SQL/db_create.sql

@@ -1,4 +1,85 @@
--- user_info
+-- 테이블 순서는 관계를 고려하여 한 번에 실행해도 에러가 발생하지 않게 정렬되었습니다.
+
+-- instagram_data Table Create SQL
+-- 테이블 생성 SQL - instagram_data
+CREATE TABLE instagram_data
+(
+    `id`           INT             NOT NULL    AUTO_INCREMENT,
+    `hospital_id`  CHAR(36)        NOT NULL,
+    `url`          VARCHAR(500)    NOT NULL,
+    `status`       VARCHAR(20)     NOT NULL    DEFAULT 'start',
+    `raw_data`     JSON            NULL,
+    `created_at`   TIMESTAMP       NOT NULL    DEFAULT CURRENT_TIMESTAMP,
+     PRIMARY KEY (id)
+);
+
+-- Index 설정 SQL - instagram_data(hospital_id)
+CREATE INDEX IX_instagram_data_1
+    ON instagram_data(hospital_id);
+
+
+-- facebook_data Table Create SQL
+-- 테이블 생성 SQL - facebook_data
+CREATE TABLE facebook_data
+(
+    `id`           INT             NOT NULL    AUTO_INCREMENT,
+    `hospital_id`  CHAR(36)        NOT NULL,
+    `url`          VARCHAR(500)    NOT NULL,
+    `status`       VARCHAR(20)     NOT NULL    DEFAULT 'start',
+    `raw_data`     JSON            NULL,
+    `created_at`   TIMESTAMP       NOT NULL    DEFAULT CURRENT_TIMESTAMP,
+     PRIMARY KEY (id)
+);
+
+-- Index 설정 SQL - facebook_data(hospital_id)
+CREATE INDEX IX_facebook_data_1
+    ON facebook_data(hospital_id);
+
+
+-- naver_blog_data Table Create SQL
+-- 테이블 생성 SQL - naver_blog_data
+CREATE TABLE naver_blog_data
+(
+    `id`           INT             NOT NULL    AUTO_INCREMENT,
+    `hospital_id`  CHAR(36)        NOT NULL,
+    `url`          VARCHAR(500)    NOT NULL,
+    `status`       VARCHAR(20)     NOT NULL    DEFAULT 'start',
+    `raw_data`     JSON            NULL,
+    `created_at`   TIMESTAMP       NOT NULL    DEFAULT CURRENT_TIMESTAMP,
+     PRIMARY KEY (id)
+);
+
+-- Index 설정 SQL - naver_blog_data(hospital_id)
+CREATE INDEX IX_naver_blog_data_1
+    ON naver_blog_data(hospital_id);
+
+
+-- hospital_baseinfo Table Create SQL
+-- 테이블 생성 SQL - hospital_baseinfo
+CREATE TABLE hospital_baseinfo
+(
+    `hospital_id`    CHAR(36)        NOT NULL,
+    `owner_user_id`  INT             NOT NULL,
+    `hospital_name`  VARCHAR(50)     NOT NULL,
+    `hospital_name_en` VARCHAR(50)   NULL,
+    `brn`            VARCHAR(50)     NOT NULL,
+    `road_address`   VARCHAR(100)    NULL,
+    `site_address`   VARCHAR(100)    NULL,
+    `url`            VARCHAR(500)    NULL,
+    `status`         VARCHAR(20)     NOT NULL    DEFAULT 'start',
+    `raw_data`       JSON            NULL,
+    `created_at`     TIMESTAMP       NOT NULL    DEFAULT CURRENT_TIMESTAMP,
+    `updated_at`     TIMESTAMP       NOT NULL    DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
+     PRIMARY KEY (hospital_id)
+);
+
+-- Index 설정 SQL - hospital_baseinfo(owner_user_id)
+CREATE INDEX IX_hospital_baseinfo_1
+    ON hospital_baseinfo(owner_user_id);
+
+
+-- user_info Table Create SQL
+-- 테이블 생성 SQL - user_info
 CREATE TABLE user_info
 (
     `user_id`     INT            NOT NULL    AUTO_INCREMENT, 
@@ -9,49 +90,52 @@ CREATE TABLE user_info
      PRIMARY KEY (user_id)
 );
 
-
+-- youtube_data Table Create SQL
--- hospital_baseinfo
+CREATE TABLE youtube_data
-CREATE TABLE hospital_baseinfo
 (
+    `id`           INT             NOT NULL    AUTO_INCREMENT,
     `hospital_id`  CHAR(36)        NOT NULL,
-    `owner_user_id`    INT          NOT NULL,
-    `hospital_name`    VARCHAR(50)  NOT NULL,
-    `hospital_name_en` VARCHAR(50)  NULL,
-    `brn`              VARCHAR(50)  NOT NULL,
-    `road_address`     VARCHAR(100) NULL,
-    `site_address`     VARCHAR(100) NULL,
-    `status`           VARCHAR(20)  NOT NULL    DEFAULT 'start',
-    `created_at`       TIMESTAMP    NOT NULL    DEFAULT CURRENT_TIMESTAMP,
-    `updated_at`       TIMESTAMP    NOT NULL    DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
-     PRIMARY KEY (hospital_id)
-);
-
-CREATE INDEX IX_hospital_baseinfo_1 ON hospital_baseinfo (owner_user_id);
-
-
--- remote_source: 병원별 채널 소스 정보 (instagram/facebook/naver_blog/youtube/gangnam_unni 등)
-CREATE TABLE remote_source
-(
-    `source_id`   INT          NOT NULL    AUTO_INCREMENT,
-    `hospital_id` CHAR(36)     NOT NULL,
-    `source_type` VARCHAR(50)  NOT NULL,
-    `language`    CHAR(2)      NULL,
     `url`          VARCHAR(500)    NOT NULL,
+    `status`       VARCHAR(20)     NOT NULL    DEFAULT 'start',
+    `raw_data`     JSON            NULL,
     `created_at`   TIMESTAMP       NOT NULL    DEFAULT CURRENT_TIMESTAMP,
-     PRIMARY KEY (source_id)
+     PRIMARY KEY (id)
 );
 
-CREATE INDEX IX_remote_source_1 ON remote_source (hospital_id);
+-- Index 설정 SQL - youtube_data(hospital_id)
-CREATE INDEX IX_remote_source_2 ON remote_source (hospital_id, source_type);
+CREATE INDEX IX_youtube_data_1
+    ON youtube_data(hospital_id);
 
 
--- analysis_runs
+-- gangnam_unni_data Table Create SQL
+CREATE TABLE gangnam_unni_data
+(
+    `id`           INT             NOT NULL    AUTO_INCREMENT,
+    `hospital_id`  CHAR(36)        NOT NULL,
+    `url`          VARCHAR(500)    NOT NULL,
+    `status`       VARCHAR(20)     NOT NULL    DEFAULT 'start',
+    `raw_data`     JSON            NULL,
+    `created_at`   TIMESTAMP       NOT NULL    DEFAULT CURRENT_TIMESTAMP,
+     PRIMARY KEY (id)
+);
+
+-- Index 설정 SQL - gangnam_unni_data(hospital_id)
+CREATE INDEX IX_gangnam_unni_data_1
+    ON gangnam_unni_data(hospital_id);
+
+
+-- analysis_runs Table Create SQL
 CREATE TABLE analysis_runs
 (
     `analysis_run_id`      CHAR(36)     NOT NULL,
     `hospital_id`          CHAR(36)     NOT NULL,
     `owner_user_id`        INT          NOT NULL    DEFAULT 0,
     `status`               VARCHAR(50)  NOT NULL    DEFAULT 'discovering',
+    `instagram_data_id`    INT          NULL,
+    `facebook_data_id`     INT          NULL,
+    `naver_blog_data_id`   INT          NULL,
+    `youtube_data_id`      INT          NULL,
+    `gangnam_unni_data_id` INT          NULL,
     `report_data`          JSON         NULL,
     `plan_data`            JSON         NULL,
     `created_at`           TIMESTAMP    NOT NULL    DEFAULT CURRENT_TIMESTAMP,
@@ -59,30 +143,16 @@ CREATE TABLE analysis_runs
      PRIMARY KEY (analysis_run_id)
 );
 
-CREATE INDEX IX_analysis_runs_1 ON analysis_runs (hospital_id);
+-- Index 설정 SQL - analysis_runs(hospital_id)
-CREATE INDEX IX_analysis_runs_2 ON analysis_runs (owner_user_id);
+CREATE INDEX IX_analysis_runs_1
+    ON analysis_runs(hospital_id);
+
+-- Index 설정 SQL - analysis_runs(owner_user_id)
+CREATE INDEX IX_analysis_runs_2
+    ON analysis_runs(owner_user_id);
 
 
--- raw_info: 분석 실행별 수집 원시 데이터
+-- file_data Table Create SQL
-CREATE TABLE raw_info
-(
-    `info_id`         INT          NOT NULL    AUTO_INCREMENT,
-    `source_id`       INT          NOT NULL,
-    `analysis_run_id` CHAR(36)     NOT NULL,
-    `data_tag`        VARCHAR(50)  NOT NULL    DEFAULT 'default',
-    `status`          VARCHAR(20)  NOT NULL    DEFAULT 'start',
-    `raw_data`        JSON         NULL,
-    `logo_url`        VARCHAR(500) NULL,
-    `created_at`      TIMESTAMP    NOT NULL    DEFAULT CURRENT_TIMESTAMP,
-    `updated_at`      TIMESTAMP    NOT NULL    DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
-     PRIMARY KEY (info_id)
-);
-
-CREATE INDEX IX_raw_info_1 ON raw_info (analysis_run_id);
-CREATE INDEX IX_raw_info_2 ON raw_info (source_id);
-
-
--- file_data
 CREATE TABLE file_data
 (
     `id`              INT             NOT NULL    AUTO_INCREMENT,
@@ -99,7 +169,7 @@ CREATE TABLE file_data
 );
 
 
--- hospital_history
+-- hospital_history Table Create SQL
 CREATE TABLE hospital_history
 (
     `id`               INT             NOT NULL    AUTO_INCREMENT,
@@ -110,17 +180,24 @@ CREATE TABLE hospital_history
     `brn`              VARCHAR(50)     NOT NULL,
     `road_address`     VARCHAR(100)    NULL,
     `site_address`     VARCHAR(100)    NULL,
+    `url`              VARCHAR(500)    NULL,
     `status`           VARCHAR(20)     NOT NULL,
+    `raw_data`         JSON            NULL,
     `analysis_run_id`  CHAR(36)        NULL,
     `created_at`       TIMESTAMP       NOT NULL    DEFAULT CURRENT_TIMESTAMP,
      PRIMARY KEY (id)
 );
 
-CREATE INDEX IX_hospital_history_1 ON hospital_history (hospital_id);
+-- Index 설정 SQL - hospital_history(hospital_id)
-CREATE INDEX IX_hospital_history_2 ON hospital_history (analysis_run_id);
+CREATE INDEX IX_hospital_history_1
+    ON hospital_history(hospital_id);
+
+-- Index 설정 SQL - hospital_history(analysis_run_id)
+CREATE INDEX IX_hospital_history_2
+    ON hospital_history(analysis_run_id);
 
 
--- market_analysis
+-- market_analysis Table Create SQL
 CREATE TABLE market_analysis
 (
     `id`               INT          NOT NULL    AUTO_INCREMENT,
@@ -133,4 +210,7 @@ CREATE TABLE market_analysis
      UNIQUE KEY UQ_market_analysis (analysis_run_id, analysis_type)
 );
 
