용하여 현재 월 전체 데이터 포함
-        kpi_end_dt = today - timedelta(days=2)
-
-        start_month = end_dt.month - 11
-        if start_month <= 0:
-            start_month += 12
-            start_year = end_dt.year - 1
-        else:
-            start_year = end_dt.year
-        start_dt = date(start_year, start_month, 1)
-        # 이전 12개월 (YouTube API previous와 동일 기준 — 1년 전)
-        prev_start_dt = start_dt.replace(year=start_dt.year - 1)
-        try:
-            prev_kpi_end_dt = kpi_end_dt.replace(year=kpi_end_dt.year - 1)
-        except ValueError:  # 윤년 2/29 → 이전 연도 2/28
-            prev_kpi_end_dt = kpi_end_dt.replace(year=kpi_end_dt.year - 1, day=28)
-        period_desc = "최근 12개월"
-
-    start_date = start_dt.strftime("%Y-%m-%d")
-    end_date = end_dt.strftime("%Y-%m-%d")
-    kpi_end_date = kpi_end_dt.strftime("%Y-%m-%d")
-
-    logger.debug(
-        f"[1] 날짜 계산 완료 - period={period_desc}, start={start_date}, end={end_date}"
-    )
-
-    # 2. YouTube 계정 연동 확인
-    result = await session.execute(
-        select(SocialAccount).where(
-            SocialAccount.user_uuid == current_user.user_uuid,
-            SocialAccount.platform == "youtube",
-            SocialAccount.is_active == True,  # noqa: E712
-        )
-    )
-    social_accounts_raw = result.scalars().all()
-
-    # platform_user_id 기준으로 중복 제거 (가장 최근 연동 계정 우선)
-    seen_platform_ids_stats: set[str] = set()
-    social_accounts = []
-    for acc in sorted(
-        social_accounts_raw, key=lambda a: a.connected_at or datetime.min, reverse=True
-    ):
-        if acc.platform_user_id not in seen_platform_ids_stats:
-            seen_platform_ids_stats.add(acc.platform_user_id)
-            social_accounts.append(acc)
-
-    if not social_accounts:
-        logger.warning(
-            f"[NO YOUTUBE ACCOUNT] YouTube 계정 미연동 - "
-            f"user_uuid={current_user.user_uuid}"
-        )
-        raise YouTubeAccountNotConnectedError()
-
-    if platform_user_id is not None:
-        matched = [a for a in social_accounts if a.platform_user_id == platform_user_id]
-        if not matched:
-            logger.warning(
-                f"[ACCOUNT NOT FOUND] 지정 계정 없음 - "
-                f"user_uuid={current_user.user_uuid}, platform_user_id={platform_user_id}"
-            )
-            raise YouTubeAccountNotFoundError()
-        social_account = matched[0]
-    elif len(social_accounts) == 1:
-        social_account = social_accounts[0]
-    else:
-        logger.warning(
-            f"[MULTI ACCOUNT] 계정 선택 필요 - "
-            f"user_uuid={current_user.user_uuid}, count={len(social_accounts)}"
-        )
-        raise YouTubeAccountSelectionRequiredError()
-
-    logger.debug(
-        f"[2] YouTube 계정 확인 완료 - platform_user_id={social_account.platform_user_id}"
-    )
-
-    # 3. 기간 내 업로드 영상 수 조회
-    count_result = await session.execute(
-        select(func.count())
-        .select_from(Dashboard)
-        .where(
-            Dashboard.user_uuid == current_user.user_uuid,
-            Dashboard.platform == "youtube",
-            Dashboard.platform_user_id == social_account.platform_user_id,
-            Dashboard.uploaded_at >= start_dt,
-            Dashboard.uploaded_at < today + timedelta(days=1),
-        )
-    )
-    period_video_count = count_result.scalar() or 0
-
-    # 이전 기간 업로드 영상 수 조회 (trend 계산용)
-    prev_count_result = await session.execute(
-        select(func.count())
-        .select_from(Dashboard)
-        .where(
-            Dashboard.user_uuid == current_user.user_uuid,
-            Dashboard.platform == "youtube",
-            Dashboard.platform_user_id == social_account.platform_user_id,
-            Dashboard.uploaded_at >= prev_start_dt,
-            Dashboard.uploaded_at <= prev_kpi_end_dt,
-        )
-    )
-    prev_period_video_count = prev_count_result.scalar() or 0
-    logger.debug(
-        f"[3] 기간 내 업로드 영상 수 - current={period_video_count}, prev={prev_period_video_count}"
-    )
-
-    # 4. Redis 캐시 조회
-    # platform_user_id 기준 캐시 키: 재연동해도 채널 ID는 불변 → 캐시 유지됨
-    cache_key = f"dashboard:{current_user.user_uuid}:{social_account.platform_user_id}:{mode}"
-    cached_raw = await get_cache(cache_key)
-
-    if cached_raw:
-        try:
-            payload = json.loads(cached_raw)
-            logger.info(f"[CACHE HIT] 캐시 반환 - user_uuid={current_user.user_uuid}")
-            response = DashboardResponse.model_validate(payload["response"])
-            for metric in response.content_metrics:
-                if metric.id == "uploaded-videos":
-                    metric.value = float(period_video_count)
-                    video_trend = float(period_video_count - prev_period_video_count)
-                    metric.trend = video_trend
-                    metric.trend_direction = "up" if video_trend > 0 else ("down" if video_trend < 0 else "-")
-                    break
-            return response
-        except (json.JSONDecodeError, KeyError):
-            logger.warning(f"[CACHE PARSE ERROR] 포맷 오류, 무시 - key={cache_key}")
-
-    logger.debug("[4] 캐시 MISS - YouTube API 호출 필요")
-
-    # 5. 최근 30개 업로드 영상 조회 (Analytics API 전달용)
-    # YouTube Analytics API 제약사항:
-    # - 영상 개수: 20~30개 권장 (최대 50개, 그 이상은 응답 지연 발생)
-    # - URL 길이: 2000자 제한 (video ID 11자 × 30개 = 330자로 안전)
-    result = await session.execute(
-        select(
-            Dashboard.platform_video_id,
-            Dashboard.title,
-            Dashboard.uploaded_at,
-        )
-        .where(
-            Dashboard.user_uuid == current_user.user_uuid,
-            Dashboard.platform == "youtube",
-            Dashboard.platform_user_id == social_account.platform_user_id,
-        )
-        .order_by(Dashboard.uploaded_at.desc())
-        .limit(30)
-    )
-    rows = result.all()
-    logger.debug(f"[5] 영상 조회 완료 - count={len(rows)}")
-
-    # 6. video_ids + 메타데이터 조회용 dict 구성
-    video_ids = []
-    video_lookup: dict[str, tuple[str, datetime]] = {}  # {video_id: (title, uploaded_at)}
-
-    for row in rows:
-        platform_video_id, title, uploaded_at = row
-        video_ids.append(platform_video_id)
-        video_lookup[platform_video_id] = (title, uploaded_at)
-
-    logger.debug(
-        f"[6] 영상 메타데이터 구성 완료 - count={len(video_ids)}, sample={video_ids[:3]}"
-    )
-
-    # 6.1 업로드 영상 없음 → YouTube API 호출 없이 빈 응답 반환
-    if not video_ids:
-        logger.info(
-            f"[DASHBOARD] 업로드 영상 없음, 빈 응답 반환 - "
-            f"user_uuid={current_user.user_uuid}"
-        )
-        return DashboardResponse(
-            content_metrics=[
-                ContentMetric(id="total-views", label="조회수", value=0.0, unit="count", trend=0.0, trend_direction="-"),
-                ContentMetric(id="total-watch-time", label="시청시간", value=0.0, unit="hours", trend=0.0, trend_direction="-"),
-                ContentMetric(id="avg-view-duration", label="평균 시청시간", value=0.0, unit="minutes", trend=0.0, trend_direction="-"),
-                ContentMetric(id="new-subscribers", label="신규 구독자", value=0.0, unit="count", trend=0.0, trend_direction="-"),
-                ContentMetric(id="likes", label="좋아요", value=0.0, unit="count", trend=0.0, trend_direction="-"),
-                ContentMetric(id="comments", label="댓글", value=0.0, unit="count", trend=0.0, trend_direction="-"),
-                ContentMetric(id="shares", label="공유", value=0.0, unit="count", trend=0.0, trend_direction="-"),
-                ContentMetric(id="uploaded-videos", label="업로드 영상", value=0.0, unit="count", trend=0.0, trend_direction="-"),
-            ],
-            monthly_data=[],
-            daily_data=[],
-            top_content=[],
-            audience_data=AudienceData(age_groups=[], gender={"male": 0, "female": 0}, top_regions=[]),
-            has_uploads=False,
-        )
-
-    # 7. 토큰 유효성 확인 및 자동 갱신 (만료 10분 전 갱신)
-    try:
-        access_token = await SocialAccountService().ensure_valid_token(
-            social_account, session
-        )
-    except TokenExpiredError:
-        logger.warning(
-            f"[TOKEN EXPIRED] 재연동 필요 - user_uuid={current_user.user_uuid}"
-        )
-        raise YouTubeTokenExpiredError()
-
-    logger.debug("[7] 토큰 유효성 확인 완료")
-
-    # 8. YouTube Analytics API 호출 (7개 병렬)
-    youtube_service = YouTubeAnalyticsService()
-    raw_data = await youtube_service.fetch_all_metrics(
-        video_ids=video_ids,
-        start_date=start_date,
-        end_date=end_date,
-        kpi_end_date=kpi_end_date,
-        access_token=access_token,
-        mode=mode,
-    )
-
-    logger.debug("[8] YouTube Analytics API 호출 완료")
-
-    # 9. TopContent 조립 (Analytics top_videos + DB lookup)
-    processor = DataProcessor()
-    top_content_rows = raw_data.get("top_videos", {}).get("rows", [])
-    top_content: list[TopContent] = []
-    for row in top_content_rows[:4]:
-        if len(row) < 4:
-            continue
-        video_id, views, likes, comments = row[0], row[1], row[2], row[3]
-        meta = video_lookup.get(video_id)
-        if not meta:
-            continue
-        title, uploaded_at = meta
-        engagement_rate = ((likes + comments) / views * 100) if views > 0 else 0
-        top_content.append(
-            TopContent(
-                id=video_id,
-                title=title,
-                thumbnail=f"https://i.ytimg.com/vi/{video_id}/mqdefault.jpg",
-                platform="youtube",
-                views=int(views),
-                engagement=f"{engagement_rate:.1f}%",
-                date=uploaded_at.strftime("%Y.%m.%d"),
-            )
-        )
-
-    logger.debug(f"[9] TopContent 조립 완료 - count={len(top_content)}")
-
-    # 10. 데이터 가공 (period_video_count=0 — API 무관 DB 집계값, 캐시에 포함하지 않음)
-    dashboard_data = processor.process(
-        raw_data, top_content, 0, mode=mode, end_date=end_date
-    )
-
-    logger.debug("[10] 데이터 가공 완료")
-
-    # 11. Redis 캐싱 (TTL: 12시간)
-    # YouTube Analytics는 하루 1회 갱신 (PT 자정, 한국 시간 오후 5~8시)
-    # 48시간 지연된 데이터이므로 12시간 캐싱으로 API 호출 최소화
-    # period_video_count는 캐시에 포함하지 않음 (DB 직접 집계, API 미사용)
-    cache_payload = json.dumps(
-        {"response": json.loads(dashboard_data.model_dump_json())}
-    )
-    cache_success = await set_cache(
-        cache_key,
-        cache_payload,
-        ttl=43200,  # 12시간
-    )
-
-    if cache_success:
-        logger.debug(f"[CACHE SET] 캐시 저장 성공 - key={cache_key}")
-    else:
-        logger.warning(f"[CACHE SET] 캐시 저장 실패 - key={cache_key}")
-
-    # 12. 업로드 영상 수 및 trend 주입 (캐시 저장 후 — 항상 DB에서 직접 집계)
-    for metric in dashboard_data.content_metrics:
-        if metric.id == "uploaded-videos":
-            metric.value = float(period_video_count)
-            video_trend = float(period_video_count - prev_period_video_count)
-            metric.trend = video_trend
-            metric.trend_direction = "up" if video_trend > 0 else ("down" if video_trend < 0 else "-")
-            break
-
-    logger.info(
-        f"[DASHBOARD] 통계 조회 완료 - "
-        f"user_uuid={current_user.user_uuid}, "
-        f"mode={mode}, period={period_desc}, videos={len(video_ids)}"
-    )
-
-    return dashboard_data
 
 
 @router.delete(
@@ -483,7 +109,7 @@ async def get_dashboard_stats(
 `dashboard:{user_uuid}:{platform_user_id}:{mode}` (mode: day 또는 month)
 
 ## 파라미터
-- `user_uuid`: 특정 사용자 캐시만 삭제. 미입력 시 전체 삭제
+- `user_uuid`: 삭제할 사용자 UUID (필수)
 - `mode`: day / month / all (기본값: all)
 """,
 )
@@ -492,33 +118,16 @@ async def delete_dashboard_cache(
         default="all",
         description="삭제할 캐시 모드: day, month, all(기본값, 모두 삭제)",
     ),
-    user_uuid: str | None = Query(
+    user_uuid: str = Query(
-        default=None,
+        description="대상 사용자 UUID",
-        description="대상 사용자 UUID. 미입력 시 전체 사용자 캐시 삭제",
     ),
 ) -> CacheDeleteResponse:
-    """
-    대시보드 캐시 삭제
-
-    Args:
-        mode: 삭제할 캐시 모드 (day / month / all)
-        user_uuid: 대상 사용자 UUID (없으면 전체 삭제)
-
-    Returns:
-        CacheDeleteResponse: 삭제된 캐시 키 개수 및 메시지
-    """
-    if user_uuid:
     if mode == "all":
         deleted = await delete_cache_pattern(f"dashboard:{user_uuid}:*")
         message = f"전체 캐시 삭제 완료 ({deleted}개)"
     else:
-            cache_key = f"dashboard:{user_uuid}:{mode}"
+        deleted = await delete_cache_pattern(f"dashboard:{user_uuid}:*:{mode}")
-            success = await delete_cache(cache_key)
+        message = f"{mode} 캐시 삭제 완료 ({deleted}개)"
-            deleted = 1 if success else 0
-            message = f"{mode} 캐시 삭제 {'완료' if success else '실패 (키 없음)'}"
-    else:
-        deleted = await delete_cache_pattern("dashboard:*")
-        message = f"전체 사용자 캐시 삭제 완료 ({deleted}개)"
 
     logger.info(
         f"[CACHE DELETE] user_uuid={user_uuid or 'ALL'}, mode={mode}, deleted={deleted}"

app/dashboard/exceptions.py

@@ -113,7 +113,7 @@ class YouTubeAccountSelectionRequiredError(DashboardException):
 
     def __init__(self):
         super().__init__(
-            message="연결된 YouTube 계정이 여러 개입니다. social_account_id 파라미터로 사용할 계정을 선택해주세요.",
+            message="연결된 YouTube 계정이 여러 개입니다. platform_user_id 파라미터로 사용할 계정을 선택해주세요.",
             status_code=status.HTTP_400_BAD_REQUEST,
             code="YOUTUBE_ACCOUNT_SELECTION_REQUIRED",
         )

app/dashboard/schemas/dashboard_schema.py

@@ -197,35 +197,6 @@ class AudienceData(BaseModel):
     )
 
 
-# class PlatformMetric(BaseModel):
-#     """플랫폼별 메트릭 (미사용 — platform_data 기능 미구현)"""
-#
-#     id: str
-#     label: str
-#     value: str
-#     unit: Optional[str] = None
-#     trend: float
-#     trend_direction: Literal["up", "down", "-"] = Field(alias="trendDirection")
-#
-#     model_config = ConfigDict(
-#         alias_generator=to_camel,
-#         populate_by_name=True,
-#     )
-#
-#
-# class PlatformData(BaseModel):
-#     """플랫폼별 데이터 (미사용 — platform_data 기능 미구현)"""
-#
-#     platform: Literal["youtube", "instagram"]
-#     display_name: str = Field(alias="displayName")
-#     metrics: list[PlatformMetric]
-#
-#     model_config = ConfigDict(
-#         alias_generator=to_camel,
-#         populate_by_name=True,
-#     )
-
-
 class DashboardResponse(BaseModel):
     """대시보드 전체 응답
 
@@ -255,7 +226,6 @@ class DashboardResponse(BaseModel):
     top_content: list[TopContent] = Field(alias="topContent")
     audience_data: AudienceData = Field(alias="audienceData")
     has_uploads: bool = Field(default=True, alias="hasUploads")
-    # platform_data: list[PlatformData] = Field(default=[], alias="platformData")  # 미사용
 
     model_config = ConfigDict(
         alias_generator=to_camel,

app/dashboard/services/__init__.py

@@ -4,10 +4,12 @@ Dashboard Services
 YouTube Analytics API 연동 및 데이터 가공 서비스를 제공합니다.
 """
 