-CREATE INDEX IX_market_analysis_1 ON market_analysis (analysis_run_id);
+-- Index 설정 SQL - market_analysis(analysis_run_id)
+CREATE INDEX IX_market_analysis_1
+    ON market_analysis(analysis_run_id);
+

app/api/analysis.py

@@ -2,23 +2,21 @@ import logging
 import uuid6
 from fastapi import APIRouter, BackgroundTasks, Depends, File, Form, HTTPException, UploadFile, status
 from common.deps import verify_api_key
-from common.db.hospital import select_hospital
+from common.db import fetchone, insert_instagram_row, insert_facebook_row, insert_naver_blog_row, insert_youtube_row, insert_gangnam_unni_row, insert_analysis_run
-from common.db.source import select_source_mainpage, insert_source, insert_raw_info
-from common.db.run import insert_run, select_run_status
-from common.utils import _normalize_homepage, _with_scheme
 from models.analysis import AnalysisCreate, AnalysisStartResponse, AnalysisStatusResponse
 from models.file import FileListItem, FileType, FileUploadResponse
-from models.status import AnalysisStatus, SourceType
+from models.status import AnalysisStatus
 from services.pipeline import run_pipeline
-from services.file_data import get_analysis_files_response, handle_analysis_file_upload, soft_delete_analysis_file
+from services.file import get_analysis_files_response, handle_analysis_file_upload, soft_delete_analysis_file
 from mock_urls import MOCK_CLINICS
+from common.utils import _normalize_homepage, _with_scheme
 
 router = APIRouter(prefix="/api/analysis", tags=["analysis"], dependencies=[Depends(verify_api_key)])
 logger = logging.getLogger(__name__)
 
-
+# 추후 DB에 클리닉별로 매핑할 채널(틱톡/영문 인스타·페북). 지금은 mock_urls에서 homepage 매칭으로 보충.
-# 클라가 일부만 보내거나 빈 값이면 mock_urls 의 동일 homepage 매칭으로 채워줌 (메인 + 부가 채널 동일 규칙).
+def _extra_channels_from_mockurls(homepage_url: str) -> dict:
-def _channels_from_mockurls(homepage_url: str) -> dict:
+    """homepage로 mock_urls에서 클리닉을 찾아 틱톡/영문 인스타·페북 URL 반환 (없으면 {})."""
     target = _normalize_homepage(homepage_url)
     if not target:
         return {}
@@ -26,18 +24,9 @@ def _channels_from_mockurls(homepage_url: str) -> dict:
         urls = c["urls"]
         if _normalize_homepage(urls.get("homepage", "")) == target:
             return {
-                # main
-                "instagram":    _with_scheme(urls.get("instagram")),
-                "facebook":     _with_scheme(urls.get("facebook")),
-                "naver_blog":   _with_scheme(urls.get("naverBlog")),
-                "youtube":      _with_scheme(urls.get("youtube")),
-                "gangnam_unni": _with_scheme(urls.get("gangnamUnni")),
-                # extra
                 "tiktok": _with_scheme(urls.get("tiktok")),
                 "instagram_en": _with_scheme(urls.get("instagramEn")),
                 "facebook_en": _with_scheme(urls.get("facebookEn")),
-                "kakao_talk":   _with_scheme(urls.get("kakaoTalk")),
-                "naver_cafe":   _with_scheme(urls.get("naverCafe")),
             }
     return {}
 
@@ -48,51 +37,34 @@ async def start_analysis(body: AnalysisCreate, background_tasks: BackgroundTasks
     analysis_run_id = str(uuid6.uuid7())
     hospital_id = body.clinic_id
 
-    # 사실 hospital 과 owner_user_id 비교 후 검증이 필요한 거지만 일단 PoC 니까. 나중에 바꿉니다.
+    # 사실 hospital과 owner_user_id 비교 후 검증이 필요한 거지만 일단 PoC 니까. 나중에 바꿉니다.
-    hospital = await select_hospital(hospital_id)
+    hospital = await fetchone(
+        "SELECT owner_user_id, url FROM hospital_baseinfo WHERE hospital_id = %s",
+        (hospital_id,),
+    )
     if not hospital:
         raise HTTPException(status_code=409, detail="Clinic not found")
 
-    analysis_run_id = await insert_run(analysis_run_id, hospital_id, hospital["owner_user_id"])
+    ig_id = await insert_instagram_row(hospital_id, body.channels.instagram) if body.channels.instagram else None
+    fb_id = await insert_facebook_row(hospital_id, body.channels.facebook) if body.channels.facebook else None
+    nb_id = await insert_naver_blog_row(hospital_id, body.channels.naver_blog) if body.channels.naver_blog else None
+    yt_id = await insert_youtube_row(hospital_id, body.channels.youtube) if body.channels.youtube else None
+    gu_id = await insert_gangnam_unni_row(hospital_id, body.channels.gangnam_unni) if body.channels.gangnam_unni else None
 
-    mainpage = await select_source_mainpage(hospital_id)
+    analysis_run_id = await insert_analysis_run(
-    if mainpage:
+        analysis_run_id, hospital_id, hospital["owner_user_id"],
-        await insert_raw_info(mainpage["source_id"], analysis_run_id, data_tag=SourceType.MAINPAGE)
+        ig_id, fb_id, nb_id, yt_id, gu_id,
-        # branding (HTML/CSS + Vision 로고 매칭) — mainpage 와 같은 homepage URL 을 source 로 사용.
+    )
-        branding_id = await insert_source(hospital_id, SourceType.BRANDING, mainpage["url"], language="KR")
-        await insert_raw_info(branding_id, analysis_run_id, data_tag=SourceType.BRANDING)
 
-    # 클라가 안 보낸 채널은 mock_urls 에서 homepage 매칭으로 보충 (main + extra 동일 규칙).
+    # 클라 값 우선, 없으면 보충 (추후 DB에서 클리닉별로 가져올 값)
-    mock = _channels_from_mockurls((mainpage or {}).get("url") or "")
+    mock_extra = _extra_channels_from_mockurls(hospital["url"])
-
+    extra_channels = {
-    # 메인 5채널 (KR). _with_scheme 으로 'gangnamunni.com/...' 같이 scheme/www 없이 와도 보강.
+        "tiktok":       body.channels.tiktok or mock_extra.get("tiktok"),
-    main_channels = [
+        "instagram_en": body.channels.instagram_en or mock_extra.get("instagram_en"),
-        (SourceType.INSTAGRAM,    _with_scheme(body.channels.instagram)    or mock.get("instagram")),
+        "facebook_en":  body.channels.facebook_en or mock_extra.get("facebook_en"),
-        (SourceType.FACEBOOK,     _with_scheme(body.channels.facebook)     or mock.get("facebook")),
+    }
-        (SourceType.NAVER_BLOG,   _with_scheme(body.channels.naver_blog)   or mock.get("naver_blog")),
+    logger.info("[analysis] extra_channels=%s (mock_matched=%s)", extra_channels, bool(mock_extra))
-        (SourceType.YOUTUBE,      _with_scheme(body.channels.youtube)      or mock.get("youtube")),
+    background_tasks.add_task(run_pipeline, analysis_run_id, extra_channels)
-        (SourceType.GANGNAM_UNNI, _with_scheme(body.channels.gangnam_unni) or mock.get("gangnam_unni")),
-    ]
-    for source_type, url in main_channels:
-        if url:
-            source_id = await insert_source(hospital_id, source_type, url, language="KR")
-            await insert_raw_info(source_id, analysis_run_id, data_tag=source_type)
-
-    # 부가 채널 — instagram_en/facebook_en 은 동일 source_type 에 language='EN' 으로 구분, 나머지는 자체 source_type.
-    extra_channels = [
-        (SourceType.INSTAGRAM, "EN", _with_scheme(body.channels.instagram_en) or mock.get("instagram_en")),
-        (SourceType.FACEBOOK,  "EN", _with_scheme(body.channels.facebook_en)  or mock.get("facebook_en")),
-        (SourceType.TIKTOK,    "KR", _with_scheme(body.channels.tiktok)       or mock.get("tiktok")),
-        (SourceType.KAKAOTALK, "KR", _with_scheme(body.channels.kakao_talk)   or mock.get("kakao_talk")),
-        (SourceType.NAVER_CAFE, "KR", _with_scheme(body.channels.naver_cafe)  or mock.get("naver_cafe")),
-    ]
-    for source_type, language, url in extra_channels:
-        if url:
-            source_id = await insert_source(hospital_id, source_type, url, language=language)
-            await insert_raw_info(source_id, analysis_run_id, data_tag=source_type)
-
-    logger.info("[analysis] main+extra channels resolved (mock_matched=%s)", bool(mock))
-    background_tasks.add_task(run_pipeline, analysis_run_id)
 
     return AnalysisStartResponse(
         analysis_run_id=analysis_run_id,
@@ -129,12 +101,12 @@ async def delete_analysis_run_file(run_id: str, file_id: int) -> None:
 @router.get("/{run_id}/status", response_model=AnalysisStatusResponse)
 async def get_analysis_status(run_id: str):
     logger.info("GET /api/analysis/%s/status", run_id)
-    run_status = await select_run_status(run_id)
+    row = await fetchone("SELECT status FROM analysis_runs WHERE analysis_run_id = %s", (run_id,))
-    if run_status is None:
+    if not row:
         raise HTTPException(status_code=404, detail="Run not found")
     return AnalysisStatusResponse(
         analysis_run_id=run_id,
-        status=AnalysisStatus(run_status),
+        status=AnalysisStatus(row["status"]),
         progress=50.0,
         current_step="",
         channel_errors={},

app/api/clinics.py

@@ -2,8 +2,7 @@ import logging
 import uuid6
 from fastapi import APIRouter, Depends, HTTPException, status
 from common.deps import verify_api_key
-from common.db.hospital import select_hospital, insert_hospital
+from common.db import insert_hospital, fetchone
-from common.db.source import insert_source
 from common.utils import get_env
 from integrations.firecrawl import FirecrawlClient
 from models.clinic import ClinicCreate, ClinicCreateResponse, ClinicResponse, ClinicHistoryResponse, RunSummary
@@ -31,8 +30,9 @@ async def create_clinic(body: ClinicCreate):
         name=info["clinicName"],
         name_en=info.get("clinicNameEn"),
         road_address=info.get("address"),
+        url=body.url,
+        raw_data=info,
     )
-    await insert_source(hospital_id, "mainpage", body.url)
     return ClinicCreateResponse(
         id=hospital_id,
         url=body.url,
@@ -44,7 +44,11 @@ async def create_clinic(body: ClinicCreate):
 @router.get("/{hospital_id}", response_model=ClinicResponse)
 async def get_clinic(hospital_id: str):
     logger.info("GET /api/clinics/%s", hospital_id)
-    row = await select_hospital(hospital_id)
+    row = await fetchone(
+        "SELECT hospital_id, hospital_name, hospital_name_en, road_address, url, status, raw_data, created_at, updated_at"
+        " FROM hospital_baseinfo WHERE hospital_id = %s",
+        (hospital_id,),
+    )
     if not row:
         raise HTTPException(status_code=404, detail="Clinic not found")
     return ClinicResponse(**{**row, "created_at": str(row["created_at"]), "updated_at": str(row["updated_at"])})

app/api/plan.py

@@ -1,13 +1,10 @@
 import json
 import logging
 from fastapi import APIRouter, Depends, HTTPException, Response
-from common.db.run import select_run_with_clinic
+from common.db import fetchone
-from common.db.source import select_run_source_raw
 from common.deps import verify_api_key
-from common.utils import _with_scheme
 from integrations.llm.schemas.plan import PlanOutput
 from models.plan import PlanApiResponse
-from models.status import SourceType
 
 router = APIRouter(prefix="/api/plan", tags=["plan"], dependencies=[Depends(verify_api_key)])
 logger = logging.getLogger(__name__)
@@ -16,21 +13,24 @@ logger = logging.getLogger(__name__)
 @router.get("/{run_id}", response_model=PlanApiResponse, response_model_by_alias=True)
 async def get_plan(run_id: str):
     logger.info("GET /api/plan/%s", run_id)
-    row = await select_run_with_clinic(run_id)
+    row = await fetchone(
+        "SELECT ar.plan_data, ar.created_at, h.hospital_name, h.hospital_name_en, h.url"
+        " FROM analysis_runs ar"
+        " JOIN hospital_baseinfo h ON ar.hospital_id = h.hospital_id"
+        " WHERE ar.analysis_run_id = %s",
+        (run_id,),
+    )
     if row is None:
         raise HTTPException(status_code=404, detail="Run not found")
     if row["plan_data"] is None:
         return Response(status_code=204)
     data = json.loads(row["plan_data"]) if isinstance(row["plan_data"], str) else row["plan_data"]
     plan = PlanOutput(**data)
-    # 강남언니에서 긁어온 이름이 있으면 우선 (hospital_baseinfo 의 정식 이름보다 강남언니가 더 광고용 표기).
-    gu = await select_run_source_raw(run_id, SourceType.GANGNAM_UNNI) or {}
-    clinic_name = gu.get("name") or row["hospital_name"]
     return PlanApiResponse(
         id=run_id,
-        clinic_name=clinic_name,
+        clinic_name=row["hospital_name"],
         clinic_name_en=row["hospital_name_en"],
         created_at=str(row["created_at"]),
-        target_url=_with_scheme(row["target_url"]),
+        target_url=row["url"],
         **plan.model_dump(),
     )

app/api/report.py

@@ -1,9 +1,8 @@
 import json
 import logging
 from fastapi import APIRouter, Depends, HTTPException, Response
-from common.db.run import select_run_with_clinic
+from common.db import fetchone
 from common.deps import verify_api_key
-from common.utils import _with_scheme
 from integrations.llm.schemas.report import ReportOutput
 from models.report import MarketingReportResponse
 
@@ -14,7 +13,13 @@ logger = logging.getLogger(__name__)
 @router.get("/{run_id}", response_model=MarketingReportResponse, response_model_by_alias=True)
 async def get_report(run_id: str):
     logger.info("GET /api/report/%s", run_id)
-    row = await select_run_with_clinic(run_id)
+    row = await fetchone(
+        "SELECT ar.report_data, ar.created_at, h.hospital_name, h.hospital_name_en, h.url"
+        " FROM analysis_runs ar"
+        " JOIN hospital_baseinfo h ON ar.hospital_id = h.hospital_id"
+        " WHERE ar.analysis_run_id = %s",
+        (run_id,),
+    )
     if row is None:
         raise HTTPException(status_code=404, detail="Run not found")
     if row["report_data"] is None:
@@ -26,6 +31,6 @@ async def get_report(run_id: str):
         clinic_name=row["hospital_name"],
         clinic_name_en=row["hospital_name_en"],
         created_at=str(row["created_at"]),
-        target_url=_with_scheme(row["target_url"]),
+        target_url=row["url"],
         **llm_output.model_dump(exclude={"id", "created_at", "target_url"}),
     )

app/common/db.py

@@ -0,0 +1,287 @@
+import json
+import os
+import aiomysql
+from common.utils import get_env
+
+_pool: aiomysql.Pool | None = None
+
+
+async def get_pool() -> aiomysql.Pool:
+    global _pool
+    if _pool is None:
+        _pool = await aiomysql.create_pool(
+            host=get_env("MYSQL_HOST"),
+            port=int(os.getenv("MYSQL_PORT", "3306")),
+            user=get_env("MYSQL_USER"),
+            password=get_env("MYSQL_PASSWORD"),
+            db=get_env("MYSQL_DB"),
+            charset="utf8mb4",
+            minsize=0,
+            maxsize=30,
+            connect_timeout=10,
+        )
+    return _pool
+
+
+# 쓰기 (INSERT/UPDATE/DELETE)
+async def execute(sql: str, args: tuple = ()) -> int:
+    pool = await get_pool()
+    async with pool.acquire() as conn:
+        try:
+            async with conn.cursor() as cur:
+                await cur.execute(sql, args)
+                await conn.commit()
+                return cur.lastrowid
+        finally:
+            conn.close()
+
+
+# 읽기 (SELECT)
+async def fetchone(sql: str, args: tuple = ()) -> dict | None:
+    pool = await get_pool()
+    async with pool.acquire() as conn:
+        try:
+            async with conn.cursor(aiomysql.DictCursor) as cur:
+                await cur.execute(sql, args)
+                return await cur.fetchone()
+        finally:
+            conn.close()
+
+
+async def fetchall(sql: str, args: tuple = ()) -> list[dict]:
+    pool = await get_pool()
+    async with pool.acquire() as conn:
+        try:
+            async with conn.cursor(aiomysql.DictCursor) as cur:
+                await cur.execute(sql, args)
+                return await cur.fetchall()
+        finally:
+            conn.close()
+
+async def insert_instagram_row(hospital_id: str, url: str) -> int:
+    return await execute("INSERT INTO instagram_data (hospital_id, url) VALUES (%s, %s)", (hospital_id, url))
+
+
+async def insert_facebook_row(hospital_id: str, url: str) -> int:
+    return await execute("INSERT INTO facebook_data (hospital_id, url) VALUES (%s, %s)", (hospital_id, url))
+
+
+async def insert_naver_blog_row(hospital_id: str, url: str) -> int:
+    return await execute("INSERT INTO naver_blog_data (hospital_id, url) VALUES (%s, %s)", (hospital_id, url))
+
+
+async def insert_youtube_row(hospital_id: str, url: str) -> int:
+    return await execute("INSERT INTO youtube_data (hospital_id, url) VALUES (%s, %s)", (hospital_id, url))
+
+
+async def insert_gangnam_unni_row(hospital_id: str, url: str) -> int:
+    return await execute("INSERT INTO gangnam_unni_data (hospital_id, url) VALUES (%s, %s)", (hospital_id, url))
+
+
+async def insert_file_row(
+    analysis_run_id: str,
+    file_type: str,
+    file_name: str,
+    file_url: str,
+    size_bytes: int | None = None,
+    hospital_id: str | None = None,
+) -> int:
+    return await execute(
+        "INSERT INTO file_data (analysis_run_id, hospital_id, file_type, file_name, file_url, size_bytes)"
+        " VALUES (%s, %s, %s, %s, %s, %s)",
+        (analysis_run_id, hospital_id, file_type, file_name, file_url, size_bytes),
+    )
+
+
+async def insert_analysis_run(
+    analysis_run_id: str,
+    hospital_id: str,
+    owner_user_id: int,
+    instagram_data_id: int | None,
+    facebook_data_id: int | None,
+    naver_blog_data_id: int | None,
+    youtube_data_id: int | None,
+    gangnam_unni_data_id: int | None,
+) -> str:
+    await execute(
+        "INSERT INTO analysis_runs"
+        " (analysis_run_id, hospital_id, owner_user_id, instagram_data_id, facebook_data_id, naver_blog_data_id, youtube_data_id, gangnam_unni_data_id)"
+        " VALUES (%s, %s, %s, %s, %s, %s, %s, %s)",
+        (analysis_run_id, hospital_id, owner_user_id, instagram_data_id, facebook_data_id, naver_blog_data_id, youtube_data_id, gangnam_unni_data_id),
+    )
+    return analysis_run_id
+
+
+
+async def save_analysis_report(analysis_run_id: str, data: dict) -> None:
+    await execute(
+        "UPDATE analysis_runs SET report_data = %s WHERE analysis_run_id = %s",
+        (json.dumps(data, ensure_ascii=False), analysis_run_id),
+    )
+
+
+async def is_done(table: str, row_id: int | None) -> bool:
+    if row_id is None:
+        return True
+    r = await fetchone(f"SELECT status FROM {table} WHERE id = %s", (row_id,))
+    return r["status"] == "done"
+
+
+async def fetch_raw(table: str, row_id: int | None) -> dict | None:
+    if row_id is None:
+        return None
+    row = await fetchone(f"SELECT raw_data FROM {table} WHERE id = %s", (row_id,))
+    if not row or not row["raw_data"]:
+        return None
+    return json.loads(row["raw_data"]) if isinstance(row["raw_data"], str) else row["raw_data"]
+
+
+async def get_analysis_raw_data(analysis_run_id: str) -> dict:
+    run = await fetchone(
+        "SELECT instagram_data_id, facebook_data_id, naver_blog_data_id, youtube_data_id, gangnam_unni_data_id"
+        " FROM analysis_runs WHERE analysis_run_id = %s",
+        (analysis_run_id,),
+    )
+    return {
+        "instagram":    await fetch_raw("instagram_data",    run["instagram_data_id"]),
+        "facebook":     await fetch_raw("facebook_data",     run["facebook_data_id"]),
+        "naver_blog":   await fetch_raw("naver_blog_data",   run["naver_blog_data_id"]),
+        "youtube":      await fetch_raw("youtube_data",      run["youtube_data_id"]),
+        "gangnam_unni": await fetch_raw("gangnam_unni_data", run["gangnam_unni_data_id"]),
+    }
+
+
+async def set_instagram_status(row_id: int, status: str) -> None:
+    await execute("UPDATE instagram_data SET status = %s WHERE id = %s", (status, row_id))
+
+
+async def set_facebook_status(row_id: int, status: str) -> None:
+    await execute("UPDATE facebook_data SET status = %s WHERE id = %s", (status, row_id))
+
+
+async def set_naver_blog_status(row_id: int, status: str) -> None:
+    await execute("UPDATE naver_blog_data SET status = %s WHERE id = %s", (status, row_id))
+
+
+async def set_youtube_status(row_id: int, status: str) -> None:
+    await execute("UPDATE youtube_data SET status = %s WHERE id = %s", (status, row_id))
+
+
+async def set_gangnam_unni_status(row_id: int, status: str) -> None:
+    await execute("UPDATE gangnam_unni_data SET status = %s WHERE id = %s", (status, row_id))
+
+
+async def save_instagram_raw_data(row_id: int, data: dict) -> None:
+    await execute("UPDATE instagram_data SET raw_data = %s, status = 'done' WHERE id = %s", (json.dumps(data, ensure_ascii=False), row_id))
+
+
+async def save_facebook_raw_data(row_id: int, data: dict) -> None:
+    await execute("UPDATE facebook_data SET raw_data = %s, status = 'done' WHERE id = %s", (json.dumps(data, ensure_ascii=False), row_id))
+
+
+async def save_naver_blog_raw_data(row_id: int, data: dict) -> None:
+    await execute("UPDATE naver_blog_data SET raw_data = %s, status = 'done' WHERE id = %s", (json.dumps(data, ensure_ascii=False), row_id))
+
+
+async def save_youtube_raw_data(row_id: int, data: dict) -> None:
+    await execute("UPDATE youtube_data SET raw_data = %s, status = 'done' WHERE id = %s", (json.dumps(data, ensure_ascii=False), row_id))
+
+
+async def save_gangnam_unni_raw_data(row_id: int, data: dict) -> None:
+    await execute("UPDATE gangnam_unni_data SET raw_data = %s, status = 'done' WHERE id = %s", (json.dumps(data, ensure_ascii=False), row_id))
+
+
+async def _insert_hospital_history(hospital_id: str, analysis_run_id: str | None) -> None:
+    row = await fetchone(
+        "SELECT owner_user_id, hospital_name, hospital_name_en, brn, road_address, site_address, url, status, raw_data"
+        " FROM hospital_baseinfo WHERE hospital_id = %s",
+        (hospital_id,),
+    )
+    if not row:
+        return
+    await execute(
+        "INSERT INTO hospital_history"
+        " (hospital_id, owner_user_id, hospital_name, hospital_name_en, brn, road_address, site_address, url, status, raw_data, analysis_run_id)"
+        " VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)",
+        (
+            hospital_id,
+            row["owner_user_id"],
+            row["hospital_name"],
+            row["hospital_name_en"],
+            row["brn"],
+            row["road_address"],
+            row["site_address"],
+            row["url"],
+            row["status"],
+            row["raw_data"] if isinstance(row["raw_data"], str) else json.dumps(row["raw_data"], ensure_ascii=False) if row["raw_data"] else None,
+            analysis_run_id,
+        ),
+    )
+
+
+async def insert_hospital(
+    hospital_id: str,
+    name: str,
+    name_en: str | None = None,
+    road_address: str | None = None,
+    site_address: str | None = None,
+    url: str | None = None,
+    raw_data: dict | None = None,
+    owner_user_id: int = 0,
+    brn: str = "",
+) -> dict:
+    await execute(
+        "INSERT INTO hospital_baseinfo (hospital_id, hospital_name, hospital_name_en, road_address, site_address, url, raw_data, status, owner_user_id, brn)"
+        " VALUES (%s, %s, %s, %s, %s, %s, %s, 'done', %s, %s)",
+        (hospital_id, name, name_en, road_address, site_address, url,
+         json.dumps(raw_data, ensure_ascii=False) if raw_data else None,
+         owner_user_id, brn),
+    )
+    await _insert_hospital_history(hospital_id, analysis_run_id=None)
+    return await fetchone(
+        "SELECT created_at FROM hospital_baseinfo WHERE hospital_id = %s",
+        (hospital_id,),
+    )
+
+
+async def save_hospital_raw_data(hospital_id: str, data: dict, analysis_run_id: str | None = None) -> None:
+    await execute(
+        "UPDATE hospital_baseinfo"
+        " SET raw_data = %s, status = 'done',"
+        "     hospital_name = COALESCE(%s, hospital_name),"
+        "     hospital_name_en = COALESCE(%s, hospital_name_en),"
+        "     road_address = COALESCE(%s, road_address)"
+        " WHERE hospital_id = %s",
+        (
+            json.dumps(data, ensure_ascii=False),
+            data.get("clinicName"),
+            data.get("clinicNameEn"),
+            data.get("address"),
+            hospital_id,
+        ),
+    )
+    await _insert_hospital_history(hospital_id, analysis_run_id)
+
+
+async def merge_hospital_raw_data(hospital_id: str, patch: dict) -> None:
+    """hospital_baseinfo.raw_data를 읽어 patch를 top-level 병합 후 저장 (read-modify-write).
+    부가 수집 단계들이 순차로 raw_data에 키를 덧붙일 때 사용."""
+    row = await fetchone("SELECT raw_data FROM hospital_baseinfo WHERE hospital_id = %s", (hospital_id,))
+    raw = row["raw_data"] if row else None
+    raw_data = json.loads(raw) if isinstance(raw, str) else (raw or {})
+    raw_data.update(patch)
+    await execute(
+        "UPDATE hospital_baseinfo SET raw_data = %s WHERE hospital_id = %s",
+        (json.dumps(raw_data, ensure_ascii=False), hospital_id),
+    )
+
+
+async def get_market_analysis(analysis_run_id: str) -> dict:
+    rows = await fetchall(
+        "SELECT analysis_type, data FROM market_analysis WHERE analysis_run_id = %s AND status = 'done'",
+        (analysis_run_id,),
+    )
+    return {
+        row["analysis_type"]: json.loads(row["data"]) if isinstance(row["data"], str) else row["data"]
+        for row in rows
+    }