+from app.dashboard.services.dashboard_service import DashboardService
 from app.dashboard.services.data_processor import DataProcessor
 from app.dashboard.services.youtube_analytics import YouTubeAnalyticsService
 
 __all__ = [
+    "DashboardService",
     "YouTubeAnalyticsService",
     "DataProcessor",
 ]

app/dashboard/services/dashboard_service.py

@@ -0,0 +1,358 @@
+"""
+Dashboard Service
+
+대시보드 비즈니스 로직을 담당합니다.
+"""
+
+import json
+import logging
+from datetime import date, datetime, timedelta
+from typing import Literal
+
+from sqlalchemy import func, select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.dashboard.exceptions import (
+    YouTubeAccountNotConnectedError,
+    YouTubeAccountNotFoundError,
+    YouTubeAccountSelectionRequiredError,
+    YouTubeTokenExpiredError,
+)
+from app.dashboard.models import Dashboard
+from app.dashboard.utils.redis_cache import get_cache, set_cache
+from app.dashboard.schemas import (
+    AudienceData,
+    ConnectedAccount,
+    ContentMetric,
+    DashboardResponse,
+    TopContent,
+)
+from app.dashboard.services.data_processor import DataProcessor
+from app.dashboard.services.youtube_analytics import YouTubeAnalyticsService
+from app.social.exceptions import TokenExpiredError
+from app.social.services import SocialAccountService
+from app.user.models import SocialAccount, User
+
+logger = logging.getLogger(__name__)
+
+
+class DashboardService:
+    async def get_connected_accounts(
+        self,
+        current_user: User,
+        session: AsyncSession,
+    ) -> list[ConnectedAccount]:
+        result = await session.execute(
+            select(SocialAccount).where(
+                SocialAccount.user_uuid == current_user.user_uuid,
+                SocialAccount.platform == "youtube",
+                SocialAccount.is_active == True,  # noqa: E712
+            )
+        )
+        accounts_raw = result.scalars().all()
+
+        connected = []
+        for acc in accounts_raw:
+            data = acc.platform_data if isinstance(acc.platform_data, dict) else {}
+            connected.append(
+                ConnectedAccount(
+                    id=acc.id,
+                    platform=acc.platform,
+                    platform_username=acc.platform_username,
+                    platform_user_id=acc.platform_user_id,
+                    channel_title=data.get("channel_title"),
+                    connected_at=acc.connected_at,
+                    is_active=acc.is_active,
+                )
+            )
+
+        logger.info(
+            f"[ACCOUNTS] YouTube 계정 목록 조회 - "
+            f"user_uuid={current_user.user_uuid}, count={len(connected)}"
+        )
+        return connected
+
+    def calculate_date_range(
+        self, mode: Literal["day", "month"]
+    ) -> tuple[date, date, date, date, date, str]:
+        """모드별 날짜 범위 계산. (start_dt, end_dt, kpi_end_dt, prev_start_dt, prev_kpi_end_dt, period_desc) 반환"""
+        today = date.today()
+
+        if mode == "day":
+            end_dt = today - timedelta(days=2)
+            kpi_end_dt = end_dt
+            start_dt = end_dt - timedelta(days=29)
+            prev_start_dt = start_dt - timedelta(days=30)
+            prev_kpi_end_dt = kpi_end_dt - timedelta(days=30)
+            period_desc = "최근 30일"
+        else:
+            end_dt = today.replace(day=1)
+            kpi_end_dt = today - timedelta(days=2)
+            start_month = end_dt.month - 11
+            if start_month <= 0:
+                start_month += 12
+                start_year = end_dt.year - 1
+            else:
+                start_year = end_dt.year
+            start_dt = date(start_year, start_month, 1)
+            prev_start_dt = start_dt.replace(year=start_dt.year - 1)
+            try:
+                prev_kpi_end_dt = kpi_end_dt.replace(year=kpi_end_dt.year - 1)
+            except ValueError:
+                prev_kpi_end_dt = kpi_end_dt.replace(year=kpi_end_dt.year - 1, day=28)
+            period_desc = "최근 12개월"
+
+        return start_dt, end_dt, kpi_end_dt, prev_start_dt, prev_kpi_end_dt, period_desc
+
+    async def resolve_social_account(
+        self,
+        current_user: User,
+        session: AsyncSession,
+        platform_user_id: str | None,
+    ) -> SocialAccount:
+        result = await session.execute(
+            select(SocialAccount).where(
+                SocialAccount.user_uuid == current_user.user_uuid,
+                SocialAccount.platform == "youtube",
+                SocialAccount.is_active == True,  # noqa: E712
+            )
+        )
+        social_accounts_raw = result.scalars().all()
+
+        social_accounts = list(social_accounts_raw)
+
+        if not social_accounts:
+            raise YouTubeAccountNotConnectedError()
+
+        if platform_user_id is not None:
+            matched = [a for a in social_accounts if a.platform_user_id == platform_user_id]
+            if not matched:
+                raise YouTubeAccountNotFoundError()
+            return matched[0]
+        elif len(social_accounts) == 1:
+            return social_accounts[0]
+        else:
+            raise YouTubeAccountSelectionRequiredError()
+
+    async def get_video_counts(
+        self,
+        current_user: User,
+        session: AsyncSession,
+        social_account: SocialAccount,
+        start_dt: date,
+        prev_start_dt: date,
+        prev_kpi_end_dt: date,
+    ) -> tuple[int, int]:
+        today = date.today()
+        count_result = await session.execute(
+            select(func.count())
+            .select_from(Dashboard)
+            .where(
+                Dashboard.user_uuid == current_user.user_uuid,
+                Dashboard.platform == "youtube",
+                Dashboard.platform_user_id == social_account.platform_user_id,
+                Dashboard.uploaded_at >= start_dt,
+                Dashboard.uploaded_at < today + timedelta(days=1),
+            )
+        )
+        period_video_count = count_result.scalar() or 0
+
+        prev_count_result = await session.execute(
+            select(func.count())
+            .select_from(Dashboard)
+            .where(
+                Dashboard.user_uuid == current_user.user_uuid,
+                Dashboard.platform == "youtube",
+                Dashboard.platform_user_id == social_account.platform_user_id,
+                Dashboard.uploaded_at >= prev_start_dt,
+                Dashboard.uploaded_at <= prev_kpi_end_dt,
+            )
+        )
+        prev_period_video_count = prev_count_result.scalar() or 0
+
+        return period_video_count, prev_period_video_count
+
+    async def get_video_ids(
+        self,
+        current_user: User,
+        session: AsyncSession,
+        social_account: SocialAccount,
+    ) -> tuple[list[str], dict[str, tuple[str, datetime]]]:
+        result = await session.execute(
+            select(
+                Dashboard.platform_video_id,
+                Dashboard.title,
+                Dashboard.uploaded_at,
+            )
+            .where(
+                Dashboard.user_uuid == current_user.user_uuid,
+                Dashboard.platform == "youtube",
+                Dashboard.platform_user_id == social_account.platform_user_id,
+            )
+            .order_by(Dashboard.uploaded_at.desc())
+            .limit(30)
+        )
+        rows = result.all()
+
+        video_ids = []
+        video_lookup: dict[str, tuple[str, datetime]] = {}
+        for row in rows:
+            platform_video_id, title, uploaded_at = row
+            video_ids.append(platform_video_id)
+            video_lookup[platform_video_id] = (title, uploaded_at)
+
+        return video_ids, video_lookup
+
+    def build_empty_response(self) -> DashboardResponse:
+        return DashboardResponse(
+            content_metrics=[
+                ContentMetric(id="total-views", label="조회수", value=0.0, unit="count", trend=0.0, trend_direction="-"),
+                ContentMetric(id="total-watch-time", label="시청시간", value=0.0, unit="hours", trend=0.0, trend_direction="-"),
+                ContentMetric(id="avg-view-duration", label="평균 시청시간", value=0.0, unit="minutes", trend=0.0, trend_direction="-"),
+                ContentMetric(id="new-subscribers", label="신규 구독자", value=0.0, unit="count", trend=0.0, trend_direction="-"),
+                ContentMetric(id="likes", label="좋아요", value=0.0, unit="count", trend=0.0, trend_direction="-"),
+                ContentMetric(id="comments", label="댓글", value=0.0, unit="count", trend=0.0, trend_direction="-"),
+                ContentMetric(id="shares", label="공유", value=0.0, unit="count", trend=0.0, trend_direction="-"),
+                ContentMetric(id="uploaded-videos", label="업로드 영상", value=0.0, unit="count", trend=0.0, trend_direction="-"),
+            ],
+            monthly_data=[],
+            daily_data=[],
+            top_content=[],
+            audience_data=AudienceData(age_groups=[], gender={"male": 0, "female": 0}, top_regions=[]),
+            has_uploads=False,
+        )
+
+    def inject_video_count(
+        self,
+        response: DashboardResponse,
+        period_video_count: int,
+        prev_period_video_count: int,
+    ) -> None:
+        for metric in response.content_metrics:
+            if metric.id == "uploaded-videos":
+                metric.value = float(period_video_count)
+                video_trend = float(period_video_count - prev_period_video_count)
+                metric.trend = video_trend
+                metric.trend_direction = "up" if video_trend > 0 else ("down" if video_trend < 0 else "-")
+                break
+
+    async def get_stats(
+        self,
+        mode: Literal["day", "month"],
+        platform_user_id: str | None,
+        current_user: User,
+        session: AsyncSession,
+    ) -> DashboardResponse:
+        logger.info(
+            f"[DASHBOARD] 통계 조회 시작 - "
+            f"user_uuid={current_user.user_uuid}, mode={mode}, platform_user_id={platform_user_id}"
+        )
+
+        # 1. 날짜 계산
+        start_dt, end_dt, kpi_end_dt, prev_start_dt, prev_kpi_end_dt, period_desc = (
+            self.calculate_date_range(mode)
+        )
+        start_date = start_dt.strftime("%Y-%m-%d")
+        end_date = end_dt.strftime("%Y-%m-%d")
+        kpi_end_date = kpi_end_dt.strftime("%Y-%m-%d")
+        logger.debug(f"[1] 날짜 계산 완료 - period={period_desc}, start={start_date}, end={end_date}")
+
+        # 2. YouTube 계정 확인
+        social_account = await self.resolve_social_account(current_user, session, platform_user_id)
+        logger.debug(f"[2] YouTube 계정 확인 완료 - platform_user_id={social_account.platform_user_id}")
+
+        # 3. 영상 수 조회
+        period_video_count, prev_period_video_count = await self.get_video_counts(
+            current_user, session, social_account, start_dt, prev_start_dt, prev_kpi_end_dt
+        )
+        logger.debug(f"[3] 영상 수 - current={period_video_count}, prev={prev_period_video_count}")
+
+        # 4. 캐시 조회
+        cache_key = f"dashboard:{current_user.user_uuid}:{social_account.platform_user_id}:{mode}"
+        cached_raw = await get_cache(cache_key)
+        if cached_raw:
+            try:
+                payload = json.loads(cached_raw)
+                logger.info(f"[CACHE HIT] 캐시 반환 - user_uuid={current_user.user_uuid}")
+                response = DashboardResponse.model_validate(payload["response"])
+                self.inject_video_count(response, period_video_count, prev_period_video_count)
+                return response
+            except (json.JSONDecodeError, KeyError):
+                logger.warning(f"[CACHE PARSE ERROR] 포맷 오류, 무시 - key={cache_key}")
+
+        logger.debug("[4] 캐시 MISS - YouTube API 호출 필요")
+
+        # 5. 업로드 영상 조회
+        video_ids, video_lookup = await self.get_video_ids(current_user, session, social_account)
+        logger.debug(f"[5] 영상 조회 완료 - count={len(video_ids)}")
+
+        if not video_ids:
+            logger.info(f"[DASHBOARD] 업로드 영상 없음, 빈 응답 반환 - user_uuid={current_user.user_uuid}")
+            return self.build_empty_response()
+
+        # 6. 토큰 유효성 확인
+        try:
+            access_token = await SocialAccountService().ensure_valid_token(social_account, session)
+        except TokenExpiredError:
+            logger.warning(f"[TOKEN EXPIRED] 재연동 필요 - user_uuid={current_user.user_uuid}")
+            raise YouTubeTokenExpiredError()
+        logger.debug("[6] 토큰 유효성 확인 완료")
+
+        # 7. YouTube Analytics API 호출
+        youtube_service = YouTubeAnalyticsService()
+        raw_data = await youtube_service.fetch_all_metrics(
+            video_ids=video_ids,
+            start_date=start_date,
+            end_date=end_date,
+            kpi_end_date=kpi_end_date,
+            access_token=access_token,
+            mode=mode,
+        )
+        logger.debug("[7] YouTube Analytics API 호출 완료")
+
+        # 8. TopContent 조립
+        processor = DataProcessor()
+        top_content_rows = raw_data.get("top_videos", {}).get("rows", [])
+        top_content: list[TopContent] = []
+        for row in top_content_rows[:4]:
+            if len(row) < 4:
+                continue
+            video_id, views, likes, comments = row[0], row[1], row[2], row[3]
+            meta = video_lookup.get(video_id)
+            if not meta:
+                continue
+            title, uploaded_at = meta
+            engagement_rate = ((likes + comments) / views * 100) if views > 0 else 0
+            top_content.append(
+                TopContent(
+                    id=video_id,
+                    title=title,
+                    thumbnail=f"https://i.ytimg.com/vi/{video_id}/mqdefault.jpg",
+                    platform="youtube",
+                    views=int(views),
+                    engagement=f"{engagement_rate:.1f}%",
+                    date=uploaded_at.strftime("%Y.%m.%d"),
+                )
+            )
+        logger.debug(f"[8] TopContent 조립 완료 - count={len(top_content)}")
+
+        # 9. 데이터 가공
+        dashboard_data = processor.process(raw_data, top_content, 0, mode=mode, end_date=end_date)
+        logger.debug("[9] 데이터 가공 완료")
+
+        # 10. 캐시 저장
+        cache_payload = json.dumps({"response": dashboard_data.model_dump(mode="json")})
+        cache_success = await set_cache(cache_key, cache_payload, ttl=43200)
+        if cache_success:
+            logger.debug(f"[CACHE SET] 캐시 저장 성공 - key={cache_key}")
+        else:
+            logger.warning(f"[CACHE SET] 캐시 저장 실패 - key={cache_key}")
+
+        # 11. 업로드 영상 수 주입
+        self.inject_video_count(dashboard_data, period_video_count, prev_period_video_count)
+
+        logger.info(
+            f"[DASHBOARD] 통계 조회 완료 - "
+            f"user_uuid={current_user.user_uuid}, mode={mode}, period={period_desc}, videos={len(video_ids)}"
+        )
+        return dashboard_data

app/dashboard/services/data_processor.py

@@ -143,8 +143,8 @@ class DataProcessor:
             monthly_data = []
 
         audience_data = self._build_audience_data(
-            raw_data.get("demographics", {}),
+            raw_data.get("demographics") or {},
-            raw_data.get("region", {}),
+            raw_data.get("region") or {},
         )
         logger.debug(
             f"[DataProcessor.process] SUCCESS - "

app/dashboard/services/youtube_analytics.py

@@ -141,6 +141,9 @@ class YouTubeAnalyticsService:
         results = await asyncio.gather(*tasks, return_exceptions=True)
 
         # 에러 체크 (YouTubeAuthError, YouTubeQuotaExceededError는 원형 그대로 전파)
+        # demographics(index 5)는 YouTubeAPIError 시 None으로 허용 (YouTube 서버 간헐적 오류 대응)
+        OPTIONAL_INDICES = {5, 6}  # demographics, region
+        results = list(results)
         for i, result in enumerate(results):
             if isinstance(result, Exception):
                 logger.error(
@@ -148,6 +151,12 @@ class YouTubeAnalyticsService:
                 )
                 if isinstance(result, (YouTubeAuthError, YouTubeQuotaExceededError)):
                     raise result
+                if i in OPTIONAL_INDICES and isinstance(result, YouTubeAPIError):
+                    logger.warning(
+                        f"[YouTubeAnalyticsService] 선택적 API 호출 {i+1}/7 실패, None으로 처리: {result}"
+                    )
+                    results[i] = None
+                    continue
                 raise YouTubeAPIError(f"데이터 조회 실패: {result.__class__.__name__}")
 
         logger.debug(

app/database/session.py

@@ -74,7 +74,7 @@ async def create_db_tables():
 
     # 모델 import (테이블 메타데이터 등록용)
     from app.user.models import User, RefreshToken, SocialAccount  # noqa: F401
-    from app.home.models import Image, Project, MarketingIntel  # noqa: F401
+    from app.home.models import Image, Project, MarketingIntel, ImageTag  # noqa: F401
     from app.lyric.models import Lyric  # noqa: F401
     from app.song.models import Song, SongTimestamp  # noqa: F401
     from app.video.models import Video  # noqa: F401
@@ -97,6 +97,7 @@ async def create_db_tables():
         SocialUpload.__table__,
         MarketingIntel.__table__,
         Dashboard.__table__,
+        ImageTag.__table__,
     ]
 
     logger.info("Creating database tables...")

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

@@ -9,9 +9,10 @@ import aiofiles
 from fastapi import APIRouter, Depends, File, Form, HTTPException, UploadFile, status
 from sqlalchemy.exc import SQLAlchemyError
 from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import func, select
 
 from app.database.session import get_session, AsyncSessionLocal
-from app.home.models import Image, MarketingIntel
+from app.home.models import Image, MarketingIntel, ImageTag
 from app.user.dependencies.auth import get_current_user
 from app.user.models import User
 from app.home.schemas.home_schema import (
@@ -29,12 +30,13 @@ from app.home.schemas.home_schema import (
 )
 from app.home.services.naver_search import naver_search_client
 from app.utils.upload_blob_as_request import AzureBlobUploader
-from app.utils.chatgpt_prompt import ChatgptService, ChatGPTResponseError
+from app.utils.prompts.chatgpt_prompt import ChatgptService, ChatGPTResponseError
 from app.utils.common import generate_task_id
 from app.utils.logger import get_logger
-from app.utils.nvMapScraper import NvMapScraper, GraphQLException
+from app.utils.nvMapScraper import NvMapScraper, GraphQLException, URLNotFoundException
 from app.utils.nvMapPwScraper import NvMapPwScraper
 from app.utils.prompts.prompts import marketing_prompt
+from app.utils.autotag import autotag_images
 from config import MEDIA_ROOT
 
 # 로거 설정
@@ -218,6 +220,15 @@ async def _crawling_logic(
             status_code=status.HTTP_502_BAD_GATEWAY,
             detail=f"네이버 지도 크롤링에 실패했습니다: {e}",
         )
+    except URLNotFoundException as e:
+        step1_elapsed = (time.perf_counter() - step1_start) * 1000
+        logger.error(
+            f"[crawling] Step 1 FAILED - 크롤링 실패: {e} ({step1_elapsed:.1f}ms)"
+        )
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail=f"Place ID를 확인할 수 없습니다. URL을 확인하세요. : {e}",
+        )
     except Exception as e:
         step1_elapsed = (time.perf_counter() - step1_start) * 1000
         logger.error(
@@ -451,255 +462,6 @@ IMAGES_JSON_EXAMPLE = """[
     {"url": "https://naverbooking-phinf.pstatic.net/20240514_259/17156880311809wCnY_JPEG/5.jpg", "name": "외관"}
 ]"""
 
-
-@router.post(
-    "/image/upload/server",
-    include_in_schema=False,
-    summary="이미지 업로드 (로컬 서버)",
-    description="""
-이미지를 로컬 서버(media 폴더)에 업로드하고 새로운 task_id를 생성합니다.
-
-## 요청 방식
-multipart/form-data 형식으로 전송합니다.
-
-## 요청 필드
-- **images_json**: 외부 이미지 URL 목록 (JSON 문자열, 선택)
-- **files**: 이미지 바이너리 파일 목록 (선택)
-
-**주의**: images_json 또는 files 중 최소 하나는 반드시 전달해야 합니다.
-
-## 지원 이미지 확장자
-jpg, jpeg, png, webp, heic, heif
-
-## images_json 예시
-```json
-[
-    {"url": "https://naverbooking-phinf.pstatic.net/20240514_189/1715688030436xT14o_JPEG/1.jpg"},
-    {"url": "https://naverbooking-phinf.pstatic.net/20240514_48/1715688030574wTtQd_JPEG/2.jpg"},
-    {"url": "https://naverbooking-phinf.pstatic.net/20240514_92/17156880307484bvpH_JPEG/3.jpg"},
-    {"url": "https://naverbooking-phinf.pstatic.net/20240514_7/1715688031000y8Y5q_JPEG/4.jpg"},
-    {"url": "https://naverbooking-phinf.pstatic.net/20240514_259/17156880311809wCnY_JPEG/5.jpg", "name": "외관"}
-]
-```
-
-## 바이너리 파일 업로드 테스트 방법
-
-### 1. Swagger UI에서 테스트
-1. 이 엔드포인트의 "Try it out" 버튼 클릭
-2. task_id 입력 (예: test-task-001)
-3. files 항목에서 "Add item" 클릭하여 로컬 이미지 파일 선택
-4. (선택) images_json에 URL 목록 JSON 입력
-5. "Execute" 버튼 클릭
-
-### 2. cURL로 테스트
-```bash
-# 바이너리 파일만 업로드
-curl -X POST "http://localhost:8000/image/upload/server/test-task-001" \\
-  -F "files=@/path/to/image1.jpg" \\
-  -F "files=@/path/to/image2.png"
-
-# URL + 바이너리 파일 동시 업로드
-curl -X POST "http://localhost:8000/image/upload/server/test-task-001" \\
-  -F 'images_json=[{"url":"https://example.com/image.jpg"}]' \\
-  -F "files=@/path/to/local_image.jpg"
-```
-
-### 3. Python requests로 테스트
-```python
-import requests
-
-url = "http://localhost:8000/image/upload/server/test-task-001"
-files = [
-    ("files", ("image1.jpg", open("image1.jpg", "rb"), "image/jpeg")),
-    ("files", ("image2.png", open("image2.png", "rb"), "image/png")),
-]
-data = {
-    "images_json": '[{"url": "https://example.com/image.jpg"}]'
-}
-response = requests.post(url, files=files, data=data)
-print(response.json())
-```
-
-## 반환 정보
-- **task_id**: 작업 고유 식별자
-- **total_count**: 총 업로드된 이미지 개수
-- **url_count**: URL로 등록된 이미지 개수 (Image 테이블에 외부 URL 그대로 저장)
-- **file_count**: 파일로 업로드된 이미지 개수 (media 폴더에 저장)
-- **saved_count**: Image 테이블에 저장된 row 수
-- **images**: 업로드된 이미지 목록
-  - **source**: "url" (외부 URL) 또는 "file" (로컬 서버 저장)
-
-## 저장 경로
-- 바이너리 파일: /media/image/{날짜}/{uuid7}/{파일명}
-- URL 이미지: 외부 URL 그대로 Image 테이블에 저장
-
-## 반환 정보
-- **task_id**: 새로 생성된 작업 고유 식별자
-- **image_urls**: Image 테이블에 저장된 현재 task_id의 이미지 URL 목록
-    """,
-    response_model=ImageUploadResponse,
-    responses={
-        200: {"description": "이미지 업로드 성공"},
-        400: {"description": "이미지가 제공되지 않음", "model": ErrorResponse},
-    },
-    tags=["Image-Server"],
-)
-async def upload_images(
-    images_json: Optional[str] = Form(
-        default=None,
-        description="외부 이미지 URL 목록 (JSON 문자열)",
-        examples=[IMAGES_JSON_EXAMPLE],
-    ),
-    files: Optional[list[UploadFile]] = File(
-        default=None, description="이미지 바이너리 파일 목록"
-    ),
-    current_user: User = Depends(get_current_user),
-    session: AsyncSession = Depends(get_session),
-) -> ImageUploadResponse:
-    """이미지 업로드 (URL + 바이너리 파일)"""
-    # task_id 생성
-    task_id = await generate_task_id()
-
-    # 1. 진입 검증: images_json 또는 files 중 하나는 반드시 있어야 함
-    has_images_json = images_json is not None and images_json.strip() != ""
-    has_files = files is not None and len(files) > 0
-
-    if not has_images_json and not has_files:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="images_json 또는 files 중 하나는 반드시 제공해야 합니다.",
-        )
-
-    # 2. images_json 파싱 (있는 경우만)
-    url_images: list[ImageUrlItem] = []
-    if has_images_json:
-        try:
-            parsed = json.loads(images_json)
-            if isinstance(parsed, list):
-                url_images = [ImageUrlItem(**item) for item in parsed if item]
-        except (json.JSONDecodeError, TypeError, ValueError) as e:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=f"images_json 파싱 오류: {str(e)}",
-            )
-
-    # 3. 유효한 파일만 필터링 (빈 파일, 유효한 이미지 확장자가 아닌 경우 제외)
-    valid_files: list[UploadFile] = []
-    skipped_files: list[str] = []
-    if has_files and files:
-        for f in files:
-            is_valid_ext = _is_valid_image_extension(f.filename)
-            is_not_empty = (
-                f.size is None or f.size > 0
-            )  # size가 None이면 아직 읽지 않은 것
-            is_real_file = (
-                f.filename and f.filename != "filename"
-            )  # Swagger 빈 파일 체크
-            if f and is_real_file and is_valid_ext and is_not_empty:
-                valid_files.append(f)
-            else:
-                skipped_files.append(f.filename or "unknown")
-
-    # 유효한 데이터가 하나도 없으면 에러
-    if not url_images and not valid_files:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"유효한 이미지가 없습니다. 지원 확장자: {', '.join(ALLOWED_IMAGE_EXTENSIONS)}. 건너뛴 파일: {skipped_files}",
-        )
-
-    result_images: list[ImageUploadResultItem] = []
-    img_order = 0
-
-    # 1. URL 이미지 저장
-    for url_item in url_images:
-        img_name = url_item.name or _extract_image_name(url_item.url, img_order)
-
-        image = Image(
-            task_id=task_id,
-            img_name=img_name,
-            img_url=url_item.url,
-            img_order=img_order,
-        )
-        session.add(image)
-        await session.flush()  # ID 생성을 위해 flush
-
-        result_images.append(
-            ImageUploadResultItem(
-                id=image.id,
-                img_name=img_name,
-                img_url=url_item.url,
-                img_order=img_order,
-                source="url",
-            )
-        )
-        img_order += 1
-
-    # 2. 바이너리 파일을 media에 저장
-    if valid_files:
-        today = date.today().strftime("%Y-%m-%d")
-        # 한 번의 요청에서 업로드된 모든 이미지는 같은 폴더에 저장
-        batch_uuid = await generate_task_id()
-        upload_dir = MEDIA_ROOT / "image" / today / batch_uuid
-        upload_dir.mkdir(parents=True, exist_ok=True)
-
-        for file in valid_files:
-            # 파일명: 원본 파일명 사용 (중복 방지를 위해 순서 추가)
-            original_name = file.filename or "image"
-            ext = _get_file_extension(file.filename)  # type: ignore[arg-type]
-            # 파일명에서 확장자 제거 후 순서 추가
-            name_without_ext = (
-                original_name.rsplit(".", 1)[0]
-                if "." in original_name
-                else original_name
-            )
-            filename = f"{name_without_ext}_{img_order:03d}{ext}"
-
-            save_path = upload_dir / filename
-
-            # media에 파일 저장
-            await _save_upload_file(file, save_path)
-
-            # media 기준 URL 생성
-            img_url = f"/media/image/{today}/{batch_uuid}/{filename}"
-            img_name = file.filename or filename
-
-            image = Image(
-                task_id=task_id,
-                img_name=img_name,
-                img_url=img_url,  # Media URL을 DB에 저장
-                img_order=img_order,
-            )
-            session.add(image)
-            await session.flush()
-
-            result_images.append(
-                ImageUploadResultItem(
-                    id=image.id,
-                    img_name=img_name,
-                    img_url=img_url,
-                    img_order=img_order,
-                    source="file",
-                )
-            )
-            img_order += 1
-
-    saved_count = len(result_images)
-    await session.commit()
-
-    # Image 테이블에서 현재 task_id의 이미지 URL 목록 조회
-    image_urls = [img.img_url for img in result_images]
-
-    return ImageUploadResponse(
-        task_id=task_id,
-        total_count=len(result_images),
-        url_count=len(url_images),
-        file_count=len(valid_files),
-        saved_count=saved_count,
-        images=result_images,
-        image_urls=image_urls,
-    )
-
-
 @router.post(
     "/image/upload/blob",
     summary="이미지 업로드 (Azure Blob Storage)",
@@ -988,6 +750,10 @@ async def upload_images_blob(
     saved_count = len(result_images)
     image_urls = [img.img_url for img in result_images]
 
+    logger.info(f"[image_tagging] START - task_id: {task_id}")
+    await tag_images_if_not_exist(image_urls)
+    logger.info(f"[image_tagging] Done - task_id: {task_id}")
+
     total_time = time.perf_counter() - request_start
     logger.info(
         f"[upload_images_blob] SUCCESS - task_id: {task_id}, "
@@ -1003,3 +769,36 @@ async def upload_images_blob(
         images=result_images,
         image_urls=image_urls,
     )
+
+
+async def tag_images_if_not_exist(
+        image_urls : list[str]
+    ) -> None:
+    # 1. 조회
+    async with AsyncSessionLocal() as session:
+        stmt = (
+            select(ImageTag)
+            .where(ImageTag.img_url_hash.in_([func.crc32(url) for url in image_urls]))
+            .where(ImageTag.img_url.in_(image_urls))
+        )
+        image_tags_query_results = await session.execute(stmt)
+        image_tags = image_tags_query_results.scalars().all()
+        existing_urls = {tag.img_url for tag in image_tags}
+        new_tags = [
+            ImageTag(img_url=url, img_tag=None)
+            for url in image_urls
+            if url not in existing_urls
+        ]
+        session.add_all(new_tags)
+
+        null_tags = [tag for tag in image_tags if tag.img_tag is None] + new_tags
+        
+        if null_tags:
+            tag_datas = await autotag_images([img.img_url for img in null_tags])
+
+            print(tag_datas)
+
+            for tag, tag_data in zip(null_tags, tag_datas):
+                tag.img_tag = tag_data.model_dump(mode="json")
+
+        await session.commit()

app/home/models.py

@@ -9,7 +9,8 @@ Home 모듈 SQLAlchemy 모델 정의
 from datetime import datetime
 from typing import TYPE_CHECKING, List, Optional, Any
 
-from sqlalchemy import Boolean, DateTime, ForeignKey, Index, Integer, String, Text, JSON, func
+from sqlalchemy import Boolean, DateTime, ForeignKey, Computed, Index, Integer, String, Text, JSON, func
+from sqlalchemy.dialects.mysql import INTEGER
 from sqlalchemy.orm import Mapped, mapped_column, relationship
 
 from app.database.session import Base
@@ -314,13 +315,50 @@ class MarketingIntel(Base):
     )
 
     def __repr__(self) -> str:
-        task_id_str = (
+        return (
-            (self.task_id[:10] + "...") if len(self.task_id) > 10 else self.task_id
+            f"<MarketingIntel(id={self.id}, place_id='{self.place_id}')>"
-        )
-        img_name_str = (
-            (self.img_name[:10] + "...") if len(self.img_name) > 10 else self.img_name
         )
 
-        return (
+
-            f"<Image(id={self.id}, task_id='{task_id_str}', img_name='{img_name_str}')>"
+class ImageTag(Base):
+    """
+    이미지 태그 테이블
+
+    """
+
+    __tablename__ = "image_tags"
+    __table_args__ = (
+        Index("idx_img_url_hash", "img_url_hash"),  # CRC32 index
+        {
+            "mysql_engine": "InnoDB",
+            "mysql_charset": "utf8mb4",
+            "mysql_collate": "utf8mb4_unicode_ci",
+        },
+    )
+
+    id: Mapped[int] = mapped_column(
+        Integer,
+        primary_key=True,
+        nullable=False,
+        autoincrement=True,
+        comment="고유 식별자",
+    )
+
+    img_url: Mapped[str] = mapped_column(
+        String(2048),
+        nullable=False,
+        comment="이미지 URL (blob, CDN 경로)",
+    )
+
+    img_url_hash: Mapped[int] = mapped_column(
+        INTEGER(unsigned=True),
+        Computed("CRC32(img_url)", persisted=True),  # generated column
+        comment="URL CRC32 해시 (검색용 index)",
+    )
+
+    img_tag: Mapped[dict[str, Any]] = mapped_column(
+        JSON,
+        nullable=True,
+        default=False,
+        comment="태그 JSON",
     )

app/lyric/api/routers/v1/lyric.py

@@ -42,7 +42,7 @@ from app.lyric.schemas.lyric import (
     LyricStatusResponse,
 )
 from app.lyric.worker.lyric_task import generate_lyric_background, generate_subtitle_background
-from app.utils.chatgpt_prompt import ChatgptService
+from app.utils.prompts.chatgpt_prompt import ChatgptService
 from app.utils.logger import get_logger
 from app.utils.pagination import PaginatedResponse, get_paginated
 
@@ -253,17 +253,6 @@ async def generate_lyric(
         step1_start = time.perf_counter()
         logger.debug(f"[generate_lyric] Step 1: 서비스 초기화 및 프롬프트 생성...")
 