app/common/db/__init__.py

@@ -1,16 +0,0 @@
-from common.db.base import execute, fetchone, fetchall
-from common.db.hospital import select_hospital, update_hospital_status, insert_hospital, update_hospital
-from common.db.source import (
-    insert_source, select_source_mainpage, select_source_by_type,
-    insert_raw_info, update_raw_info_status, update_raw_info, update_raw_info_merge,
-    update_raw_info_logo_url, select_mainpage_logo_url, select_branding_info_id,
-    select_raw_info_data,
-    select_run_sources, select_run_raw_data, select_run_source_raw,
-    select_run_mainpage_url,
-)
-from common.db.run import (
-    insert_run, select_run, select_run_status, update_run_status,
-    update_run_report, update_run_plan, select_run_with_clinic, select_run_report_data,
-)
-from common.db.market import upsert_market_status, upsert_market_result, select_market
-from common.db.file_data import insert_file, select_run_files, select_file, delete_file

app/common/db/base.py

@@ -1,56 +0,0 @@
-import os
-import aiomysql
-from common.utils import get_env
-
-_pool: aiomysql.Pool | None = None
-
-
-async def get_pool() -> aiomysql.Pool:
-    global _pool
-    if _pool is None:
-        _pool = await aiomysql.create_pool(
-            host=get_env("MYSQL_HOST"),
-            port=int(os.getenv("MYSQL_PORT", "3306")),
-            user=get_env("MYSQL_USER"),
-            password=get_env("MYSQL_PASSWORD"),
-            db=get_env("MYSQL_DB"),
-            charset="utf8mb4",
-            minsize=0,
-            maxsize=30,
-            connect_timeout=10,
-        )
-    return _pool
-
-
-async def execute(sql: str, args: tuple = ()) -> int:
-    pool = await get_pool()
-    async with pool.acquire() as conn:
-        try:
-            async with conn.cursor() as cur:
-                await cur.execute(sql, args)
-                await conn.commit()
-                return cur.lastrowid
-        finally:
-            conn.close()
-
-
-async def fetchone(sql: str, args: tuple = ()) -> dict | None:
-    pool = await get_pool()
-    async with pool.acquire() as conn:
-        try:
-            async with conn.cursor(aiomysql.DictCursor) as cur:
-                await cur.execute(sql, args)
-                return await cur.fetchone()
-        finally:
-            conn.close()
-
-
-async def fetchall(sql: str, args: tuple = ()) -> list[dict]:
-    pool = await get_pool()
-    async with pool.acquire() as conn:
-        try:
-            async with conn.cursor(aiomysql.DictCursor) as cur:
-                await cur.execute(sql, args)
-                return await cur.fetchall()
-        finally:
-            conn.close()

app/common/db/file_data.py

@@ -1,39 +0,0 @@
-from common.db.base import execute, fetchone, fetchall
-
-
-async def insert_file(
-    analysis_run_id: str,
-    file_type: str,
-    file_name: str,
-    file_url: str,
-    size_bytes: int | None = None,
-    hospital_id: str | None = None,
-) -> int:
-    return await execute(
-        "INSERT INTO file_data (analysis_run_id, hospital_id, file_type, file_name, file_url, size_bytes)"
-        " VALUES (%s, %s, %s, %s, %s, %s)",
-        (analysis_run_id, hospital_id, file_type, file_name, file_url, size_bytes),
-    )
-
-
-async def select_run_files(analysis_run_id: str) -> list[dict]:
-    return await fetchall(
-        "SELECT id, file_type, file_name, file_url, size_bytes, created_at"
-        " FROM file_data WHERE analysis_run_id = %s AND is_deleted = FALSE"
-        " ORDER BY created_at DESC",
-        (analysis_run_id,),
-    )
-
-
-async def select_file(file_id: int, analysis_run_id: str) -> dict | None:
-    return await fetchone(
-        "SELECT id FROM file_data WHERE id = %s AND analysis_run_id = %s",
-        (file_id, analysis_run_id),
-    )
-
-
-async def delete_file(file_id: int) -> None:
-    await execute(
-        "UPDATE file_data SET is_deleted = TRUE WHERE id = %s AND is_deleted = FALSE",
-        (file_id,),
-    )

app/common/db/hospital.py

@@ -1,78 +0,0 @@
-from common.db.base import execute, fetchone
-
-
-async def select_hospital(hospital_id: str) -> dict | None:
-    return await fetchone(
-        "SELECT hospital_id, owner_user_id, hospital_name, hospital_name_en,"
-        " brn, road_address, site_address, status, created_at, updated_at"
-        " FROM hospital_baseinfo WHERE hospital_id = %s",
-        (hospital_id,),
-    )
-
-
-async def update_hospital_status(hospital_id: str, status: str) -> None:
-    await execute(
-        "UPDATE hospital_baseinfo SET status = %s WHERE hospital_id = %s",
-        (status, hospital_id),
-    )
-
-
-async def _insert_hospital_history(hospital_id: str, analysis_run_id: str | None) -> None:
-    row = await fetchone(
-        "SELECT owner_user_id, hospital_name, hospital_name_en, brn, road_address, site_address, status"
-        " FROM hospital_baseinfo WHERE hospital_id = %s",
-        (hospital_id,),
-    )
-    if not row:
-        return
-    await execute(
-        "INSERT INTO hospital_history"
-        " (hospital_id, owner_user_id, hospital_name, hospital_name_en, brn, road_address, site_address, status, analysis_run_id)"
-        " VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)",
-        (
-            hospital_id,
-            row["owner_user_id"],
-            row["hospital_name"],
-            row["hospital_name_en"],
-            row["brn"],
-            row["road_address"],
-            row["site_address"],
-            row["status"],
-            analysis_run_id,
-        ),
-    )
-
-
-async def insert_hospital(
-    hospital_id: str,
-    name: str,
-    name_en: str | None = None,
-    road_address: str | None = None,
-    site_address: str | None = None,
-    owner_user_id: int = 0,
-    brn: str = "",
-) -> dict:
-    await execute(
-        "INSERT INTO hospital_baseinfo"
-        " (hospital_id, hospital_name, hospital_name_en, road_address, site_address, status, owner_user_id, brn)"
-        " VALUES (%s, %s, %s, %s, %s, 'done', %s, %s)",
-        (hospital_id, name, name_en, road_address, site_address, owner_user_id, brn),
-    )
-    await _insert_hospital_history(hospital_id, analysis_run_id=None)
-    return await fetchone(
-        "SELECT created_at FROM hospital_baseinfo WHERE hospital_id = %s",
-        (hospital_id,),
-    )
-
-
-async def update_hospital(hospital_id: str, data: dict, analysis_run_id: str | None = None) -> None:
-    await execute(
-        "UPDATE hospital_baseinfo"
-        " SET status = 'done',"
-        "     hospital_name = COALESCE(%s, hospital_name),"
-        "     hospital_name_en = COALESCE(%s, hospital_name_en),"
-        "     road_address = COALESCE(%s, road_address)"
-        " WHERE hospital_id = %s",
-        (data.get("clinicName"), data.get("clinicNameEn"), data.get("address"), hospital_id),
-    )
-    await _insert_hospital_history(hospital_id, analysis_run_id)

app/common/db/market.py

@@ -1,31 +0,0 @@
-import json
-from common.db.base import execute, fetchall
-
-
-async def upsert_market_status(analysis_run_id: str, analysis_type: str, status: str) -> None:
-    await execute(
-        "INSERT INTO market_analysis (analysis_run_id, analysis_type, status)"
-        " VALUES (%s, %s, %s)"
-        " ON DUPLICATE KEY UPDATE status = VALUES(status)",
-        (analysis_run_id, analysis_type, status),
-    )
-
-
-async def upsert_market_result(analysis_run_id: str, analysis_type: str, data: dict) -> None:
-    await execute(
-        "INSERT INTO market_analysis (analysis_run_id, analysis_type, status, data)"
-        " VALUES (%s, %s, 'done', %s)"
-        " ON DUPLICATE KEY UPDATE status = 'done', data = VALUES(data)",
-        (analysis_run_id, analysis_type, json.dumps(data, ensure_ascii=False)),
-    )
-
-
-async def select_market(analysis_run_id: str) -> dict:
-    rows = await fetchall(
-        "SELECT analysis_type, data FROM market_analysis WHERE analysis_run_id = %s AND status = 'done'",
-        (analysis_run_id,),
-    )
-    return {
-        row["analysis_type"]: json.loads(row["data"]) if isinstance(row["data"], str) else row["data"]
-        for row in rows
-    }

app/common/db/run.py

@@ -1,76 +0,0 @@
-import json
-from common.db.base import execute, fetchone
-
-
-async def insert_run(
-    analysis_run_id: str,
-    hospital_id: str,
-    owner_user_id: int,
-) -> str:
-    await execute(
-        "INSERT INTO analysis_runs (analysis_run_id, hospital_id, owner_user_id) VALUES (%s, %s, %s)",
-        (analysis_run_id, hospital_id, owner_user_id),
-    )
-    return analysis_run_id
-
-
-async def select_run(analysis_run_id: str) -> dict | None:
-    return await fetchone(
-        "SELECT analysis_run_id, hospital_id, owner_user_id, status, created_at, updated_at"
-        " FROM analysis_runs WHERE analysis_run_id = %s",
-        (analysis_run_id,),
-    )
-
-
-async def select_run_report_data(analysis_run_id: str) -> dict | None:
-    """report 결과가 필요할 때만 호출. raw JSON 파싱해서 dict 반환."""
-    import json
-    row = await fetchone(
-        "SELECT report_data FROM analysis_runs WHERE analysis_run_id = %s",
-        (analysis_run_id,),
-    )
-    if not row or not row["report_data"]:
-        return None
-    return json.loads(row["report_data"]) if isinstance(row["report_data"], str) else row["report_data"]
-
-
-async def select_run_status(analysis_run_id: str) -> str | None:
-    row = await fetchone(
-        "SELECT status FROM analysis_runs WHERE analysis_run_id = %s",
-        (analysis_run_id,),
-    )
-    return row["status"] if row else None
-
-
-async def update_run_status(analysis_run_id: str, status: str) -> None:
-    await execute(
-        "UPDATE analysis_runs SET status = %s WHERE analysis_run_id = %s",
-        (status, analysis_run_id),
-    )
-
-
-async def update_run_report(analysis_run_id: str, data: dict) -> None:
-    await execute(
-        "UPDATE analysis_runs SET report_data = %s WHERE analysis_run_id = %s",
-        (json.dumps(data, ensure_ascii=False), analysis_run_id),
-    )
-
-
-async def update_run_plan(analysis_run_id: str, data: dict) -> None:
-    await execute(
-        "UPDATE analysis_runs SET plan_data = %s WHERE analysis_run_id = %s",
-        (json.dumps(data, ensure_ascii=False), analysis_run_id),
-    )
-
-
-async def select_run_with_clinic(analysis_run_id: str) -> dict | None:
-    return await fetchone(
-        "SELECT ar.report_data, ar.plan_data, ar.created_at,"
-        " h.hospital_name, h.hospital_name_en,"
-        " rs.url AS target_url"
-        " FROM analysis_runs ar"
-        " JOIN hospital_baseinfo h ON ar.hospital_id = h.hospital_id"
-        " LEFT JOIN remote_source rs ON rs.hospital_id = h.hospital_id AND rs.source_type = 'mainpage'"
-        " WHERE ar.analysis_run_id = %s",
-        (analysis_run_id,),
-    )

app/common/db/source.py

@@ -1,162 +0,0 @@
-import json
-from common.db.base import execute, fetchone, fetchall
-from models.status import SourceType
-
-
-async def insert_source(
-    hospital_id: str,
-    source_type: SourceType,
-    url: str,
-    language: str | None = None,
-) -> int:
-    return await execute(
-        "INSERT INTO remote_source (hospital_id, source_type, language, url) VALUES (%s, %s, %s, %s)",
-        (hospital_id, source_type, language, url),
-    )
-
-
-async def select_source_mainpage(hospital_id: str) -> dict | None:
-    return await fetchone(
-        "SELECT source_id, url FROM remote_source WHERE hospital_id = %s AND source_type = 'mainpage'",
-        (hospital_id,),
-    )
-
-
-async def insert_raw_info(
-    source_id: int,
-    analysis_run_id: str,
-    data_tag: SourceType,
-) -> int:
-    return await execute(
-        "INSERT INTO raw_info (source_id, analysis_run_id, data_tag) VALUES (%s, %s, %s)",
-        (source_id, analysis_run_id, data_tag),
-    )
-
-
-async def update_raw_info_status(info_id: int, status: str) -> None:
-    await execute("UPDATE raw_info SET status = %s WHERE info_id = %s", (status, info_id))
-
-
-async def update_raw_info(info_id: int, data: dict) -> None:
-    await execute(
-        "UPDATE raw_info SET raw_data = %s, status = 'done' WHERE info_id = %s",
-        (json.dumps(data, ensure_ascii=False), info_id),
-    )
-
-
-async def select_raw_info_data(info_id: int | None) -> dict | None:
-    if info_id is None:
-        return None
-    row = await fetchone("SELECT raw_data FROM raw_info WHERE info_id = %s", (info_id,))
-    if not row or not row["raw_data"]:
-        return None
-    return json.loads(row["raw_data"]) if isinstance(row["raw_data"], str) else row["raw_data"]
-
-
-async def select_run_sources(analysis_run_id: str) -> list[dict]:
-    return await fetchall(
-        "SELECT ri.info_id, rs.source_type, rs.url"
-        " FROM raw_info ri JOIN remote_source rs USING (source_id)"
-        " WHERE ri.analysis_run_id = %s",
-        (analysis_run_id,),
-    )
-
-
-async def select_run_raw_data(analysis_run_id: str) -> dict:
-    rows = await fetchall(
-        "SELECT rs.source_type, rs.language, ri.raw_data, ri.logo_url"
-        " FROM raw_info ri JOIN remote_source rs USING (source_id)"
-        " WHERE ri.analysis_run_id = %s",
-        (analysis_run_id,),
-    )
-    result: dict = {}
-    for row in rows:
-        raw = row["raw_data"]
-        key = row["source_type"]
-        if (row.get("language") or "").upper() == "EN":
-            key = f"{key}_en"
-        data = json.loads(raw) if isinstance(raw, str) else (raw or {})
-        if isinstance(data, dict) and row.get("logo_url"):
-            data["_logo_url"] = row["logo_url"]
-        result[key] = data
-    return result
-
-
-async def select_run_source_raw(
-    analysis_run_id: str, source_type: str, language: str | None = None,
-) -> dict | None:
-    sql = (
-        "SELECT ri.raw_data FROM raw_info ri JOIN remote_source rs USING (source_id)"
-        " WHERE ri.analysis_run_id = %s AND rs.source_type = %s"
-    )
-    args: tuple = (analysis_run_id, source_type)
-    if language:
-        sql += " AND rs.language = %s"
-        args = (*args, language)
-    sql += " LIMIT 1"
-    row = await fetchone(sql, args)
-    if not row or not row["raw_data"]:
-        return None
-    return json.loads(row["raw_data"]) if isinstance(row["raw_data"], str) else row["raw_data"]
-
-
-async def update_raw_info_logo_url(info_id: int, logo_url: str) -> None:
-    """raw_info.logo_url 컬럼에 로고 URL 저장 (JSON raw_data 와 분리해 컬럼 인덱스/조회 용이)."""
-    await execute(
-        "UPDATE raw_info SET logo_url = %s WHERE info_id = %s",
-        (logo_url, info_id),
-    )
-
-
-async def select_branding_info_id(analysis_run_id: str) -> int | None:
-    row = await fetchone(
-        "SELECT ri.info_id FROM raw_info ri JOIN remote_source rs USING (source_id)"
-        " WHERE ri.analysis_run_id = %s AND rs.source_type = 'branding' LIMIT 1",
-        (analysis_run_id,),
-    )
-    return (row or {}).get("info_id")
-
-
-async def select_mainpage_logo_url(analysis_run_id: str) -> str | None:
-    row = await fetchone(
-        "SELECT ri.logo_url FROM raw_info ri JOIN remote_source rs USING (source_id)"
-        " WHERE ri.analysis_run_id = %s AND rs.source_type = 'mainpage' LIMIT 1",
-        (analysis_run_id,),
-    )
-    return (row or {}).get("logo_url")
-
-
-async def update_raw_info_merge(info_id: int, patch: dict) -> None:
-    """raw_info.raw_data 를 read-modify-write 로 top-level 머지.
-    한 source 가 단계별로 (예: branding 의 brandAssets → channelLogos) 키를 덧붙일 때 사용."""
-    row = await fetchone("SELECT raw_data FROM raw_info WHERE info_id = %s", (info_id,))
-    if not row:
-        return
-    raw = row["raw_data"]
-    data = json.loads(raw) if isinstance(raw, str) else (raw or {})
-    data.update(patch)
-    await execute(
-        "UPDATE raw_info SET raw_data = %s, status = 'done' WHERE info_id = %s",
-        (json.dumps(data, ensure_ascii=False), info_id),
-    )
-
-
-async def select_source_by_type(
-    hospital_id: str, source_type: str, language: str | None = None,
-) -> dict | None:
-    sql = "SELECT source_id, url FROM remote_source WHERE hospital_id = %s AND source_type = %s"
-    args: tuple = (hospital_id, source_type)
-    if language:
-        sql += " AND language = %s"
-        args = (*args, language)
-    sql += " LIMIT 1"
-    return await fetchone(sql, args)
-
-
-async def select_run_mainpage_url(analysis_run_id: str) -> str:
-    row = await fetchone(
-        "SELECT rs.url FROM raw_info ri JOIN remote_source rs USING (source_id)"
-        " WHERE ri.analysis_run_id = %s AND rs.source_type = 'mainpage'",
-        (analysis_run_id,),
-    )
-    return (row or {}).get("url") or ""

app/common/utils.py

@@ -1,7 +1,6 @@
 import os
 import asyncio
 import logging
-from datetime import datetime, timezone
 from http import HTTPMethod
 import httpx
 
@@ -10,27 +9,6 @@ logger = logging.getLogger(__name__)
 REQUEST_TIMEOUT = 60
 
 
-def parse_ts(v) -> datetime | None:
-    """수집기마다 다른 timestamp 포맷을 통일된 datetime으로 변환.
-    파싱 실패 시 None.
-    """
-    # 숫자면 epoch (Unix timestamp) — apify가 가끔 epoch로 줌
-    if isinstance(v, (int, float)):
-        return datetime.fromtimestamp(v, tz=timezone.utc)
-    if isinstance(v, str):
-        # 1순위: ISO 8601 (대부분 apify/firecrawl 출력)
-        try:
-            return datetime.fromisoformat(v.replace("Z", "+00:00"))
-        except ValueError:
-            pass
-        # 2순위: RFC 2822 (네이버 블로그 RSS 등 — 표준 라이브러리 파서로)
-        try:
-            from email.utils import parsedate_to_datetime
-            return parsedate_to_datetime(v)
-        except (TypeError, ValueError):
-            return None
-    return None
-
 
 def get_env(key: str) -> str:
     v = os.environ.get(key, "")
@@ -83,27 +61,6 @@ def _normalize_homepage(url: str) -> str:
     return u.rstrip("/")
 