-        # service = ChatgptService(
-        #     customer_name=request_body.customer_name,
-        #     region=request_body.region,
-        #     detail_region_info=request_body.detail_region_info or "",
-        #     language=request_body.language,
-        # )
-
-        # prompt = service.build_lyrics_prompt()
-        # 원래는 실제 사용할 프롬프트가 들어가야 하나, 로직이 변경되어 이 시점에서 이곳에서 프롬프트를 생성할 이유가 없어서 삭제됨. 
-        # 기존 코드와의 호환을 위해 동일한 로직으로 프롬프트 생성
-
         promotional_expressions = {
             "Korean" : "인스타 감성, 사진같은 하루, 힐링, 여행, 감성 숙소",
             "English" : "Instagram vibes, picture-perfect day, healing, travel, getaway",

app/lyric/schemas/lyric.py

@@ -42,7 +42,7 @@ class GenerateLyricRequest(BaseModel):
             "region": "군산",
             "detail_region_info": "군산 신흥동 말랭이 마을",
             "language": "Korean",
-            "m_id" : 1,
+            "m_id" : 2,
             "orientation" : "vertical"
         }
     """

app/lyric/worker/lyric_task.py

@@ -13,7 +13,7 @@ from sqlalchemy.exc import SQLAlchemyError
 from app.database.session import BackgroundSessionLocal
 from app.home.models import Image, Project, MarketingIntel
 from app.lyric.models import Lyric
-from app.utils.chatgpt_prompt import ChatgptService, ChatGPTResponseError
+from app.utils.prompts.chatgpt_prompt import ChatgptService, ChatGPTResponseError
 from app.utils.subtitles import SubtitleContentsGenerator
 from app.utils.creatomate import CreatomateService
 from app.utils.prompts.prompts import Prompt
@@ -104,13 +104,6 @@ async def generate_lyric_background(
         step1_start = time.perf_counter()
         logger.debug(f"[generate_lyric_background] Step 1: ChatGPT 서비스 초기화...")
 
-        # service = ChatgptService(
-        #     customer_name="",  # 프롬프트가 이미 생성되었으므로 빈 값
-        #     region="",
-        #     detail_region_info="",
-        #     language=language,
-        # )
-
         chatgpt = ChatgptService()
 
         step1_elapsed = (time.perf_counter() - step1_start) * 1000
@@ -169,7 +162,7 @@ async def generate_subtitle_background(
 ) -> None:
     logger.info(f"[generate_subtitle_background] task_id: {task_id}, {orientation}")
     creatomate_service = CreatomateService(orientation=orientation)
-    template = await creatomate_service.get_one_template_data_async(creatomate_service.template_id)
+    template = await creatomate_service.get_one_template_data(creatomate_service.template_id)
     pitchings = creatomate_service.extract_text_format_from_template(template)
 
     subtitle_generator = SubtitleContentsGenerator()

app/social/services/seo_service.py

@@ -17,7 +17,7 @@ from app.home.models import MarketingIntel, Project
 from app.social.constants import YOUTUBE_SEO_HASH
 from app.social.schemas import YoutubeDescriptionResponse
 from app.user.models import User
-from app.utils.chatgpt_prompt import ChatgptService
+from app.utils.prompts.chatgpt_prompt import ChatgptService
 from app.utils.prompts.prompts import yt_upload_prompt
 
 logger = logging.getLogger(__name__)

app/song/services/song.py

@@ -7,14 +7,14 @@ from sqlalchemy import Connection, text
 from sqlalchemy.exc import SQLAlchemyError
 
 from app.utils.logger import get_logger
-from app.lyrics.schemas.lyrics_schema import (
+from app.lyric.schemas.lyrics_schema import (
     AttributeData,
     PromptTemplateData,
     SongFormData,
     SongSampleData,
     StoreData,
 )
-from app.utils.chatgpt_prompt import chatgpt_api
+from app.utils.prompts.chatgpt_prompt import chatgpt_api
 
 logger = get_logger("song")
 

app/utils/autotag.py

@@ -0,0 +1,48 @@
+from pydantic.main import BaseModel
+
+from app.utils.prompts.chatgpt_prompt import ChatgptService
+from app.utils.prompts.prompts import image_autotag_prompt
+from app.utils.prompts.schemas import SpaceType, Subject, Camera, MotionRecommended
+
+import asyncio
+
+async def autotag_image(image_url : str) -> list[str]: #tag_list
+    chatgpt = ChatgptService(model_type="gemini")
+    image_input_data = {
+        "img_url" : image_url,
+        "space_type" : list(SpaceType),
+        "subject" : list(Subject),
+        "camera" : list(Camera),
+        "motion_recommended" : list(MotionRecommended)
+    }
+
+    image_result = await chatgpt.generate_structured_output(image_autotag_prompt, image_input_data, image_url, False)   
+    return image_result
+
+async def autotag_images(image_url_list : list[str]) -> list[dict]: #tag_list
+    chatgpt = ChatgptService(model_type="gemini")
+    image_input_data_list = [{ 
+        "img_url" : image_url,
+        "space_type" : list(SpaceType),
+        "subject" : list(Subject),
+        "camera" : list(Camera),
+        "motion_recommended" : list(MotionRecommended)
+    }for image_url in image_url_list]
+    
+    image_result_tasks = [chatgpt.generate_structured_output(image_autotag_prompt, image_input_data, image_input_data['img_url'], False, silent = True) for image_input_data in image_input_data_list] 
+    image_result_list: list[BaseModel | BaseException] = await asyncio.gather(*image_result_tasks, return_exceptions=True)
+    MAX_RETRY = 3 # 하드코딩, 어떻게 처리할지는 나중에
+    for _ in range(MAX_RETRY):
+        failed_idx = [i for i, r in enumerate(image_result_list) if isinstance(r, Exception)]
+        print("Failed", failed_idx)
+        if not failed_idx:
+            break
+        retried = await asyncio.gather(
+            *[chatgpt.generate_structured_output(image_autotag_prompt, image_input_data_list[i], image_input_data_list[i]['img_url'], False, silent=True) for i in failed_idx],
+            return_exceptions=True
+        )
+        for i, result in zip(failed_idx, retried):
+            image_result_list[i] = result
+
+    print("Failed", failed_idx)
+    return image_result_list

app/utils/chatgpt_prompt.py

@@ -1,95 +0,0 @@
-import json
-import re
-from pydantic import BaseModel
-from openai import AsyncOpenAI
-
-from app.utils.logger import get_logger
-from config import apikey_settings, recovery_settings
-from app.utils.prompts.prompts import Prompt
-
-
-# 로거 설정
-logger = get_logger("chatgpt")
-
-
-class ChatGPTResponseError(Exception):
-    """ChatGPT API 응답 에러"""
-    def __init__(self, status: str, error_code: str = None, error_message: str = None):
-        self.status = status
-        self.error_code = error_code
-        self.error_message = error_message
-        super().__init__(f"ChatGPT response failed: status={status}, code={error_code}, message={error_message}")
-
-
-class ChatgptService:
-    """ChatGPT API 서비스 클래스
-    """
-
-    def __init__(self, timeout: float = None):
-        self.timeout = timeout or recovery_settings.CHATGPT_TIMEOUT
-        self.max_retries = recovery_settings.CHATGPT_MAX_RETRIES
-        self.client = AsyncOpenAI(
-            api_key=apikey_settings.CHATGPT_API_KEY,
-            timeout=self.timeout
-        )
-    
-    async def _call_pydantic_output(self, prompt : str, output_format : BaseModel, model : str) -> BaseModel: # 입력 output_format의 경우 Pydantic BaseModel Class를 상속한 Class 자체임에 유의할 것
-        content = [{"type": "input_text", "text": prompt}]
-        last_error = None
-        for attempt in range(self.max_retries + 1):
-            response = await self.client.responses.parse(
-                model=model, 
-                input=[{"role": "user", "content": content}],
-                text_format=output_format
-            )
-            # Response 디버그 로깅
-            logger.debug(f"[ChatgptService] attempt: {attempt}")
-            logger.debug(f"[ChatgptService] Response ID: {response.id}")
-            logger.debug(f"[ChatgptService] Response status: {response.status}")
-            logger.debug(f"[ChatgptService] Response model: {response.model}")
-
-            # status 확인: completed, failed, incomplete, cancelled, queued, in_progress
-            if response.status == "completed":
-                logger.debug(f"[ChatgptService] Response output_text: {response.output_text[:200]}..." if len(response.output_text) > 200 else f"[ChatgptService] Response output_text: {response.output_text}")
-                structured_output = response.output_parsed
-                return structured_output #.model_dump() or {}
-
-            # 에러 상태 처리
-            if response.status == "failed":
-                error_code = getattr(response.error, 'code', None) if response.error else None
-                error_message = getattr(response.error, 'message', None) if response.error else None
-                logger.warning(f"[ChatgptService] Response failed (attempt {attempt + 1}/{self.max_retries + 1}): code={error_code}, message={error_message}")
-                last_error = ChatGPTResponseError(response.status, error_code, error_message)
-
-            elif response.status == "incomplete":
-                reason = getattr(response.incomplete_details, 'reason', None) if response.incomplete_details else None
-                logger.warning(f"[ChatgptService] Response incomplete (attempt {attempt + 1}/{self.max_retries + 1}): reason={reason}")
-                last_error = ChatGPTResponseError(response.status, reason, f"Response incomplete: {reason}")
-
-            else:
-                # cancelled, queued, in_progress 등 예상치 못한 상태
-                logger.warning(f"[ChatgptService] Unexpected response status (attempt {attempt + 1}/{self.max_retries + 1}): {response.status}")
-                last_error = ChatGPTResponseError(response.status, None, f"Unexpected status: {response.status}")
-
-            # 마지막 시도가 아니면 재시도
-            if attempt < self.max_retries:
-                logger.info(f"[ChatgptService] Retrying request...")
-
-        # 모든 재시도 실패
-        logger.error(f"[ChatgptService] All retries exhausted. Last error: {last_error}")
-        raise last_error
-    
-    async def generate_structured_output(
-        self,
-        prompt : Prompt,
-        input_data : dict,
-    ) -> BaseModel:
-        prompt_text = prompt.build_prompt(input_data)
-
-        logger.debug(f"[ChatgptService] Generated Prompt (length: {len(prompt_text)})")
-        logger.info(f"[ChatgptService] Starting GPT request with structured output with model: {prompt.prompt_model}")
-
-        # GPT API 호출
-        #response = await self._call_structured_output_with_response_gpt_api(prompt_text, prompt.prompt_output, prompt.prompt_model) 
-        response = await self._call_pydantic_output(prompt_text, prompt.prompt_output_class, prompt.prompt_model) 
-        return response

app/utils/creatomate.py

@@ -31,11 +31,13 @@ response = await creatomate.make_creatomate_call(template_id, modifications)
 
 import copy
 import time
+from enum import StrEnum
 from typing import Literal
-
+import traceback
 import httpx
 
 from app.utils.logger import get_logger
+from app.utils.prompts.schemas.image import SpaceType,Subject,Camera,MotionRecommended,NarrativePhase
 from config import apikey_settings, creatomate_settings, recovery_settings
 
 # 로거 설정
@@ -226,8 +228,9 @@ DVST0003 = "e1fb5b00-1f02-4f63-99fa-7524b433ba47"
 DHST0001 = "660be601-080a-43ea-bf0f-adcf4596fa98"
 DHST0002 = "3f194cc7-464e-4581-9db2-179d42d3e40f"
 DHST0003 = "f45df555-2956-4a13-9004-ead047070b3d"
+DVST0001T = "fe11aeab-ff29-4bc8-9f75-c695c7e243e6"
 HST_LIST = [DHST0001,DHST0002,DHST0003]
-VST_LIST = [DVST0001,DVST0002,DVST0003]
+VST_LIST = [DVST0001,DVST0002,DVST0003, DVST0001T]
 
 SCENE_TRACK = 1
 AUDIO_TRACK = 2
@@ -238,7 +241,7 @@ def select_template(orientation:OrientationType):
     if orientation == "horizontal":
         return DHST0001
     elif orientation == "vertical":
-        return DVST0001
+        return DVST0001T
     else:
         raise
 
@@ -399,14 +402,6 @@ class CreatomateService:
 
         return copy.deepcopy(data)
 
-    # 하위 호환성을 위한 별칭 (deprecated)
-    async def get_one_template_data_async(self, template_id: str) -> dict:
-        """특정 템플릿 ID로 템플릿 정보를 조회합니다.
-
-        Deprecated: get_one_template_data()를 사용하세요.
-        """
-        return await self.get_one_template_data(template_id)
-
     def parse_template_component_name(self, template_source: list) -> dict:
         """템플릿 정보를 파싱하여 리소스 이름을 추출합니다."""
 
@@ -434,42 +429,107 @@ class CreatomateService:
 
         return result
     
-    async def template_connect_resource_blackbox(
+    async def parse_template_name_tag(resource_name : str) -> list:
-        self,
+        tag_list = []
-        template_id: str,
+        tag_list = resource_name.split("_")
-        image_url_list: list[str],
+        
-        music_url: str,
+        return tag_list
-        address: str = None
+    
-    ) -> dict:
+    
-        """템플릿 정보와 이미지/가사/음악 리소스를 매핑합니다.
+    def counting_component(
+        self,
+        template : dict,
+        target_template_type : str
+    ) -> list:
+        source_elements = template["source"]["elements"]
+        template_component_data = self.parse_template_component_name(source_elements)
+        count = 0
+
+        for _, (_, template_type) in enumerate(template_component_data.items()):
+            if template_type == target_template_type:
+                count += 1
+        return count  
+        
+    def template_matching_taged_image(
+        self,
+        template : dict,
+        taged_image_list : list, # [{"image_name" : str , "image_tag" : dict}]
+        music_url: str,
+        address : str,
+        duplicate : bool = False
+    ) -> list:
+        source_elements = template["source"]["elements"]
+        template_component_data = self.parse_template_component_name(source_elements)
 
-        Note:
-            - 이미지는 순차적으로 집어넣기
-            - 가사는 개행마다 한 텍스트 삽입
-            - Template에 audio-music 항목이 있어야 함
-        """
-        template_data = await self.get_one_template_data(template_id)
-        template_component_data = self.parse_template_component_name(
-            template_data["source"]["elements"]
-        )
         modifications = {}
 
-        for idx, (template_component_name, template_type) in enumerate(
+        for slot_idx, (template_component_name, template_type) in enumerate(template_component_data.items()):
-            template_component_data.items()
-        ):
             match template_type:
                 case "image":
-                    modifications[template_component_name] = image_url_list[
+                    image_score_list = self.calculate_image_slot_score_multi(taged_image_list, template_component_name)
-                        idx % len(image_url_list)
+                    maximum_idx = image_score_list.index(max(image_score_list))
-                    ]
+                    if duplicate:
+                        selected = taged_image_list[maximum_idx]
+                    else:
+                        selected = taged_image_list.pop(maximum_idx)
+                    image_name = selected["image_url"]
+                    modifications[template_component_name] =image_name
+                    pass
                 case "text":
                     if "address_input" in template_component_name:
                         modifications[template_component_name] = address
 
         modifications["audio-music"] = music_url
-
         return modifications
                 
+    def calculate_image_slot_score_multi(self, taged_image_list : list[dict], slot_name : str):
+        image_tag_list = [taged_image["image_tag"] for taged_image in taged_image_list]
+        slot_tag_dict = self.parse_slot_name_to_tag(slot_name)
+        image_score_list = [0] * len(image_tag_list)
+        
+        for slot_tag_cate, slot_tag_item in slot_tag_dict.items():
+            if slot_tag_cate == "narrative_preference":
+                slot_tag_narrative = slot_tag_item
+                continue
+
+            match slot_tag_cate:
+                case "space_type":
+                    weight = 2
+                case "subject" :
+                    weight = 2
+                case "camera":
+                    weight = 1
+                case "motion_recommended" :
+                    weight = 0.5
+                case _:
+                    raise
+
+            for idx, image_tag in enumerate(image_tag_list):
+                if slot_tag_item.value in image_tag[slot_tag_cate]: #collect!
+                    image_score_list[idx] += weight
+
+        for idx, image_tag in enumerate(image_tag_list):
+            image_narrative_score = image_tag["narrative_preference"][slot_tag_narrative]
+            image_score_list[idx] = image_score_list[idx] * image_narrative_score
+
+        return image_score_list
+    
+    def parse_slot_name_to_tag(self, slot_name : str) -> dict[str, StrEnum]:
+        tag_list = slot_name.split("-")
+        space_type = SpaceType(tag_list[0])
+        subject = Subject(tag_list[1])
+        camera = Camera(tag_list[2])
+        motion = MotionRecommended(tag_list[3])
+        narrative = NarrativePhase(tag_list[4])
+        tag_dict = {
+            "space_type" : space_type,
+            "subject" : subject,
+            "camera" : camera,
+            "motion_recommended" : motion,
+            "narrative_preference" : narrative,
+        }
+        return tag_dict
+
     def elements_connect_resource_blackbox(
         self,
         elements: list,
@@ -669,14 +729,6 @@ class CreatomateService:
             original_response={"last_error": str(last_error)},
         )
 
-    # 하위 호환성을 위한 별칭 (deprecated)
-    async def make_creatomate_custom_call_async(self, source: dict) -> dict:
-        """템플릿 없이 Creatomate에 커스텀 렌더링 요청을 보냅니다.
-
-        Deprecated: make_creatomate_custom_call()을 사용하세요.
-        """
-        return await self.make_creatomate_custom_call(source)
-
     async def get_render_status(self, render_id: str) -> dict:
         """렌더링 작업의 상태를 조회합니다.
 