 
-# SSL 인증서가 www.* 에만 유효한 도메인 — bare 도메인이면 사용자 클릭 시 브라우저 SSL warning 뜸.
-_WWW_REQUIRED = ("gangnamunni.com", "facebook.com", "instagram.com", "toxnfill.com")
-
-
 def _with_scheme(u: str | None) -> str | None:
-    """scheme 없는 URL에 https:// 보정 (수집기/링크 표시용). 빈 값은 None.
+    """scheme 없는 URL에 https:// 보정 (수집기 파싱용). 빈 값은 None."""
-    + 중첩된 https://가 끼어있으면 마지막 URL만 추출 (LLM이 가끔 'https://www.X/https://Y' 같이 만듦).
+    return (u if "://" in u else "https://" + u) if u else None
-    + SSL 엄격 도메인(gangnamunni/facebook/instagram)은 www. 자동 보강."""
-    if not u:
-        return None
-    u = u.strip()
-    # 'https://www.facebook.com/https://facebook.com/X' 같은 중첩 → 마지막 'http(s)://' 부터 잘라 사용
-    last = max(u.rfind("https://"), u.rfind("http://"))
-    if last > 0:
-        u = u[last:]
-    if "://" not in u:
-        u = "https://" + u
-    # scheme 뒤가 www. 없이 SSL 엄격 도메인이면 www. 추가
-    for dom in _WWW_REQUIRED:
-        for scheme in ("https://", "http://"):
-            if u.startswith(scheme + dom):
-                u = scheme + "www." + u[len(scheme):]
-                break
-    return u

app/integrations/apify.py

@@ -9,13 +9,6 @@ APIFY_BASE = "https://api.apify.com/v2"
 IG_PROFILE_ACTOR = "coderx~instagram-profile-scraper-bio-posts"
 IG_HIGHLIGHTS_ACTOR = "igview-owner~instagram-highlights-scraper"
 
-# Facebook: pages + posts 두 actor 직접 호출.
-FB_PAGES_ACTOR = "apify~facebook-pages-scraper"
-FB_POSTS_ACTOR = "apify~facebook-posts-scraper"
-
-# TikTok
-TIKTOK_ACTOR = "clockworks~tiktok-scraper"
-
 
 def _ig_username(url: str) -> str:
     return urlparse(url).path.strip("/").split("/")[0] if "://" in url else url.lstrip("@")
@@ -26,7 +19,7 @@ class ApifyClient:
         self.token = token
         self.wait_for_finish = wait_for_finish
 
-    async def _run_actor(self, actor_id: str, input_data: dict, limit: int = 20) -> list[dict]:
+    async def _run_actor(self, actor_id: str, input_data: dict) -> list[dict]:
         resp = await http_request(
             HTTPMethod.POST,
             url=f"{APIFY_BASE}/acts/{actor_id}/runs",
@@ -42,7 +35,7 @@ class ApifyClient:
         items_resp = await http_request(
             HTTPMethod.GET,
             url=f"{APIFY_BASE}/datasets/{dataset_id}/items",
-            params={"token": self.token, "limit": limit},
+            params={"token": self.token, "limit": 20},
             label=f"apify-dataset-{dataset_id}",
         )
         if not items_resp or not items_resp.is_success:
@@ -68,13 +61,6 @@ class ApifyClient:
             return None
         if isinstance(highlights, Exception):
             highlights = []
-        # 프로필상 하이라이트가 있다고 하면(highlight_reel_count>0) 빈 결과일 때 최대 2회 재시도.
-        if not highlights and (profile.get("highlight_reel_count", 0) or profile.get("highlightReelCount", 0)) > 0:
-            for _ in range(2):
-                retry = await self.fetch_instagram_highlights(username)
-                if retry:
-                    highlights = retry
-                    break
         return {
             "username": profile["username"],
             "profileImage": profile.get("hdProfilePicUrl") or profile.get("profilePicUrl"),
@@ -130,52 +116,31 @@ class ApifyClient:
     #     }
 
     async def fetch_facebook_page(self, page_url: str) -> dict | None:
-        items = await self._run_actor(FB_PAGES_ACTOR, {"startUrls": [{"url": page_url}]})
+        items = await self._run_actor("apify~facebook-pages-scraper", {"startUrls": [{"url": page_url}]})
         return items[0] if items else None
 
-    async def fetch_facebook_posts(self, page_url: str, limit: int = 20) -> list[dict]:
-        return await self._run_actor(
-            FB_POSTS_ACTOR, {"startUrls": [{"url": page_url}], "resultsLimit": limit}, limit=limit,
-        )
-
     async def get_facebook_page(self, page_url: str) -> dict | None:
-        # pages·posts 두 task 병렬 호출 (posts 실패해도 page만 있으면 진행)
+        page = await self.fetch_facebook_page(page_url)
-        page, posts = await asyncio.gather(
+        if not page:
-            self.fetch_facebook_page(page_url),
-            self.fetch_facebook_posts(page_url),
-            return_exceptions=True,
-        )
-        if isinstance(page, Exception) or not page:
             return None
-        if isinstance(posts, Exception):
-            posts = []
         return {
             "pageName": page.get("title") or page.get("name"),
             "profileImage": page.get("profilePictureUrl") or page.get("profilePhoto") or page.get("profilePic"),
             "pageUrl": page.get("pageUrl", page_url),
             "followers": page.get("followers", 0),
-            "following": page.get("followings", 0),
+            "likes": page.get("likes", 0),
-            "reviews": page.get("ratingCount", 0),
             "categories": page.get("categories", []),
-            "website": page.get("website") or page.get("websites"),
+            "email": page.get("email"),
+            "phone": page.get("phone"),
+            "website": page.get("website"),
+            "address": page.get("address"),
             "intro": page.get("intro"),
-            "latestPosts": [
+            "rating": page.get("rating"),
-                {
-                    "text": (p.get("text") or "")[:160],
-                    "likes": p.get("likes", 0),
-                    "reactions": p.get("topReactionsCount", 0),
-                    "shares": p.get("shares", 0),
-                    "views": p.get("viewsCount") or 0,
-                    "isVideo": p.get("isVideo", False),
-                    "timestamp": p.get("time") or p.get("timestamp"),
-                }
-                for p in (posts or []) if isinstance(p, dict)
-            ],
         }
 
     async def fetch_tiktok_profile(self, url: str) -> list[dict]:
         user = urlparse(url).path.strip("/").lstrip("@").split("/")[0] if "://" in url else url.lstrip("@")
-        return await self._run_actor(TIKTOK_ACTOR, {
+        return await self._run_actor("clockworks~tiktok-scraper", {
             "profiles": [user],
             "resultsPerPage": 10,
             "profileScrapeSections": ["videos"],