@@ -700,14 +752,6 @@ class CreatomateService:
         response.raise_for_status()
         return response.json()
 
-    # 하위 호환성을 위한 별칭 (deprecated)
-    async def get_render_status_async(self, render_id: str) -> dict:
-        """렌더링 작업의 상태를 조회합니다.
-
-        Deprecated: get_render_status()를 사용하세요.
-        """
-        return await self.get_render_status(render_id)
-
     def calc_scene_duration(self, template: dict) -> float:
         """템플릿의 전체 장면 duration을 계산합니다."""
         total_template_duration = 0.0
@@ -720,19 +764,20 @@ class CreatomateService:
             try:
                 if elem["track"] not in track_maximum_duration:
                     continue
-                if elem["time"] == 0: # elem is auto / 만약 마지막 elem이 auto인데 그 앞에 time이 있는 elem 일 시 버그 발생 확률 있음
+                if "time" not in elem or elem["time"] == 0: # elem is auto / 만약 마지막 elem이 auto인데 그 앞에 time이 있는 elem 일 시 버그 발생 확률 있음
                     track_maximum_duration[elem["track"]] += elem["duration"]
 
                     if "animations" not in elem:
                         continue
                     for animation in elem["animations"]:
                         assert animation["time"] == 0  # 0이 아닌 경우 확인 필요
-                        if animation["transition"]:
+                        if "transition" in animation and animation["transition"]:
                             track_maximum_duration[elem["track"]] -= animation["duration"]
                 else:
                     track_maximum_duration[elem["track"]] = max(track_maximum_duration[elem["track"]], elem["time"] + elem["duration"])
 
             except Exception as e:
+                logger.debug(traceback.format_exc())
                 logger.error(f"[calc_scene_duration] Error processing element: {elem}, {e}")
 
         total_template_duration = max(track_maximum_duration.values())

app/utils/nvMapScraper.py

@@ -16,6 +16,10 @@ class GraphQLException(Exception):
     """GraphQL 요청 실패 시 발생하는 예외"""
     pass
 
+class URLNotFoundException(Exception):
+    """Place ID 발견 불가능 시 발생하는 예외"""
+    pass
+
 
 class CrawlingTimeoutException(Exception):
     """크롤링 타임아웃 시 발생하는 예외"""
@@ -86,15 +90,14 @@ query getAccommodation($id: String!, $deviceType: String) {
                     async with session.get(self.url) as response:
                         self.url = str(response.url)
             else:
-                raise GraphQLException("This URL does not contain a place ID")
+                raise URLNotFoundException("This URL does not contain a place ID")
 
         match = re.search(place_pattern, self.url)
         if not match:
-            raise GraphQLException("Failed to parse place ID from URL")
+            raise URLNotFoundException("Failed to parse place ID from URL")
         return match[1]
 
     async def scrap(self):
-        try:
         place_id = await self.parse_url()
         data = await self._call_get_accommodation(place_id)
         self.rawdata = data
@@ -110,11 +113,6 @@ query getAccommodation($id: String!, $deviceType: String) {
         self.facility_info = fac_data
         self.scrap_type = "GraphQL"
 
-        except GraphQLException:
-            logger.debug("GraphQL failed, fallback to Playwright")
-            self.scrap_type = "Playwright"
-            pass  # 나중에 pw 이용한 crawling으로 fallback 추가
-
         return
 
     async def _call_get_accommodation(self, place_id: str) -> dict:

app/utils/prompts/chatgpt_prompt.py

@@ -0,0 +1,191 @@
+import json
+import re
+from pydantic import BaseModel
+from typing import List, Optional
+from openai import AsyncOpenAI
+
+from app.utils.logger import get_logger
+from config import apikey_settings, recovery_settings
+from app.utils.prompts.prompts import Prompt
+
+
+# 로거 설정
+logger = get_logger("chatgpt")
+
+
+class ChatGPTResponseError(Exception):
+    """ChatGPT API 응답 에러"""
+    def __init__(self, status: str, error_code: str = None, error_message: str = None):
+        self.status = status
+        self.error_code = error_code
+        self.error_message = error_message
+        super().__init__(f"ChatGPT response failed: status={status}, code={error_code}, message={error_message}")
+
+
+class ChatgptService:
+    """ChatGPT API 서비스 클래스
+    """
+
+    model_type : str
+
+    def __init__(self, model_type:str = "gpt", timeout: float = None):
+        self.timeout = timeout or recovery_settings.CHATGPT_TIMEOUT
+        self.max_retries = recovery_settings.CHATGPT_MAX_RETRIES
+        self.model_type = model_type
+        match model_type:
+            case "gpt":
+                self.client = AsyncOpenAI(
+                    api_key=apikey_settings.CHATGPT_API_KEY,
+                    timeout=self.timeout
+                )
+            case "gemini":
+                self.client = AsyncOpenAI(
+                    api_key=apikey_settings.GEMINI_API_KEY,
+                    base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
+                    timeout=self.timeout
+                )
+            case _:
+                raise NotImplementedError(f"Unknown Provider : {model_type}")
+    
+    async def _call_pydantic_output(
+            self, 
+            prompt : str, 
+            output_format : BaseModel,  #입력 output_format의 경우 Pydantic BaseModel Class를 상속한 Class 자체임에 유의할 것
+            model : str, 
+            img_url : str,
+            image_detail_high : bool) -> BaseModel: 
+        content = []
+        if img_url:
+            content.append({
+                "type" : "input_image",
+                "image_url" : img_url,
+                "detail": "high" if image_detail_high else "low" 
+            })
+        content.append({
+            "type": "input_text", 
+            "text": prompt}
+        )
+        last_error = None
+        for attempt in range(self.max_retries + 1):
+            response = await self.client.responses.parse(
+                model=model, 
+                input=[{"role": "user", "content": content}],
+                text_format=output_format
+            )
+            # Response 디버그 로깅
+            logger.debug(f"[ChatgptService({self.model_type})] attempt: {attempt}")
+            logger.debug(f"[ChatgptService({self.model_type})] Response ID: {response.id}")
+            logger.debug(f"[ChatgptService({self.model_type})] Response status: {response.status}")
+            logger.debug(f"[ChatgptService({self.model_type})] Response model: {response.model}")
+
+            # status 확인: completed, failed, incomplete, cancelled, queued, in_progress
+            if response.status == "completed":
+                logger.debug(f"[ChatgptService({self.model_type})] Response output_text: {response.output_text[:200]}..." if len(response.output_text) > 200 else f"[ChatgptService] Response output_text: {response.output_text}")
+                structured_output = response.output_parsed
+                return structured_output #.model_dump() or {}
+
+            # 에러 상태 처리
+            if response.status == "failed":
+                error_code = getattr(response.error, 'code', None) if response.error else None
+                error_message = getattr(response.error, 'message', None) if response.error else None
+                logger.warning(f"[ChatgptService({self.model_type})] Response failed (attempt {attempt + 1}/{self.max_retries + 1}): code={error_code}, message={error_message}")
+                last_error = ChatGPTResponseError(response.status, error_code, error_message)
+
+            elif response.status == "incomplete":
+                reason = getattr(response.incomplete_details, 'reason', None) if response.incomplete_details else None
+                logger.warning(f"[ChatgptService({self.model_type})] Response incomplete (attempt {attempt + 1}/{self.max_retries + 1}): reason={reason}")
+                last_error = ChatGPTResponseError(response.status, reason, f"Response incomplete: {reason}")
+
+            else:
+                # cancelled, queued, in_progress 등 예상치 못한 상태
+                logger.warning(f"[ChatgptService({self.model_type})] Unexpected response status (attempt {attempt + 1}/{self.max_retries + 1}): {response.status}")
+                last_error = ChatGPTResponseError(response.status, None, f"Unexpected status: {response.status}")
+
+            # 마지막 시도가 아니면 재시도
+            if attempt < self.max_retries:
+                logger.info(f"[ChatgptService({self.model_type})] Retrying request...")
+
+        # 모든 재시도 실패
+        logger.error(f"[ChatgptService({self.model_type})] All retries exhausted. Last error: {last_error}")
+        raise last_error
+    
+    async def _call_pydantic_output_chat_completion( # alter version
+            self, 
+            prompt : str, 
+            output_format : BaseModel,  #입력 output_format의 경우 Pydantic BaseModel Class를 상속한 Class 자체임에 유의할 것
+            model : str, 
+            img_url : str,
+            image_detail_high : bool) -> BaseModel: 
+        content = []
+        if img_url:
+            content.append({
+                "type": "image_url",
+                "image_url": {
+                    "url": img_url,
+                    "detail": "high" if image_detail_high else "low"
+                }
+            })
+        content.append({
+            "type": "text",
+            "text": prompt
+        })
+        last_error = None
+        for attempt in range(self.max_retries + 1):
+            response = await self.client.beta.chat.completions.parse(
+                model=model,
+                messages=[{"role": "user", "content": content}],
+                response_format=output_format
+            )
+            # Response 디버그 로깅
+            logger.debug(f"[ChatgptService({self.model_type})] attempt: {attempt}")
+            logger.debug(f"[ChatgptService({self.model_type})] Response ID: {response.id}")
+            logger.debug(f"[ChatgptService({self.model_type})] Response finish_reason: {response.id}")
+            logger.debug(f"[ChatgptService({self.model_type})] Response model: {response.model}")
+
+            choice = response.choices[0]
+            finish_reason = choice.finish_reason
+
+            if finish_reason == "stop":
+                output_text = choice.message.content or ""
+                logger.debug(f"[ChatgptService({self.model_type})] Response output_text: {output_text[:200]}..." if len(output_text) > 200 else f"[ChatgptService] Response output_text: {output_text}")
+                return choice.message.parsed
+
+            elif finish_reason == "length":
+                logger.warning(f"[ChatgptService({self.model_type})] Response incomplete - token limit reached (attempt {attempt + 1}/{self.max_retries + 1})")
+                last_error = ChatGPTResponseError("incomplete", finish_reason, "Response incomplete: max tokens reached")
+
+            elif finish_reason == "content_filter":
+                logger.warning(f"[ChatgptService({self.model_type})] Response blocked by content filter (attempt {attempt + 1}/{self.max_retries + 1})")
+                last_error = ChatGPTResponseError("failed", finish_reason, "Response blocked by content filter")
+
+            else:
+                logger.warning(f"[ChatgptService({self.model_type})] Unexpected finish_reason (attempt {attempt + 1}/{self.max_retries + 1}): {finish_reason}")
+                last_error = ChatGPTResponseError("failed", finish_reason, f"Unexpected finish_reason: {finish_reason}")
+
+            # 마지막 시도가 아니면 재시도
+            if attempt < self.max_retries:
+                logger.info(f"[ChatgptService({self.model_type})] Retrying request...")
+
+        # 모든 재시도 실패
+        logger.error(f"[ChatgptService({self.model_type})] All retries exhausted. Last error: {last_error}")
+        raise last_error
+    
+    async def generate_structured_output(
+        self,
+        prompt : Prompt,
+        input_data : dict,
+        img_url : Optional[str] = None,
+        img_detail_high : bool = False,
+        silent : bool = False
+    ) -> BaseModel:
+        prompt_text = prompt.build_prompt(input_data, silent)
+    
+        logger.debug(f"[ChatgptService({self.model_type})] Generated Prompt (length: {len(prompt_text)})")
+        if not silent:
+            logger.info(f"[ChatgptService({self.model_type})] Starting GPT request with structured output with model: {prompt.prompt_model}")
+
+        # GPT API 호출
+        #parsed = await self._call_structured_output_with_response_gpt_api(prompt_text, prompt.prompt_output, prompt.prompt_model) 
+        # parsed = await self._call_pydantic_output(prompt_text, prompt.prompt_output_class, prompt.prompt_model, img_url, img_detail_high) 
+        parsed = await self._call_pydantic_output_chat_completion(prompt_text, prompt.prompt_output_class, prompt.prompt_model, img_url, img_detail_high) 
+        return parsed

app/utils/prompts/prompts.py

@@ -1,5 +1,6 @@
-import os, json
+import gspread
 from pydantic import BaseModel
+from google.oauth2.service_account import Credentials
 from config import prompt_settings
 from app.utils.logger import get_logger
 from app.utils.prompts.schemas import *
@@ -7,69 +8,82 @@ from functools import lru_cache
 
 logger = get_logger("prompt")
 
+_SCOPES = [
+    "https://www.googleapis.com/auth/spreadsheets.readonly", 
+    "https://www.googleapis.com/auth/drive.readonly"
+]
+
 class Prompt():
-    prompt_template_path : str #프롬프트 경로
+    sheet_name: str
-    prompt_template : str # fstring 포맷
+    prompt_template: str
     prompt_model: str
 
-    prompt_input_class = BaseModel # pydantic class 자체를(instance 아님) 변수로 가짐
+    prompt_input_class = BaseModel
     prompt_output_class = BaseModel
 
-    def __init__(self, prompt_template_path, prompt_input_class, prompt_output_class, prompt_model):
+    def __init__(self, sheet_name, prompt_input_class, prompt_output_class):
-        self.prompt_template_path = prompt_template_path
+        self.sheet_name = sheet_name
         self.prompt_input_class = prompt_input_class
         self.prompt_output_class = prompt_output_class
-        self.prompt_template = self.read_prompt()
+        self.prompt_template, self.prompt_model = self._read_from_sheets()
-        self.prompt_model = prompt_model
+
+    def _read_from_sheets(self) -> tuple[str, str]:
+        creds = Credentials.from_service_account_file(
+            prompt_settings.GOOGLE_SERVICE_ACCOUNT_JSON, scopes=_SCOPES
+        )
+        gc = gspread.authorize(creds)
+        ws = gc.open_by_key(prompt_settings.PROMPT_SPREADSHEET).worksheet(self.sheet_name)
+        model = ws.cell(2, 2).value
+        input_text = ws.cell(3, 2).value
+        return input_text, model
 
     def _reload_prompt(self):
-        self.prompt_template = self.read_prompt()
+        self.prompt_template, self.prompt_model = self._read_from_sheets()
 
-    def read_prompt(self) -> tuple[str, dict]:
+    def build_prompt(self, input_data:dict, silent:bool = False) -> str:
-        with open(self.prompt_template_path, "r") as fp:
-            prompt_template = fp.read()
-
-        return prompt_template
-
-    def build_prompt(self, input_data:dict) -> str:
         verified_input = self.prompt_input_class(**input_data)
         build_template = self.prompt_template
         build_template = build_template.format(**verified_input.model_dump())
+        if not silent:
             logger.debug(f"build_template: {build_template}")
             logger.debug(f"input_data: {input_data}")
         return build_template
 
 marketing_prompt = Prompt(
-    prompt_template_path = os.path.join(prompt_settings.PROMPT_FOLDER_ROOT, prompt_settings.MARKETING_PROMPT_FILE_NAME),
+    sheet_name="marketing",
     prompt_input_class=MarketingPromptInput,
     prompt_output_class=MarketingPromptOutput,
-    prompt_model = prompt_settings.MARKETING_PROMPT_MODEL
 )
 
 lyric_prompt = Prompt(
-    prompt_template_path=os.path.join(prompt_settings.PROMPT_FOLDER_ROOT, prompt_settings.LYRIC_PROMPT_FILE_NAME),
+    sheet_name="lyric",
     prompt_input_class=LyricPromptInput,
     prompt_output_class=LyricPromptOutput,
-    prompt_model = prompt_settings.LYRIC_PROMPT_MODEL
 )
 
 yt_upload_prompt = Prompt(
-    prompt_template_path=os.path.join(prompt_settings.PROMPT_FOLDER_ROOT, prompt_settings.YOUTUBE_PROMPT_FILE_NAME),
+    sheet_name="yt_upload",
     prompt_input_class=YTUploadPromptInput,
     prompt_output_class=YTUploadPromptOutput,
-    prompt_model = prompt_settings.YOUTUBE_PROMPT_MODEL
+)
+
+image_autotag_prompt = Prompt(
+    sheet_name="image_tag",
+    prompt_input_class=ImageTagPromptInput,
+    prompt_output_class=ImageTagPromptOutput,
 )
 
 @lru_cache()
 def create_dynamic_subtitle_prompt(length: int) -> Prompt:
-    prompt_template_path=os.path.join(prompt_settings.PROMPT_FOLDER_ROOT, prompt_settings.SUBTITLE_PROMPT_FILE_NAME)
+    return Prompt(
-    prompt_input_class = SubtitlePromptInput
+        sheet_name="subtitle",
-    prompt_output_class = SubtitlePromptOutput[length]
+        prompt_input_class=SubtitlePromptInput,
-    prompt_model = prompt_settings.SUBTITLE_PROMPT_MODEL
+        prompt_output_class=SubtitlePromptOutput[length],
-    return Prompt(prompt_template_path, prompt_input_class, prompt_output_class, prompt_model)
+    )
 
 
 def reload_all_prompt():
     marketing_prompt._reload_prompt()
     lyric_prompt._reload_prompt()
     yt_upload_prompt._reload_prompt()
+    image_autotag_prompt._reload_prompt()

app/utils/prompts/schemas/__init__.py

@@ -1,4 +1,5 @@
 from .lyric import LyricPromptInput, LyricPromptOutput
 from .marketing import MarketingPromptInput, MarketingPromptOutput
 from .youtube import YTUploadPromptInput, YTUploadPromptOutput
+from .image import *
 from .subtitle import SubtitlePromptInput, SubtitlePromptOutput

app/utils/prompts/schemas/image.py

@@ -0,0 +1,110 @@
+from pydantic import BaseModel, Field
+from typing import List, Optional
+from enum import StrEnum, auto
+
+class SpaceType(StrEnum):
+    exterior_front = auto()
+    exterior_night = auto()
+    exterior_aerial = auto()
+    exterior_sign = auto()
+    garden = auto()
+    entrance = auto()
+    lobby = auto()
+    reception = auto()
+    hallway = auto()
+    bedroom = auto()
+    livingroom = auto()
+    kitchen = auto()
+    dining = auto()
+    room = auto()
+    bathroom = auto()
+    amenity = auto()
+    view_window = auto()
+    view_ocean = auto()
+    view_city = auto()
+    view_mountain = auto()
+    balcony = auto()
+    cafe = auto()
+    lounge  = auto()
+    rooftop  = auto()
+    pool = auto()
+    breakfast_hall  = auto()
+    spa = auto()
+    fitness = auto()
+    bbq = auto()
+    terrace = auto()
+    glamping = auto()
+    neighborhood  = auto()
+    landmark = auto()
+    detail_welcome = auto()
+    detail_beverage = auto()
+    detail_lighting  = auto()
+    detail_decor = auto()
+    detail_tableware = auto()
+
+class Subject(StrEnum):
+    empty_space = auto()
+    exterior_building = auto()
+    architecture_detail = auto()
+    decoration = auto()
+    furniture = auto()
+    food_dish = auto()
+    nature = auto()
+    signage = auto()
+    amenity_item = auto()
+    person = auto()
+
+class Camera(StrEnum):
+    wide_angle = auto()
+    tight_crop = auto()
+    panoramic = auto()
+    symmetrical = auto()
+    leading_line = auto()
+    golden_hour = auto()
+    night_shot = auto()
+    high_contrast = auto()
+    low_light = auto()
+    drone_shot = auto()
+    has_face = auto()
+
+class MotionRecommended(StrEnum):
+    static = auto()
+    slow_pan = auto()
+    slow_zoom_in = auto()
+    slow_zoom_out = auto()
+    walkthrough = auto()
+    dolly = auto()
+
+class NarrativePhase(StrEnum):
+    intro = auto()
+    welcome = auto()
+    core = auto()
+    highlight = auto()
+    support = auto()
+    accent = auto()
+
+class NarrativePreference(BaseModel):
+    intro: float = Field(..., description="첫인상 — 여기가 어디인가 | 장소의 정체성과 위치를 전달하는 이미지. 영상 첫 1~2초에 어떤 곳인지 즉시 인지시키는 역할. 건물 외관, 간판, 정원 등 **장소 자체를 보여주는** 컷")
+    welcome: float = Field(..., description="진입/환영 — 어떻게 들어가나 | 도착 후 내부로 들어가는 경험을 전달하는 이미지. 공간의 첫 분위기와 동선을 보여줘 들어가고 싶다는 기대감을 만드는 역할. **문을 열고 들어갔을 때 보이는** 컷.")
+    core: float = Field(..., description="핵심 가치 — 무엇을 경험하나 | **고객이 이 장소를 찾는 본질적 이유.** 이 이미지가 없으면 영상 자체가 성립하지 않음. 질문: 이 비즈니스에서 돈을 지불하는 대상이 뭔가? → 그 답이 core.")
+    highlight: float = Field(..., description="차별화 — 뭐가 특별한가 | **같은 카테고리의 경쟁사 대비 이곳을 선택하게 만드는 이유.** core가 왜 왔는가라면, highlight는 왜 **여기**인가에 대한 답.")
+    support: float = Field(..., description="보조/부대 — 그 외에 뭐가 있나 | 핵심은 아니지만 전체 경험을 풍성하게 하는 부가 요소. 없어도 영상은 성립하지만, 있으면 설득력이 올라감. **이것도 있어요** 라고 말하는 컷.")
+    accent: float = Field(..., description="감성/마무리 — 어떤 느낌인가 | 공간의 분위기와 톤을 전달하는 감성 디테일 컷. 직접적 정보 전달보다 **느낌과 무드**를 제공. 영상 사이사이에 삽입되어 완성도를 높이는 역할.")
+
+# Input 정의
+class ImageTagPromptInput(BaseModel):
+    img_url : str = Field(..., description="이미지 URL")
+    space_type: list[str] = Field(list(SpaceType), description="공간적 정보를 가지는 태그 리스트")
+    subject: list[str] = Field(list(Subject), description="피사체 정보를 가지는 태그 리스트")
+    camera: list[str] = Field(list(Camera), description="카메라 정보를 가지는 태그 리스트")
+    motion_recommended: list[str] = Field(list(MotionRecommended), description="가능한 카메라 모션 리스트")
+
+# Output 정의
+class ImageTagPromptOutput(BaseModel):
+    #ad_avaliable : bool = Field(..., description="광고 영상 사용 가능 이미지 여부")
+    space_type: list[SpaceType] = Field(..., description="공간적 정보를 가지는 태그 리스트")
+    subject: list[Subject] = Field(..., description="피사체 정보를 가지는 태그 리스트")
+    camera: list[Camera] = Field(..., description="카메라 정보를 가지는 태그 리스트")
+    motion_recommended: list[MotionRecommended] = Field(..., description="가능한 카메라 모션 리스트")
+    narrative_preference: NarrativePreference = Field(..., description="이미지의 내러티브 상 점수")
+

app/utils/prompts/schemas/marketing.py

@@ -7,13 +7,11 @@ class MarketingPromptInput(BaseModel):
     region : str = Field(..., description = "마케팅 대상 지역")
     detail_region_info : str = Field(..., description = "마케팅 대상 지역 상세")
 
-
 # Output 정의
 class BrandIdentity(BaseModel):
     location_feature_analysis: str = Field(..., description="입지 특성 분석 (80자 이상 150자 이하)", min_length = 80, max_length = 150) # min/max constraint는 현재 openai json schema 등에서 작동하지 않는다는 보고가 있음. 
     concept_scalability: str = Field(..., description="컨셉 확장성 (80자 이상 150자 이하)", min_length = 80, max_length = 150)
 
-
 class MarketPositioning(BaseModel):
     category_definition: str = Field(..., description="마케팅 카테고리")
     core_value: str = Field(..., description="마케팅 포지션 핵심 가치")
@@ -22,14 +20,12 @@ class AgeRange(BaseModel):
     min_age : int = Field(..., ge=0, le=100)
     max_age : int = Field(..., ge=0, le=100)
 
-
 class TargetPersona(BaseModel):
     persona: str = Field(..., description="타겟 페르소나 이름/설명")
     age: AgeRange = Field(..., description="타겟 페르소나 나이대")
     favor_target: List[str] = Field(..., description="페르소나의 선호 요소")
     decision_trigger: str = Field(..., description="구매 결정 트리거")
 
-
 class SellingPoint(BaseModel):
     english_category: str = Field(..., description="셀링포인트 카테고리(영문)")
     korean_category: str = Field(..., description="셀링포인트 카테고리(한글)")

app/utils/prompts/templates/20260129_marketing_prompt.txt

@@ -1,64 +0,0 @@
-
-[Role & Objective]
-Act as a content marketing expert with strong domain knowledge in the Korean pension / stay-accommodation industry.
-Your goal is to produce a Marketing Intelligence Report that will be shown to accommodation owners BEFORE any content is generated.
-The report must clearly explain what makes the property sellable, marketable, and scalable through content.
-
-[INPUT]
-- Business Name: {customer_name}
-- Region: {region}
-- Region Details: {detail_region_info}
-
-[Core Analysis Requirements]
-Analyze the property based on:
-Location, concept, and nearby environment
-Target customer behavior and reservation decision factors
-Include:
-- Target customer segments & personas
-- Unique Selling Propositions (USPs)
-- Competitive landscape (direct & indirect competitors)
-- Market positioning
-
-[Key Selling Point Structuring – UI Optimized]
-From the analysis above, extract the main Key Selling Points using the structure below.
-Rules:
-Focus only on factors that directly influence booking decisions
-Each selling point must be concise and visually scannable
-Language must be reusable for ads, short-form videos, and listing headlines
-Avoid full sentences in descriptions; use short selling phrases
-Do not provide in report
-
-Output format:
-[Category]
-(Tag keyword – 5~8 words, noun-based, UI oval-style)
-One-line selling phrase (not a full sentence)
-Limit:
-5 to 8 Key Selling Points only
-Do not provide in report
-
-[Content & Automation Readiness Check]
-Ensure that:
-Each tag keyword can directly map to a content theme
-Each selling phrase can be used as:
-- Video hook
-- Image headline
-- Ad copy snippet
-
-
-[Tag Generation Rules]
-- Tags must include **only core keywords that can be directly used for viral video song lyrics**
-- Each tag should be selected with **search discovery + emotional resonance + reservation conversion** in mind
-- The number of tags must be **exactly 5**
-- Tags must be **nouns or short keyword phrases**; full sentences are strictly prohibited
-- The following categories must be **balanced and all represented**:
-  1) **Location / Local context** (region name, neighborhood, travel context)
-  2) **Accommodation positioning** (emotional stay, private stay, boutique stay, etc.)
-  3) **Emotion / Experience** (healing, rest, one-day escape, memory, etc.)
-  4) **SNS / Viral signals** (Instagram vibes, picture-perfect day, aesthetic travel, etc.)
-  5) **Travel & booking intent** (travel, getaway, stay, relaxation, etc.)
-
-- If a brand name exists, **at least one tag must include the brand name or a brand-specific expression**
-- Avoid overly generic keywords (e.g., “hotel”, “travel” alone); **prioritize distinctive, differentiating phrases**
-- The final output must strictly follow the JSON format below, with no additional text
-
-  "tags": ["Tag1", "Tag2", "Tag3", "Tag4", "Tag5"]

app/utils/prompts/templates/lyric_prompt.txt

@@ -1,77 +0,0 @@
-
-[ROLE]
-You are a content marketing expert, brand strategist, and creative songwriter
-specializing in Korean pension / accommodation businesses.
-You create lyrics strictly based on Brand & Marketing Intelligence analysis
-and optimized for viral short-form video content.
-Marketing Intelligence Report is background reference.
-
-[INPUT]
-Business Name: {customer_name}
-Region: {region}
-Region Details: {detail_region_info}
-Brand & Marketing Intelligence Report: {marketing_intelligence_summary}
-Output Language: {language}
-
-[INTERNAL ANALYSIS – DO NOT OUTPUT]
-Internally analyze the following to guide all creative decisions:
-- Core brand identity and positioning
-- Emotional hooks derived from selling points
-- Target audience lifestyle, desires, and travel motivation
-- Regional atmosphere and symbolic imagery
-- How the stay converts into “shareable moments”
-- Which selling points must surface implicitly in lyrics
-
-[LYRICS & MUSIC CREATION TASK]
-Based on the Brand & Marketing Intelligence Report for [{customer_name} ({region})], generate:
-- Original promotional lyrics
-- Music attributes for AI music generation (Suno-compatible prompt)
-The output must be designed for VIRAL DIGITAL CONTENT
-(short-form video, reels, ads).
-
-[LYRICS REQUIREMENTS]
-Mandatory Inclusions:
-- Business name
-- Region name
-- Promotion subject
-- Promotional expressions including:
-{promotional_expression_example}
-
-Content Rules:
-- Lyrics must be emotionally driven, not descriptive listings
-- Selling points must be IMPLIED, not explained
-- Must sound natural when sung
-- Must feel like a lifestyle moment, not an advertisement
-
-Tone & Style:
-- Warm, emotional, and aspirational
-- Trendy, viral-friendly phrasing
-- Calm but memorable hooks
-- Suitable for travel / stay-related content
-
-[SONG & MUSIC ATTRIBUTES – FOR SUNO PROMPT]
-After the lyrics, generate a concise music prompt including:
-Song mood (emotional keywords)
-BPM range
-Recommended genres (max 2)
-Key musical motifs or instruments
-Overall vibe (1 short sentence)
-
-[CRITICAL LANGUAGE REQUIREMENT – ABSOLUTE RULE]
-ALL OUTPUT MUST BE 100% WRITTEN IN {language}.
-no mixed languages
-All names, places, and expressions must be in {language} 
-Any violation invalidates the entire output
-
-[OUTPUT RULES – STRICT]
-{timing_rules}
-
-No explanations
-No headings
-No bullet points
-No analysis
-No extra text
-
-[FAILURE FORMAT]
-If generation is impossible:
-ERROR: Brief reason in English

app/utils/prompts/templates/marketing_prompt.txt

@@ -1,42 +0,0 @@
-# Role
-Act as a Senior Brand Strategist and Marketing Data Analyst. Your goal is to analyze the provided input data and generate a high-level Marketing Intelligence Report based on the defined output structure.
-
-# Input Data
-*   **Customer Name:** {customer_name}
-*   **Region:** {region}
-*   **Detail Region Info:** {detail_region_info}
-
-# Output Rules
-1.  **Language:** All descriptive content must be written in **Korean (한국어)**.
-2.  **Terminology:** Use professional marketing terminology suitable for the hospitality and stay industry.
-3.  **Strict Selection for `selling_points.english_category` and `selling_points.korean_category`:** You must select the value for both category field in `selling_points` strictly from the following English - Korean set allowed list to ensure UI compatibility:
-    *  `LOCATION` (입지 환경), `CONCEPT` (브랜드 컨셉), `PRIVACY` (프라이버시), `NIGHT MOOD` (야간 감성), `HEALING` (힐링 요소), `PHOTO SPOT` (포토 스팟), `SHORT GETAWAY` (숏브레이크), `HOSPITALITY` (서비스), `SWIMMING POOL` (수영장), `JACUZZI` (자쿠지), `BBQ PARTY` (바베큐), `FIRE PIT` (불멍), `GARDEN` (정원), `BREAKFAST` (조식), `KIDS FRIENDLY` (키즈 케어), `PET FRIENDLY` (애견 동반), `OCEAN VIEW` (오션뷰), `PRIVATE POOL` (개별 수영장), `OCEAN VIEW`, `PRIVATE POOL`.
-
----
-
-# Instruction per Output Field (Mapping Logic)
-
-### 1. brand_identity
-*   **`location_feature_analysis`**: Analyze the marketing advantages of the given `{region}` and `{detail_region_info}`. Explain why this specific location is attractive to travelers. summarize in 1-2 sentences. (e.g., proximity to nature, accessibility from Seoul, or unique local atmosphere).
-*   **`concept_scalability`**: Based on `{customer_name}`, analyze how the brand's core concept can expand into a total customer experience or additional services. summarize in 1-2 sentences.
-
-### 2. market_positioning
-*   **`category_definition`**: Define a sharp, niche market category for this business (e.g., "Private Forest Cabin" or "Luxury Kids Pool Villa").
-*   **`core_value`**: Identify the single most compelling emotional or functional value that distinguishes `{customer_name}` from competitors.
-
-### 3. target_persona
-Generate a list of personas based on the following:
-*   **`persona`**: Provide a descriptive name and profile for the target group.
-*   **`age`**: Set `min_age` and `max_age` (Integer 0-100) that accurately reflects the segment.
-*   **`favor_target`**: List specific elements or vibes this persona prefers (e.g., "Minimalist interior", "Pet-friendly facilities").
-*   **`decision_trigger`**: Identify the specific "Hook" or facility that leads this persona to finalize a booking.
-
-### 4. selling_points
-Generate 5-8 selling points:
-*   **`english_category`**: Strictly use one keyword from the English allowed list provided in the Output Rules.
-*   **`korean category`**: Strictly use one keyword from the Korean allowed list provided in the Output Rules . It must be matched with english category.
-*   **`description`**: A short, punchy marketing phrase in Korean (15~30 characters).
-*   **`score`**: An integer (0-100) representing the strength of this feature based on the brand's potential.
-
-### 5. target_keywords
-*   **`target_keywords`**: Provide a list of 10 highly relevant marketing keywords or hashtags for search engine optimization and social media targeting. Do not insert # in front of hashtag. 

app/utils/prompts/templates/subtitle_prompt.txt

@@ -1,75 +1,224 @@
-당신은 숙박 브랜드 숏폼 영상의 자막 콘텐츠를 추출하는 전문가입니다.
+# System Prompt: 숙박 숏폼 자막 생성 (OpenAI Optimized)
 
-입력으로 주어지는 **1) 5가지 기준의 레이어 이름 리스트**와 **2) 마케팅 인텔리전스 분석 결과(JSON)**를 바탕으로, 각 레이어 이름의 의미에 정확히 1:1 매칭되는 텍스트 콘텐츠만을 추출하세요.
+You are a subtitle copywriter for hospitality short-form videos. You generate subtitle text AND layer names from marketing JSON data.
-
-분석 결과에 없는 정보는 절대 지어내거나 추론하지 마세요. 오직 제공된 JSON 데이터 내에서만 텍스트를 구성해야 합니다.
 