app/integrations/color_extractor.py

@@ -0,0 +1,250 @@
+"""홈페이지 HTML/CSS에서 hex 색상 직접 추출 + 빈도 기반 brand palette 산출.
+
+Vision LLM에 의존하지 않고 페이지의 실제 CSS 값을 정규식으로 잡음.
+로고만 분석하는 Vision보다 사이트 전체 컬러 시스템 (primary/secondary/background/text)을 더 정확히 추출.
+"""
+import logging
+import re
+import ssl
+from collections import Counter
+from urllib.parse import urljoin, urlparse
+import httpx
+
+logger = logging.getLogger(__name__)
+
+
+def _make_ssl_context() -> ssl.SSLContext:
+    """오래된 한국 의료 사이트들이 SSL DH_KEY_TOO_SMALL / cipher 약함 등으로 차단되는 문제 우회.
+    보안 등급 1로 낮춤 + cert 검증 유지."""
+    ctx = ssl.create_default_context()
+    try:
+        ctx.set_ciphers("DEFAULT@SECLEVEL=1")
+    except ssl.SSLError:
+        pass
+    return ctx
+
+
+async def _fetch_html(url: str, timeout: float = 20.0) -> tuple[int, str]:
+    """SSL/검증 단계별 fallback으로 HTML 받기. 그랜드/톡스앤필 같은 oldsite 대응."""
+    headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36"}
+    # 1차: 표준 검증
+    try:
+        async with httpx.AsyncClient(timeout=timeout, follow_redirects=True, headers=headers) as c:
+            r = await c.get(url)
+            return r.status_code, r.text
+    except (httpx.ConnectError, httpx.ReadError, ssl.SSLError) as e:
+        logger.info("[fetch] %s standard SSL failed: %s — fallback to weak cipher", url, e)
+    # 2차: 약한 cipher 허용
+    try:
+        async with httpx.AsyncClient(timeout=timeout, follow_redirects=True, headers=headers, verify=_make_ssl_context()) as c:
+            r = await c.get(url)
+            return r.status_code, r.text
+    except (httpx.ConnectError, httpx.ReadError, ssl.SSLError) as e:
+        logger.info("[fetch] %s weak cipher failed: %s — fallback to verify=False", url, e)
+    # 3차: SSL 검증 끔 (host mismatch 등)
+    try:
+        async with httpx.AsyncClient(timeout=timeout, follow_redirects=True, headers=headers, verify=False) as c:
+            r = await c.get(url)
+            return r.status_code, r.text
+    except Exception as e:
+        logger.warning("[fetch] %s all fallbacks failed: %s", url, e)
+        return 0, ""
+
+LOGO_IMG_PATTERNS = [
+    # 1) <img class="...logo..." src="...">
+    re.compile(r'<img[^>]*\bclass=["\'][^"\']*\blogo\b[^"\']*["\'][^>]*\bsrc=["\']([^"\']+)["\']', re.IGNORECASE),
+    # 2) <img src="..." class="...logo...">
+    re.compile(r'<img[^>]*\bsrc=["\']([^"\']+)["\'][^>]*\bclass=["\'][^"\']*\blogo\b[^"\']*["\']', re.IGNORECASE),
+    # 3) <img id="...logo..." src="...">
+    re.compile(r'<img[^>]*\bid=["\'][^"\']*\blogo\b[^"\']*["\'][^>]*\bsrc=["\']([^"\']+)["\']', re.IGNORECASE),
+    # 4) <img alt="...logo..." src="...">
+    re.compile(r'<img[^>]*\balt=["\'][^"\']*\blogo\b[^"\']*["\'][^>]*\bsrc=["\']([^"\']+)["\']', re.IGNORECASE),
+    # 5) <a/h1 class="logo"><...nested...><img src="...">
+    re.compile(r'<(?:a|h[1-6]|div|span)[^>]*\b(?:class|id)=["\'][^"\']*\blogo\b[^"\']*["\'][^>]*>(?:[^<]|<(?!img))*<img[^>]*\bsrc=["\']([^"\']+)["\']', re.IGNORECASE | re.DOTALL),
+    # 6) inline background-image: <a/div class="logo" style="background-image: url(...)">
+    re.compile(r'<(?:a|div|span|h[1-6])[^>]*\b(?:class|id)=["\'][^"\']*\blogo\b[^"\']*["\'][^>]*\bstyle=["\'][^"\']*background(?:-image)?\s*:\s*url\(\s*["\']?([^"\')\s]+)', re.IGNORECASE),
+    # 7) inline background-image: <a/div style="background-image: url(...)" class="logo">  (속성 순서 반대)
+    re.compile(r'<(?:a|div|span|h[1-6])[^>]*\bstyle=["\'][^"\']*background(?:-image)?\s*:\s*url\(\s*["\']?([^"\')\s]+)[^"\']*["\'][^>]*\b(?:class|id)=["\'][^"\']*\blogo\b', re.IGNORECASE),
+    # 8) src 자체에 "logo" 포함 (header_logo.png, brand-logo.svg 등)
+    re.compile(r'<img[^>]*\bsrc=["\']([^"\']*\blogo\b[^"\']*\.(?:png|svg|jpe?g|webp)[^"\']*)["\']', re.IGNORECASE),
+    # 9) <header>...<img src="..."> (헤더 영역 첫 img)
+    re.compile(r'<header\b[^>]*>(?:[^<]|<(?!img))*<img[^>]*\bsrc=["\']([^"\']+\.(?:png|svg|jpe?g|webp)[^"\']*)["\']', re.IGNORECASE | re.DOTALL),
+    # 10) <nav>...<img src="..."> (nav 영역 첫 img)
+    re.compile(r'<nav\b[^>]*>(?:[^<]|<(?!img))*<img[^>]*\bsrc=["\']([^"\']+\.(?:png|svg|jpe?g|webp)[^"\']*)["\']', re.IGNORECASE | re.DOTALL),
+    # 11) Open Graph image (대표 이미지) - 최후 fallback
+    re.compile(r'<meta[^>]*\bproperty=["\']og:image["\'][^>]*\bcontent=["\']([^"\']+)["\']', re.IGNORECASE),
+    re.compile(r'<meta[^>]*\bcontent=["\']([^"\']+)["\'][^>]*\bproperty=["\']og:image["\']', re.IGNORECASE),
+]
+
+# CSS 파일에서 .logo { background-image: url(...) } 추출용
+LOGO_CSS_PATTERN = re.compile(
+    r'\.[\w-]*\blogo\b[\w-]*\s*(?:,\s*\.[\w-]+\s*)*\{[^}]*background(?:-image)?\s*:\s*url\(\s*["\']?([^"\')\s]+)',
+    re.IGNORECASE | re.DOTALL,
+)
+
+
+def find_logo_url_in_html(html: str, base_url: str, css_texts: list[str] | None = None) -> str | None:
+    """HTML에서 logo URL 찾기. class/id/alt → 부모 + 중첩 img → background-image → src에 logo → header/nav → og:image 순."""
+    for pat in LOGO_IMG_PATTERNS:
+        for m in pat.finditer(html):
+            src = m.group(1)
+            if not src or src.startswith("data:"):
+                continue
+            if re.search(r"(blank|spacer|pixel|transparent|1x1)\b", src, re.IGNORECASE):
+                continue
+            return urljoin(base_url, src)
+    # 외부 CSS에서 .logo background-image 추출
+    for css in (css_texts or []):
+        m = LOGO_CSS_PATTERN.search(css)
+        if m:
+            src = m.group(1)
+            if src and not src.startswith("data:"):
+                return urljoin(base_url, src)
+    return None
+
+
+HEX6 = re.compile(r"#([0-9a-fA-F]{6})\b")
+HEX3 = re.compile(r"#([0-9a-fA-F]{3})\b(?![0-9a-fA-F])")
+RGB  = re.compile(r"rgba?\(\s*(\d{1,3})\s*,\s*(\d{1,3})\s*,\s*(\d{1,3})\s*(?:,\s*[\d.]+\s*)?\)")
+CSS_VAR_HEX = re.compile(r"--[\w-]+\s*:\s*(#[0-9a-fA-F]{3,8})", re.IGNORECASE)
+CSS_LINK = re.compile(r'<link[^>]+rel=["\']stylesheet["\'][^>]+href=["\']([^"\']+)["\']', re.IGNORECASE)
+STYLE_BLOCK = re.compile(r"<style[^>]*>(.*?)</style>", re.IGNORECASE | re.DOTALL)
+
+# 무채색·아주 흔한 노이즈 컬러 (이런 건 brand color로 잡지 않음)
+NOISE = {
+    "#ffffff", "#000000", "#fff", "#000",
+    "#333", "#222", "#111", "#444", "#555", "#666", "#777", "#888", "#999",
+    "#aaa", "#bbb", "#ccc", "#ddd", "#eee", "#f0f0f0", "#f5f5f5", "#fafafa",
+}
+
+
+def _normalize(hex_str: str) -> str:
+    h = hex_str.lstrip("#").lower()
+    if len(h) == 3:
+        h = "".join(c * 2 for c in h)
+    if len(h) == 8:
+        h = h[:6]
+    return f"#{h}"
+
+
+def _rgb_to_hex(r: int, g: int, b: int) -> str:
+    return f"#{r:02x}{g:02x}{b:02x}"
+
+
+def _hex_to_rgb(h: str) -> tuple[int, int, int]:
+    h = h.lstrip("#")
+    return int(h[0:2], 16), int(h[2:4], 16), int(h[4:6], 16)
+
+
+def _distance(a: str, b: str) -> float:
+    ar, ag, ab = _hex_to_rgb(a)
+    br, bg, bb = _hex_to_rgb(b)
+    return ((ar - br) ** 2 + (ag - bg) ** 2 + (ab - bb) ** 2) ** 0.5
+
+
+def _is_grayscale(h: str, tol: int = 12) -> bool:
+    r, g, b = _hex_to_rgb(h)
+    return max(r, g, b) - min(r, g, b) < tol
+
+
+def _extract_hex(text: str) -> list[str]:
+    """텍스트에서 모든 hex 색상 추출 (정규화)."""
+    out: list[str] = []
+    out.extend(_normalize(m.group(0)) for m in HEX6.finditer(text))
+    out.extend(_normalize(m.group(0)) for m in HEX3.finditer(text))
+    for m in RGB.finditer(text):
+        r, g, b = int(m.group(1)), int(m.group(2)), int(m.group(3))
+        if 0 <= r <= 255 and 0 <= g <= 255 and 0 <= b <= 255:
+            out.append(_rgb_to_hex(r, g, b))
+    return out
+
+
+def _cluster(colors: Counter, threshold: float = 25.0) -> list[tuple[str, int]]:
+    """비슷한 색은 묶음. 가장 빈도 높은 색을 대표로."""
+    ranked = colors.most_common()
+    clusters: list[tuple[str, int]] = []
+    for color, count in ranked:
+        merged = False
+        for i, (rep, rep_count) in enumerate(clusters):
+            if _distance(color, rep) < threshold:
+                clusters[i] = (rep, rep_count + count)
+                merged = True
+                break
+        if not merged:
+            clusters.append((color, count))
+    return clusters
+
+
+async def _fetch_html_and_css(homepage_url: str, max_css_files: int = 8) -> tuple[str, list[str]]:
+    """홈페이지 HTML + 외부 CSS(Top N)를 한 번에 fetch. 로고/색상 추출이 사이트를 중복으로 긁지 않도록 공유.
+    _fetch_html이 SSL 약함/host mismatch까지 fallback 처리. 실패 시 ("", [])."""
+    status, html = await _fetch_html(homepage_url)
+    if status != 200 or not html:
+        logger.warning("[color_extractor] homepage fetch failed status=%s url=%s", status, homepage_url)
+        return "", []
+    css_texts: list[str] = []
+    for css_href in CSS_LINK.findall(html)[:max_css_files]:
+        cstatus, ctext = await _fetch_html(urljoin(homepage_url, css_href), timeout=15.0)
+        if cstatus == 200 and ctext:
+            css_texts.append(ctext)
+    return html, css_texts
+
+
+def _colors_from_text(html: str, css_texts: list[str], source_url: str = "") -> dict:
+    """이미 받아온 HTML + CSS 텍스트에서 hex 빈도 분석 → primary/accent/text + palette. (fetch 없음, 순수 계산)"""
+    # 1. HTML 내 <style> 블록 + 통째(inline style="color:#...") + 외부 CSS
+    all_text_chunks: list[str] = list(STYLE_BLOCK.findall(html))
+    all_text_chunks.append(html)
+    all_text_chunks.extend(css_texts)
+
+    # 2. 모든 hex 추출 (NOISE 제외)
+    counter: Counter = Counter()
+    for text in all_text_chunks:
+        for color in _extract_hex(text):
+            if color in NOISE:
+                continue
+            counter[color] += 1
+
+    if not counter:
+        logger.info("[color_extractor] no colors extracted from %s", source_url)
+        return {}
+
+    # 3. 비슷한 색 클러스터링
+    clustered = _cluster(counter)
+
+    # 4. primary = 빈도 높은 채도 있는 색 / accent = 두번째 채도 있는 색 / text = 빈도 높은 무채색
+    chromatic = [c for c, _ in clustered if not _is_grayscale(c)]
+    grayscale = [c for c, _ in clustered if _is_grayscale(c)]
+
+    palette_top = clustered[:8]
+    palette = [{"name": f"색상 {i+1}", "hex": h, "usage": f"빈도 {n}"} for i, (h, n) in enumerate(palette_top)]
+
+    return {
+        "brand_colors": {
+            "primary": chromatic[0] if chromatic else None,
+            "accent": chromatic[1] if len(chromatic) > 1 else None,
+            "text": grayscale[0] if grayscale else None,
+        },
+        "color_palette": palette,
+        "extracted_from": "html+css",
+    }
+
+
+async def extract_brand_colors_from_site(homepage_url: str, max_css_files: int = 8) -> dict:
+    """홈페이지 HTML + 외부 CSS fetch → hex 색상 빈도 분석 → primary/accent/text + palette 5종."""
+    html, css_texts = await _fetch_html_and_css(homepage_url, max_css_files)
+    if not html:
+        return {}
+    return _colors_from_text(html, css_texts, homepage_url)
+
+
+async def extract_brand_assets_from_site(homepage_url: str, max_css_files: int = 8) -> dict:
+    """사이트를 한 번만 fetch해서 로고 URL과 brand 색상을 함께 추출.
+    반환: {"logo_url": str | None, "colors": {brand_colors, color_palette, ...} | {}}"""
+    html, css_texts = await _fetch_html_and_css(homepage_url, max_css_files)
+    if not html:
+        return {"logo_url": None, "colors": {}}
+    return {
+        "logo_url": find_logo_url_in_html(html, homepage_url, css_texts=css_texts),
+        "colors": _colors_from_text(html, css_texts, homepage_url),
+    }