 ---
 
-## 1. 레이어 네이밍 규칙 해석 및 매핑 가이드
+### RULES
 
-입력되는 모든 레이어 이름은 예외 없이 `<track_role>-<narrative_phase>-<content_type>-<tone>-<pair_id>` 의 5단계 구조로 되어 있습니다. 
+1. NEVER copy JSON verbatim. ALWAYS rewrite into video-optimized copy.
-마지막의 3자리 숫자 ID(`-001`, `-002` 등)는 모든 레이어에 필수적으로 부여됩니다.
+2. NEVER invent facts not in the data. You MAY freely transform expressions.
-
+3. Each scene = 1 subtitle + 1 keyword (a "Pair"). Same pair_id for both.
-### [1] track_role (텍스트 형태)
-- `subtitle`: 씬 상황을 설명하는 간결한 문장형 텍스트 (1줄 이내)
-- `keyword`: 씬을 상징하고 시선을 끄는 단답형/명사형 텍스트 (1~2단어)
-
-### [2] narrative_phase (영상 흐름)
-- `intro`: 영상 도입부. 가장 시선을 끄는 정보를 배치.
-- `core`: 핵심 매력이나 주요 편의 시설 어필.
-- `highlight`: 세부적인 매력 포인트나 공간의 특별한 분위기 묘사.
-- `outro`: 영상 마무리. 브랜드 명칭 복기 및 타겟/위치 정보 제공.
-
-### [3] content_type (데이터 매핑 대상)
-- `hook_claim` 👉 `selling_points`에서 점수가 가장 높은 1순위 소구점이나 `market_positioning.core_value`를 활용하여 가장 강력한 핵심 세일즈 포인트를 어필. (가장 강력한 셀링포인트를 의미함)
-- `selling_point` 👉 `selling_points`의 `description`, `korean_category` 등을 narrative 흐름에 맞춰 순차적으로 추출.
-- `brand_name` 👉 JSON의 `store_name`을 추출.
-- `location_info` 👉 JSON의 `detail_region_info`를 요약.
-- `target_tag` 👉 `target_persona`나 `target_keywords`에서 타겟 고객군 또는 해시태그 추출.
-
-### [4] tone (텍스트 어조)
-- `sensory`: 직관적이고 감각적인 단어 사용
-- `factual`: 과장 없이 사실 정보를 담백하게 전달
-- `empathic`: 고객의 상황에 공감하는 따뜻한 어조
-- `aspirational`: 열망을 자극하고 기대감을 주는 느낌
-
-### [5] pair_id (씬 묶음 식별 번호)
-- 텍스트 레이어는 `subtitle`과 `keyword`가 하나의 페어(Pair)를 이뤄 하나의 씬(Scene)에서 함께 등장합니다.
-- 따라서 **동일한 씬에 속하는 `subtitle`과 `keyword` 레이어는 동일한 3자리 순번 ID(예: `-001`)**를 공유합니다.
-- 영상 전반적인 씬 전개 순서에 따라 **다음 씬으로 넘어갈 때마다 ID가 순차적으로 증가**합니다. (예: 씬1은 `-001`, 씬2는 `-002`, 씬3은 `-003`...)
-- **중요**: ID가 달라진다는 것은 '새로운 씬' 혹은 '다른 텍스트 쌍'을 의미하므로, **ID가 바뀌면 반드시 JSON 내의 다른 소구점이나 데이터를 추출**하여 내용이 중복되지 않도록 해야 합니다.
 
 ---
 
-## 2. 콘텐츠 추출 시 주의사항
+### LAYER NAME FORMAT (5-criteria)
 
-1. 각 입력 레이어 이름 1개당 **오직 1개의 텍스트 콘텐츠**만 매핑하여 출력합니다. (레이어명 이름 자체를 수정하거나 새로 만들지 마세요.)
+```
-2. `content_type`이 `selling_point`로 동일하더라도, `narrative_phase`(core, highlight)나 `tone`이 달라지면 JSON 내의 2순위, 3순위 세일즈 포인트를 순차적으로 활용하여 내용 겹침을 방지하세요.
+(track_role)-(narrative_phase)-(content_type)-(tone)-(pair_id)
-3. 같은 씬에 속하는(같은 ID 번호를 가진) keyword는 핵심 단어로, subtitle은 적절한 마케팅 문구가 되어야 하며, 자연스럽게 이어지는 문맥을 형성하도록 구성하세요.
+```
-4. keyword가 subtitle에 완전히 포함되는 단어가 되지 않도록 유의하세요.
+
-5. 정보 태그가 같더라도 ID가 다르다면 중복되지 않는 새로운 텍스트를 도출해야 합니다.
+- Criteria separator: hyphen `-`
-6. 콘텐츠 추출 시 마케팅 인텔리전스의 내용을 그대로 사용하기보다는 paraphrase을 수행하세요.
+- Multi-word value: underscore `_`
-7. keyword는 공백 포함 전각 8자 / 반각 16자내, subtitle은 전각 15자 / 반각 30자 내로 구성하세요. 
+- pair_id: 3-digit zero-padded (`001`~`999`)
+
+Example: `subtitle-intro-hook_claim-aspirational-001`
 
 ---
 
-## 3. 출력 결과 포맷 및 예시
+### TAG VALUES
 
-입력된 레이어 이름 순서에 맞춰, 매핑된 텍스트 콘텐츠만 작성하세요. (반드시 intro, core, highlight, outro 등 모든 씬 단계가 명확하게 매핑되어야 합니다.)
+**track_role**: `subtitle` | `keyword`
 
-### 입력 레이어 리스트 예시 및 출력 예시
+**narrative_phase** (= emotion goal):
+- `intro` → Curiosity (stop the scroll)
+- `welcome` → Warmth
+- `core` → Trust
+- `highlight` → Desire (peak moment)
+- `support` → Discovery
+- `accent` → Belonging
+- `cta` → Action
 
-| Layer Name | Text Content |
+**content_type** → source mapping:
+- `hook_claim` ← selling_points[0] or core_value
+- `space_feature` ← selling_points[].description
+- `emotion_cue` ← same source, sensory rewrite
+- `brand_name` ← store_name (verbatim OK)
+- `brand_address` ← detail_region_info (verbatim OK)
+- `lifestyle_fit` ← target_persona[].favor_target
+- `local_info` ← location_feature_analysis
+- `target_tag` ← target_keywords[] as hashtags
+- `availability` ← fixed: "지금 예약 가능"
+- `cta_action` ← fixed: "예약하러 가기"
+
+**tone**: `sensory` | `factual` | `empathic` | `aspirational` | `social_proof` | `urgent`
+
+---
+
+### SCENE STRUCTURE
+
+**Anchors (FIXED — never remove):**
+
+| Position | Phase | subtitle | keyword |
+|---|---|---|---|
+| First | intro | hook_claim | brand_name |
+| Last-3 | support | brand_address | brand_name |
+| Last-2 | accent | target_tag | lifestyle_fit |
+| Last | cta | availability | cta_action |
+
+**Middle (FLEXIBLE — fill by selling_points score desc):**
+
+| Phase | subtitle | keyword |
+|---|---|---|
+| welcome | emotion_cue | space_feature |
+| core | space_feature | emotion_cue |
+| highlight | space_feature | emotion_cue |
+| support(mid) | local_info | lifestyle_fit |
+
+Default: 7 scenes. Fewer scenes → remove flexible slots only.
+
+---
+
+### TEXT SPECS
+
+**subtitle**: 8~18 chars. Sentence fragment, conversational.
+**keyword**: 2~6 chars. MUST follow Korean word-formation rules below.
+
+---
+
+### KEYWORD RULES (한국어 조어법 기반)
+
+Keywords MUST follow one of these **permitted Korean patterns**. Any keyword that does not match a pattern below is INVALID.
+
+#### Pattern 1: 관형형 + 명사 (Attributive + Noun) — 가장 자연스러운 패턴
+한국어는 수식어가 앞, 피수식어가 뒤. 형용사의 관형형(~ㄴ/~한/~는/~운)을 명사 앞에 붙인다.
+
+| Structure | GOOD | BAD (역순/비문) |
+|---|---|---|
+| 형용사 관형형 + 명사 | 고요한 숲, 깊은 쉼, 온전한 쉼 | ~~숲고요~~, ~~쉼깊은~~ |
+| 형용사 관형형 + 명사 | 따뜻한 독채, 느린 하루 | ~~독채따뜻~~, ~~하루느린~~ |
+| 동사 관형형 + 명사 | 쉬어가는 숲, 머무는 시간 | ~~숲쉬어가는~~ |
+
+#### Pattern 2: 기존 대중화 합성어 ONLY (Established Trending Compound)
+이미 SNS·미디어에서 대중화된 합성어만 허용. 임의 신조어 생성 금지.
+
+| GOOD (대중화 확인됨) | Origin | BAD (임의 생성) |
+|---|---|---|
+| 숲멍 | 숲+멍때리기 (불멍, 물멍 시리즈) | ~~숲고요~~, ~~숲힐~~ |
+| 댕캉스 | 댕댕이+바캉스 (여행업계 통용) | ~~댕쉼~~, ~~댕여행~~ |
+| 꿀잠 / 꿀쉼 | 꿀+잠/쉼 (일상어 정착) | ~~꿀독채~~, ~~꿀숲~~ |
+| 집콕 / 숲콕 | 집+콕 → 숲+콕 (변형 허용) | ~~계곡콕~~ |
+| 주말러 | 주말+~러 (~러 접미사 정착) | ~~평일러~~ |
+
+> **판별 기준**: "이 단어를 네이버/인스타에서 검색하면 결과가 나오는가?" YES → 허용, NO → 금지
+
+#### Pattern 3: 명사 + 명사 (Natural Compound Noun)
+한국어 복합명사 규칙을 따르는 결합만 허용. 앞 명사가 뒷 명사를 수식하는 관계여야 한다.
+
+| Structure | GOOD | BAD (부자연스러운 결합) |
+|---|---|---|
+| 장소 + 유형 | 숲속독채, 계곡펜션 | ~~햇살독채~~ (햇살은 장소가 아님) |
+| 대상 + 활동 | 반려견산책, 가족피크닉 | ~~견주피크닉~~ (견주가 피크닉하는 건 어색) |
+| 시간 + 활동 | 주말탈출, 새벽산책 | ~~자연독채~~ (자연은 시간/방식이 아님) |
+
+#### Pattern 4: 해시태그형 (#키워드)
+accent(target_tag) 씬에서만 사용. 기존 검색 키워드를 # 붙여서 사용.
+
+| GOOD | BAD |
 |---|---|
-| subtitle-intro-hook_claim-aspirational-001 | 반려견과 눈치 없이 온전하게 쉬는 완벽한 휴식 |
+| #프라이빗독채, #홍천여행 | #숲고요, #감성쩌는 (검색량 없음) |
-| keyword-intro-brand_name-sensory-001 | 스테이펫 홍천 |
+
-| subtitle-core-selling_point-empathic-002 | 우리만의 독립된 공간감이 주는 진정한 쉼 |
+#### Pattern 5: 감각/상태 명사 (단독 사용 가능한 것만)
-| keyword-core-selling_point-factual-002 | 프라이빗 독채 |
+그 자체로 의미가 완결되는 감각·상태 명사만 단독 사용 허용.
-| subtitle-highlight-selling_point-sensory-003 | 탁 트인 야외 무드존과 포토 스팟의 감성 컷 |
+
-| keyword-highlight-selling_point-factual-003 | 넓은 정원 |
+| GOOD (단독 의미 완결) | BAD (단독으로 의미 불완전) |
-| subtitle-outro-target_tag-empathic-004 | #강원도애견동반 #주말숏브레이크 |
+|---|---|
-| keyword-outro-location_info-factual-004 | 강원 홍천군 화촌면 |
+| 고요, 여유, 쉼, 온기 | ~~감성~~, ~~자연~~, ~~힐링~~ (너무 모호) |
+| 숲멍, 꿀쉼 | ~~좋은쉼~~, ~~편안함~~ (형용사 포함 시 Pattern 1 사용) |
+
+---
+
+### KEYWORD VALIDATION CHECKLIST (생성 후 자가 검증)
+
+Every keyword MUST pass ALL of these:
+
+- [ ] 한국어 어순이 자연스러운가? (수식어→피수식어 순서)
+- [ ] 소리 내어 읽었을 때 어색하지 않은가?
+- [ ] 네이버/인스타에서 검색하면 실제 결과가 나올 법한 표현인가?
+- [ ] 허용된 5개 Pattern 중 하나에 해당하는가?
+- [ ] 이전 씬 keyword와 동일한 Pattern을 연속 사용하지 않았는가?
+- [ ] 금지 표현 사전에 해당하지 않는가?
+
+---
+
+### EXPRESSION DICTIONARY
+
+**SCAN BEFORE WRITING.** If JSON contains these → MUST replace:
+
+| Forbidden | → Use Instead |
+|---|---|
+| 눈치 없는/없이 | 눈치 안 보는 · 프라이빗한 · 온전한 · 마음 편히 |
+| 감성 쩌는/쩌이 | 감성 가득한 · 감성이 머무는 |
+| 가성비 | 합리적인 · 가치 있는 |
+| 힐링되는 | 회복되는 · 쉬어가는 · 숨 쉬는 |
+| 인스타감성 | 감성 스팟 · 기록하고 싶은 |
+| 혜자 | 풍성한 · 넉넉한 |
+
+**ALWAYS FORBIDDEN**: 저렴한, 싼, 그냥, 보통, 무난한, 평범한, 쩌는, 쩔어, 개(접두사), 존맛, 핵, 인스타, 유튜브, 틱톡
+
+**SYNONYM ROTATION**: Same Korean word max 2 scenes. Rotate:
+- 프라이빗 계열: 온전한 · 오롯한 · 나만의 · 독채 · 단독
+- 자연 계열: 숲속 · 초록 · 산림 · 계곡
+- 쉼 계열: 쉼 · 여유 · 느린 하루 · 머무름 · 숨고르기
+- 반려견: 댕댕이(max 1회, intro/accent만) · 반려견 · 우리 강아지
+
+---
+
+### TRANSFORM RULES BY CONTENT_TYPE
+
+**hook_claim** (intro only):
+- Format: question OR exclamation OR provocation. Pick ONE.
+- FORBIDDEN: brand name, generic greetings
+- `"반려견과 눈치 없는 힐링"` → BAD: 그대로 복사 → GOOD: "댕댕이가 먼저 뛰어간 숲"
+
+**space_feature** (core/highlight):
+- ONE selling point per scene
+- NEVER use korean_category directly
+- Viewer must imagine themselves there
+- `"홍천 자연 속 조용한 쉼"` → BAD: "입지 환경이 좋은 곳" → GOOD: "계곡 소리만 들리는 독채"
+
+**emotion_cue** (welcome/core/highlight):
+- Senses: smell, sound, touch, temperature, light
+- Poetic fragments, not full sentences
+- `"감성 쩌이 완성되는 공간"` → GOOD: "햇살이 내려앉는 테라스"
+
+**lifestyle_fit** (accent/support):
+- Address target directly in their language
+- `persona: "서울·경기 주말러"` → GOOD: "이번 주말, 댕댕이랑 어디 가지?"
+
+**local_info** (support):
+- Accessibility or charm, NOT administrative address
+- GOOD: "서울에서 1시간 반, 홍천 숲속" / BAD: "강원 홍천군 화촌면"
+
+---
+
+### PACING
+
+```
+intro(8~12) → welcome(12~18) → core(alternate 8~12 ↔ 12~18) → highlight(8~14) → support(12~18) → accent(variable) → cta(12~16)
+```
+
+**RULE: No 3+ consecutive scenes in same char-count range.**
+
+---
+
+Keyword pattern analysis:
+- "스테이펫" → brand_name verbatim (허용)
+- "고요한 숲" → Pattern 1: 관형형+명사 (형용사 관형형 "고요한" + 명사 "숲")
+- "깊은 쉼" → Pattern 1: 관형형+명사 (형용사 관형형 "깊은" + 명사 "쉼")
+- "숲멍" → Pattern 2: 기존 대중화 합성어 (불멍·물멍·숲멍 시리즈)
+- "댕캉스" → Pattern 2: 기존 대중화 합성어 (댕댕이+바캉스, 여행업계 통용)
+- "예약하기" → Pattern 5: 의미 완결 동사 명사형
 
 
 # 입력
@@ -82,6 +231,3 @@
 **입력 3: 비즈니스 정보 **
 Business Name: {customer_name}
 Region Details: {detail_region_info}
-
-
-

app/utils/prompts/templates/yt_upload_prompt.txt

@@ -1,143 +0,0 @@
-[ROLE]
-You are a YouTube SEO/AEO content strategist specialized in local stay, pension, and accommodation brands in Korea.
-You create search-optimized, emotionally appealing, and action-driving titles and descriptions based on Brand & Marketing Intelligence.
-
-Your goal is to:
-
-Increase search visibility
-Improve click-through rate
-Reflect the brand’s positioning
-Trigger emotional interest
-Encourage booking or inquiry actions through subtle CTA
- 
-
-[INPUT]
-Business Name: {customer_name}
-Region Details: {detail_region_info}
-Brand & Marketing Intelligence Report: {marketing_intelligence_summary}
-Target Keywords: {target_keywords}
-Output Language: {language}
-
- 
-
-[INTERNAL ANALYSIS – DO NOT OUTPUT]
-Analyze the following from the marketing intelligence:
-
-Core brand concept
-Main emotional promise
-Primary target persona
-Top 2–3 USP signals
-Stay context (date, healing, local trip, etc.)
-Search intent behind the target keywords
-Main booking trigger
-Emotional moment that would make the viewer want to stay
-Use these to guide:
-
-Title tone
-Opening CTA line
-Emotional hook in the first sentences
- 
-
-[TITLE GENERATION RULES]
-
-The title must:
-
-Include the business name or region when natural
-Always wrap the business name in quotation marks
-Example: “스테이 머뭄”
-Include 1–2 high-intent keywords
-Reflect emotional positioning
-Suggest a desirable stay moment
-Sound like a natural YouTube title, not an advertisement
-Length rules:
-
-Hard limit: 100 characters
-Target range: 45–65 characters
-Place primary keyword in the first half
-Avoid:
-
-ALL CAPS
-Excessive symbols
-Price or promotion language
-Hard-sell expressions
- 
-
-[DESCRIPTION GENERATION RULES]
-
-Character rules:
-
-Maximum length: 1,000 characters
-Critical information must appear within the first 150 characters
-Language style rules (mandatory):
-
-Use polite Korean honorific style
-Replace “있나요?” with “있으신가요?”
-Do not start sentences with “이곳은”
-Replace “선택이 됩니다” with “추천 드립니다”
-Always wrap the business name in quotation marks
-Example: “스테이 머뭄”
-Avoid vague location words like “근대거리” alone
-Use specific phrasing such as:
-“군산 근대역사문화거리 일대”
-Structure:
-
-Opening CTA (first line)
-Must be a question or gentle suggestion
-Must use honorific tone
-Example:
-“조용히 쉴 수 있는 군산숙소를 찾고 있으신가요?”
-Core Stay Introduction (within first 150 characters total)
-Mention business name with quotation marks
-Mention region
-Include main keyword
-Briefly describe the stay experience
-Brand Experience
-Core value and emotional promise
-Based on marketing intelligence positioning
-Key Highlights (3–4 short lines)
-Derived from USP signals
-Natural sentences
-Focus on booking-trigger moments
-Local Context
-Mention nearby experiences
-Use specific local references
-Example:
-“군산 근대역사문화거리 일대 산책이나 로컬 카페 투어”
-Soft Closing Line
-One gentle, non-salesy closing sentence
-Must end with a recommendation tone
-Example:
-“군산에서 조용한 시간을 보내고 싶다면 ‘스테이 머뭄’을 추천 드립니다.”
- 
-
-[SEO & AEO RULES]
-
-Naturally integrate 3–5 keywords from {target_keywords}
-Avoid keyword stuffing
-Use conversational, search-like phrasing
-Optimize for:
-YouTube search
-Google video results
-AI answer summaries
-Keywords should appear in:
-
-Title (1–2)
-First 150 characters of description
-Highlight or context sections
- 
-
-[LANGUAGE RULE]
-
-All output must be written entirely in {language}.
-No mixed languages.
-
- 
-
-[OUTPUT FORMAT – STRICT]
-
-title:
-description:
-
-No explanations.
-No headings.
-No extra text.

app/utils/subtitles.py

@@ -6,7 +6,7 @@ from typing import Literal, Any
 import httpx
 
 from app.utils.logger import get_logger
-from app.utils.chatgpt_prompt import ChatgptService
+from app.utils.prompts.chatgpt_prompt import ChatgptService
 from app.utils.prompts.schemas import *
 from app.utils.prompts.prompts import *
 

app/video/api/routers/v1/video.py

@@ -25,7 +25,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
 from app.database.session import get_session
 from app.user.dependencies.auth import get_current_user
 from app.user.models import User
-from app.home.models import Image, Project, MarketingIntel
+from app.home.models import Image, Project, MarketingIntel, ImageTag
 from app.lyric.models import Lyric
 from app.song.models import Song, SongTimestamp
 from app.utils.creatomate import CreatomateService
@@ -39,6 +39,7 @@ from app.video.schemas.video_schema import (
     VideoRenderData,
 )
 from app.video.worker.video_task import download_and_upload_video_to_blob
+from app.video.services.video import get_image_tags_by_task_id
 
 from config import creatomate_settings
 
@@ -170,7 +171,7 @@ async def generate_video(
                 logger.info(f"[generate_video] Check subtitle done task_id: {task_id}")
                 break
             await asyncio.sleep(5)
-            if count > 12 :
+            if count > 60 :
                 raise Exception("subtitle 결과 생성 실패")
             count += 1
             
@@ -337,17 +338,30 @@ async def generate_video(
         )
 
         # 6-1. 템플릿 조회 (비동기)
-        template = await creatomate_service.get_one_template_data_async(
+        template = await creatomate_service.get_one_template_data(
             creatomate_service.template_id
         )
         logger.debug(f"[generate_video] Template fetched - task_id: {task_id}")
 
         # 6-2. elements에서 리소스 매핑 생성
-        modifications = creatomate_service.elements_connect_resource_blackbox(
+        # modifications = creatomate_service.elements_connect_resource_blackbox(
-            elements=template["source"]["elements"],
+        #     elements=template["source"]["elements"],
-            image_url_list=image_urls,
+        #     image_url_list=image_urls,
+        #     music_url=music_url,
+        #     address=store_address
+        taged_image_list = await get_image_tags_by_task_id(task_id)
+        min_image_num = creatomate_service.counting_component(
+            template = template,
+            target_template_type = "image"
+        )
+        duplicate = bool(len(taged_image_list) < min_image_num)
+        logger.info(f"[generate_video] Duplicate : {duplicate} | length of taged_image {len(taged_image_list)}, min_len {min_image_num},- task_id: {task_id}")
+        modifications = creatomate_service.template_matching_taged_image(
+            template = template,
+            taged_image_list = taged_image_list,
             music_url = music_url, 
-            address=store_address
+            address = store_address,
+            duplicate = duplicate,
         )
         logger.debug(f"[generate_video] Modifications created - task_id: {task_id}")
 
@@ -413,7 +427,7 @@ async def generate_video(
         #     f"[generate_video] final_template: {json.dumps(final_template, indent=2, ensure_ascii=False)}"
         # )
         # 6-5. 커스텀 렌더링 요청 (비동기)
-        render_response = await creatomate_service.make_creatomate_custom_call_async(
+        render_response = await creatomate_service.make_creatomate_custom_call(
             final_template["source"],
         )
         
@@ -565,7 +579,7 @@ async def get_video_status(
     )
     try:
         creatomate_service = CreatomateService()
-        result = await creatomate_service.get_render_status_async(creatomate_render_id)
+        result = await creatomate_service.get_render_status(creatomate_render_id)
         logger.debug(
             f"[get_video_status] Creatomate API response - creatomate_render_id: {creatomate_render_id}, status: {result.get('status')}"
         )

config.py

@@ -42,6 +42,7 @@ class ProjectSettings(BaseSettings):
 
 class APIKeySettings(BaseSettings):
     CHATGPT_API_KEY: str = Field(default="your-chatgpt-api-key")  # 기본값 추가
+    GEMINI_API_KEY: str = Field(default="your-gemeni-api-key")  # 기본값 추가
     SUNO_API_KEY: str = Field(default="your-suno-api-key")  # Suno API 키
     SUNO_CALLBACK_URL: str = Field(
         default="https://example.com/api/suno/callback"
@@ -180,19 +181,8 @@ class CreatomateSettings(BaseSettings):
     model_config = _base_config
 
 class PromptSettings(BaseSettings):
-    PROMPT_FOLDER_ROOT : str = Field(default="./app/utils/prompts/templates")
+    GOOGLE_SERVICE_ACCOUNT_JSON: str = Field(...)
-
+    PROMPT_SPREADSHEET: str = Field(...)
-    MARKETING_PROMPT_FILE_NAME : str = Field(default="marketing_prompt.txt")
-    MARKETING_PROMPT_MODEL : str = Field(default="gpt-5.2")
-
-    LYRIC_PROMPT_FILE_NAME : str = Field(default="lyric_prompt.txt")
-    LYRIC_PROMPT_MODEL : str = Field(default="gpt-5-mini")
-
-    YOUTUBE_PROMPT_FILE_NAME : str = Field(default="yt_upload_prompt.txt")
-    YOUTUBE_PROMPT_MODEL : str = Field(default="gpt-5-mini")
-    
-    SUBTITLE_PROMPT_FILE_NAME : str = Field(...)
-    SUBTITLE_PROMPT_MODEL : str = Field(...)
 
     model_config = _base_config
 
@@ -206,6 +196,14 @@ class RecoverySettings(BaseSettings):
     # ============================================================
     # ChatGPT API 설정
     # ============================================================
+    LLM_TIMEOUT: float = Field(
+        default=600.0,
+        description="LLM Default API 타임아웃 (초)",
+    )
+    LLM_MAX_RETRIES: int = Field(
+        default=1,
+        description="LLM API 응답 실패 시 최대 재시도 횟수",
+    )
     CHATGPT_TIMEOUT: float = Field(
         default=600.0,
         description="ChatGPT API 타임아웃 (초). OpenAI Python SDK 기본값: 600초 (10분)",
@@ -214,7 +212,6 @@ class RecoverySettings(BaseSettings):
         default=1,
         description="ChatGPT API 응답 실패 시 최대 재시도 횟수",
     )
-
     # ============================================================
     # Suno API 설정
     # ============================================================

pyproject.toml

@@ -12,6 +12,7 @@ dependencies = [
     "beautifulsoup4>=4.14.3",
     "fastapi-cli>=0.0.16",
     "fastapi[standard]>=0.125.0",
+    "gspread>=6.2.1",
     "openai>=2.13.0",
     "playwright>=1.57.0",
     "pydantic-settings>=2.12.0",

uv.lock

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 ]
 
+[[package]]
+name = "charset-normalizer"
+version = "3.4.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7b/60/e3bec1881450851b087e301bedc3daa9377a4d45f1c26aa90b0b235e38aa/charset_normalizer-3.4.6.tar.gz", hash = "sha256:1ae6b62897110aa7c79ea2f5dd38d1abca6db663687c0b1ad9aed6f6bae3d9d6", size = 143363, upload-time = "2026-03-15T18:53:25.478Z" }
+wheels = [
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+    { url = "https://files.pythonhosted.org/packages/47/7b/20e809b89c69d37be748d98e84dce6820bf663cf19cf6b942c951a3e8f41/charset_normalizer-3.4.6-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:423fb7e748a08f854a08a222b983f4df1912b1daedce51a72bd24fe8f26a1843", size = 198527, upload-time = "2026-03-15T18:51:17.177Z" },
+    { url = "https://files.pythonhosted.org/packages/37/a6/4f8d27527d59c039dce6f7622593cdcd3d70a8504d87d09eb11e9fdc6062/charset_normalizer-3.4.6-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:d73beaac5e90173ac3deb9928a74763a6d230f494e4bfb422c217a0ad8e629bf", size = 218388, upload-time = "2026-03-15T18:51:18.934Z" },
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+    { url = "https://files.pythonhosted.org/packages/01/a5/7abf15b4c0968e47020f9ca0935fb3274deb87cb288cd187cad92e8cdffd/charset_normalizer-3.4.6-cp313-cp313-win_arm64.whl", hash = "sha256:a4474d924a47185a06411e0064b803c68be044be2d60e50e8bddcc2649957c1f", size = 143109, upload-time = "2026-03-15T18:51:39.565Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/68/687187c7e26cb24ccbd88e5069f5ef00eba804d36dde11d99aad0838ab45/charset_normalizer-3.4.6-py3-none-any.whl", hash = "sha256:947cf925bc916d90adba35a64c82aace04fa39b46b52d4630ece166655905a69", size = 61455, upload-time = "2026-03-15T18:53:23.833Z" },
+]
+
 [[package]]
 name = "click"
 version = "8.3.1"
@@ -413,6 +438,32 @@ wheels = [
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 ]
 
+[[package]]
+name = "google-auth"
+version = "2.49.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cryptography" },
+    { name = "pyasn1-modules" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ea/80/6a696a07d3d3b0a92488933532f03dbefa4a24ab80fb231395b9a2a1be77/google_auth-2.49.1.tar.gz", hash = "sha256:16d40da1c3c5a0533f57d268fe72e0ebb0ae1cc3b567024122651c045d879b64", size = 333825, upload-time = "2026-03-12T19:30:58.135Z" }
+wheels = [
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+]
+
+[[package]]
+name = "google-auth-oauthlib"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "google-auth" },
+    { name = "requests-oauthlib" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a6/82/62482931dcbe5266a2680d0da17096f2aab983ecb320277d9556700ce00e/google_auth_oauthlib-1.3.1.tar.gz", hash = "sha256:14c22c7b3dd3d06dbe44264144409039465effdd1eef94f7ce3710e486cc4bfa", size = 21663, upload-time = "2026-03-30T22:49:56.408Z" }
+wheels = [
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+]
+
 [[package]]
 name = "greenlet"
 version = "3.3.0"
@@ -429,6 +480,19 @@ wheels = [
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 ]
 
+[[package]]
+name = "gspread"
+version = "6.2.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "google-auth" },
+    { name = "google-auth-oauthlib" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/91/83/42d1d813822ed016d77aabadc99b09de3b5bd68532fd6bae23fd62347c41/gspread-6.2.1.tar.gz", hash = "sha256:2c7c99f7c32ebea6ec0d36f2d5cbe8a2be5e8f2a48bde87ad1ea203eff32bd03", size = 82590, upload-time = "2025-05-14T15:56:25.254Z" }
+wheels = [
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+]
+
 [[package]]
 name = "h11"
 version = "0.16.0"
@@ -654,6 +718,7 @@ dependencies = [
     { name = "beautifulsoup4" },
     { name = "fastapi", extra = ["standard"] },
     { name = "fastapi-cli" },
+    { name = "gspread" },
     { name = "openai" },
     { name = "playwright" },
     { name = "pydantic-settings" },
@@ -683,6 +748,7 @@ requires-dist = [
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@@ -703,6 +769,15 @@ dev = [
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