app/integrations/llm/gemini_vision.py

@@ -1,334 +0,0 @@
-"""Gemini Vision — 로고/브랜드 비주얼 자동 분석 (OpenAI 호환 모드).
-
-정확한 hex 색상은 color_extractor가 CSS에서 직접 뽑음 (Vision은 근사값밖에 못 냄).
-Vision은 사람이 봐야 알 수 있는 정성 정보 — 심볼 형태/워드마크/톤 — 를 담당.
-"""
-import asyncio
-import base64
-import json
-import logging
-import re
-import ssl
-import httpx
-import resvg_py
-from openai import AsyncOpenAI
-
-logger = logging.getLogger(__name__)
-
-DEFAULT_MODEL = "gemini-2.5-flash"
-
-
-class VisionClient:
-    """Gemini Vision을 OpenAI 호환 endpoint로 호출. GEMINI_API_KEY만 필요."""
-
-    def __init__(self, api_key: str, model: str = DEFAULT_MODEL, timeout: float = 30.0, max_retries: int = 2):
-        self.client = AsyncOpenAI(
-            api_key=api_key,
-            base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
-            timeout=timeout,
-            max_retries=max_retries,
-        )
-        self.model = model
-
-    @staticmethod
-    def _extract_json(text: str) -> dict | None:
-        if not text:
-            return None
-        m = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", text, re.DOTALL)
-        if m:
-            try:
-                return json.loads(m.group(1))
-            except json.JSONDecodeError:
-                pass
-        m = re.search(r"\{.*\}", text, re.DOTALL)
-        if m:
-            try:
-                return json.loads(m.group(0))
-            except json.JSONDecodeError:
-                return None
-        return None
-
-    @staticmethod
-    async def _fetch_as_data_url(url: str) -> str | None:
-        """Gemini는 URL 직접 fetch가 막힌 호스트가 많아 base64 인라인으로 변환.
-        + 'image does not exist' 같은 placeholder 이미지 거부 (작은 bytes / 잘못된 content-type).
-        + 한국 의료 사이트 중 SSL이 약해서 표준 검증에 실패하는 곳 대응 (3단 SSL fallback)."""
-        headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36"}
-
-        def _weak_ctx() -> ssl.SSLContext:
-            ctx = ssl.create_default_context()
-            try:
-                ctx.set_ciphers("DEFAULT@SECLEVEL=1")
-            except ssl.SSLError:
-                pass
-            return ctx
-
-        last_err: Exception | None = None
-        for verify in (True, _weak_ctx(), False):
-            try:
-                async with httpx.AsyncClient(
-                    timeout=15.0, follow_redirects=True, headers=headers, verify=verify,
-                ) as c:
-                    resp = await c.get(url)
-                if resp.status_code != 200:
-                    logger.warning("[vision] fetch %s status=%s", url, resp.status_code)
-                    return None
-                mime = resp.headers.get("content-type", "").split(";")[0].strip()
-                # 실제 이미지가 아니면 거부 (HTML 페이지가 404 대신 200으로 리다이렉트 되는 경우)
-                if not mime.startswith("image/"):
-                    logger.warning("[vision] %s not an image (content-type=%s)", url, mime)
-                    return None
-                # SVG는 Gemini가 못 보므로 즉시 PNG로 래스터화 (resvg, in-memory ~1ms)
-                content = resp.content
-                if mime == "image/svg+xml" or url.lower().split("?")[0].endswith(".svg"):
-                    try:
-                        content = bytes(resvg_py.svg_to_bytes(svg_string=resp.text))
-                        mime = "image/png"
-                    except Exception as e:
-                        logger.warning("[vision] svg rasterize failed %s: %s", url, e)
-                        return None
-                size = len(content)
-                if size < 500:
-                    logger.warning("[vision] %s too small (%d bytes) — likely placeholder", url, size)
-                    return None
-                b64 = base64.b64encode(content).decode("ascii")
-                return f"data:{mime};base64,{b64}"
-            except (httpx.ConnectError, httpx.ReadError, ssl.SSLError) as e:
-                last_err = e
-                continue
-            except Exception as e:
-                logger.warning("[vision] fetch error %s: %s", url, e)
-                return None
-        logger.warning("[vision] fetch %s SSL fallback all failed: %s", url, last_err)
-        return None
-
-    async def _ask(self, image_urls: list[str], prompt: str, max_tokens: int = 4000) -> dict | None:
-        content: list[dict] = []
-        for u in image_urls:
-            if not u:
-                continue
-            data_url = await self._fetch_as_data_url(u)
-            if not data_url:
-                continue
-            content.append({"type": "image_url", "image_url": {"url": data_url}})
-        if not any(c.get("type") == "image_url" for c in content):
-            logger.warning("[vision] no images could be fetched")
-            return None
-        content.append({"type": "text", "text": prompt})
-
-        try:
-            resp = await self.client.chat.completions.create(
-                model=self.model,
-                messages=[{"role": "user", "content": content}],
-                max_tokens=max_tokens,
-            )
-            choice = resp.choices[0]
-            if choice.finish_reason != "stop":
-                logger.warning("[vision] unexpected finish_reason=%s", choice.finish_reason)
-            return self._extract_json(choice.message.content or "")
-        except Exception as e:
-            logger.warning("[vision] error: %s", e)
-            return None
-
-    async def describe_svg_text(self, svg_url: str) -> dict | None:
-        """SVG는 Gemini Vision이 못 보지만 XML 텍스트 자체는 LLM이 읽을 수 있음.
-        SVG 소스를 받아 그대로 text endpoint에 던지고 색·심볼·텍스트를 추론하게 함.
-        analyze_brand_assets와 동일한 스키마(logo_description/style/has_symbol/...) 반환."""
-        headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36"}
-
-        def _weak_ctx() -> ssl.SSLContext:
-            ctx = ssl.create_default_context()
-            try:
-                ctx.set_ciphers("DEFAULT@SECLEVEL=1")
-            except ssl.SSLError:
-                pass
-            return ctx
-
-        svg_text: str | None = None
-        for verify in (True, _weak_ctx(), False):
-            try:
-                async with httpx.AsyncClient(
-                    timeout=15.0, follow_redirects=True, headers=headers, verify=verify,
-                ) as c:
-                    resp = await c.get(svg_url)
-                if resp.status_code == 200:
-                    svg_text = resp.text
-                break
-            except (httpx.ConnectError, httpx.ReadError, ssl.SSLError):
-                continue
-            except Exception as e:
-                logger.warning("[vision] svg fetch error %s: %s", svg_url, e)
-                return None
-        if not svg_text:
-            logger.warning("[vision] svg fetch failed %s", svg_url)
-            return None
-        # 페이로드 폭주 방지 — 평범한 로고 SVG는 수 KB 수준
-        if len(svg_text) > 60000:
-            svg_text = svg_text[:60000]
-
-        prompt = (
-            "아래는 병원 로고 SVG 소스 코드입니다. SVG 마크업(path/circle/text/fill/stroke 등)을 "
-            "읽고 로고의 시각적 특징을 추론해 아래 JSON 스키마로만 응답하세요. 코드펜스 없이 순수 JSON.\n"
-            "{\n"
-            '  "logo_description": "심볼 형태 + 워드마크 + 톤을 1~2문장 한국어로",\n'
-            '  "logo_style": "minimal | illustrative | typographic | abstract 중 하나",\n'
-            '  "has_symbol": "심볼/아이콘이 있으면 true, 글자만 있으면 false (boolean)",\n'
-            '  "logo_symbol": "심볼 묘사 (예: \'잎사귀\'). 없으면 빈 문자열",\n'
-            '  "logo_text": "워드마크 텍스트 그대로. <text> 태그 내용 우선",\n'
-            '  "logo_colors_desc": "쓰인 색감을 사람이 부르는 이름으로 (예: \'딥네이비 + 골드\'). hex 출력 금지"\n'
-            "}\n"
-            "주의: hex 값이나 URL은 출력하지 마세요 (별도 추출 로직 처리). 모든 텍스트는 한국어로.\n\n"
-            "SVG 소스:\n"
-            f"{svg_text}"
-        )
-        try:
-            resp = await self.client.chat.completions.create(
-                model=self.model,
-                messages=[{"role": "user", "content": prompt}],
-                max_tokens=8000,  # Gemini 2.5는 thinking 토큰을 max_tokens에서 차감하므로 여유 필요
-            )
-            choice = resp.choices[0]
-            if choice.finish_reason != "stop":
-                logger.warning("[vision] svg describe finish_reason=%s", choice.finish_reason)
-            result = self._extract_json(choice.message.content or "")
-        except Exception as e:
-            logger.warning("[vision] svg describe error: %s", e)
-            return None
-        if not result:
-            return None
-        result["logo_images"] = {"circle": None, "horizontal": svg_url, "korean": None}
-        return result
-
-    async def analyze_brand_assets(
-        self,
-        logo_url: str | None,
-        homepage_url: str | None,
-        additional_images: list[str] | None = None,
-    ) -> dict:
-        """로고 이미지를 보고 정성 분석. 정확한 hex는 color_extractor가 따로 처리하므로 여기선 안 뽑음."""
-        urls = [u for u in [logo_url] + list(additional_images or []) if u]
-        if not urls:
-            return {}
-        prompt = (
-            "당신은 브랜드 로고 시각 분석가입니다. 첨부된 이미지(첫 번째가 병원의 대표 로고)를 보고 "
-            "아래 JSON 스키마로만 응답하세요. 코드펜스 없이 순수 JSON만 출력.\n"
-            "{\n"
-            '  "logo_description": "로고를 1~2문장으로 설명 (심볼 형태 + 워드마크 + 전반적 톤). 예: \'둥근 잎사귀를 감싼 추상 심볼에 세리프 한글 워드마크, 차분하고 고급스러운 톤\'",\n'
-            '  "logo_style": "minimal | illustrative | typographic | abstract 중 하나",\n'
-            '  "has_symbol": "심볼/아이콘이 있으면 true, 글자만 있으면 false (boolean)",\n'
-            '  "logo_symbol": "심볼이 묘사하는 대상 (예: \'잎사귀\', \'추상 곡선\'). 없으면 빈 문자열",\n'
-            '  "logo_text": "로고에 보이는 워드마크 텍스트 그대로 (한글/영문). 없으면 빈 문자열",\n'
-            '  "logo_colors_desc": "로고에 쓰인 색감을 사람이 부르는 이름으로 서술 (예: \'딥네이비 + 골드\')",\n'
-            '  "logo_colors_hex": ["로고에서 시각적으로 두드러진 색 정확히 5개의 hex 근사값 배열. 예: [\'#1A2B3C\', \'#D4A017\', \'#FFFFFF\', \'#9E5C2A\', \'#1F1F1F\']. 강한 색이 5개 안 되면 음영/명도 차이로 5개 채울 것. 빈 배열 금지."]\n'
-            "}\n"
-            "주의: logo_colors_hex 는 시각 추정이라 정확도 떨어질 수 있음. CSS 추출이 우선이고 이건 fallback/보완 용.\n"
-            "모든 설명/텍스트 값은 반드시 한국어로 작성하세요 (영어 금지)."
-        )
-        result = await self._ask(urls, prompt)
-        if not result:
-            return {}
-        # logo_images는 우리가 직접 채움 (Vision은 묘사만)
-        result["logo_images"] = {"circle": None, "horizontal": logo_url, "korean": None}
-        # logo_colors_hex 5개 강제 정규화 — LLM 이 4개나 6개 줄 수도 있어서 길이 fallback.
-        hex_list = [h for h in (result.get("logo_colors_hex") or []) if isinstance(h, str) and h.startswith("#")]
-        if hex_list:
-            while len(hex_list) < 5:
-                hex_list.append(hex_list[-1])  # 마지막 색 복제로 패딩
-            result["logo_colors_hex"] = hex_list[:5]
-        else:
-            result["logo_colors_hex"] = []
-        return result
-
-    async def describe_channel_logos(
-        self,
-        official_logo_url: str | None,
-        channel_logos: list[dict],
-    ) -> dict | None:
-        """채널별 프로필 이미지(로고)를 보고 각각 설명 + 공식 로고와 일치 여부 평가.
-        channel_logos: [{"channel": "Instagram", "url": "..."}, ...]
-        반환: {"channel_logos": [{"channel","logo_description","is_official"}], "inconsistency_summary", "recommendation"}
-
-        **3채널씩 묶어 병렬 호출** (한 번에 다 묶으면 LLM이 채널-이미지 매칭 헷갈려 같은 묘사를
-        여러 채널에 복사하는 문제 — VIEW 한국페북·영문인스타가 둘 다 "공식 로고" 묘사로 잘못
-        박혔던 케이스 — 가 있어서 분리. 1채널씩 N번보다 가성비 좋음)."""
-        items = [c for c in channel_logos if c.get("url")]
-        if not items:
-            return None
-
-        CHUNK = 3
-
-        async def _chunk(batch: list[dict]) -> list[dict]:
-            urls = [official_logo_url] + [c["url"] for c in batch] if official_logo_url else [c["url"] for c in batch]
-            n = len(batch)
-            # 이미지 번호 ↔ 채널 매핑 명시
-            if official_logo_url:
-                mapping = "이미지 1 = 공식 로고\n" + "\n".join(
-                    f"이미지 {i+2} = {c.get('channel','?')} 채널 프로필" for i, c in enumerate(batch)
-                )
-                instruction = (
-                    f"{mapping}\n\n"
-                    f"이미지 2~{n+1}(채널 프로필 {n}개)을 각각 **그 이미지에 실제로 보이는 그대로** "
-                    "한국어 1문장으로 묘사하세요 (색·형태·텍스트·배경 그대로).\n"
-                    "❗ 공식 로고(이미지 1) 묘사를 절대 복사하지 마세요. 각 채널 이미지에 보이는 실제 특징만.\n"
-                    "각 채널이 공식 로고와 시각적으로 거의 동일하면 is_official=true, "
-                    "심볼/색/배경/텍스트가 다르거나 모델 사진이면 false.\n"
-                )
-            else:
-                mapping = "\n".join(f"이미지 {i+1} = {c.get('channel','?')} 채널 프로필" for i, c in enumerate(batch))
-                instruction = (
-                    f"{mapping}\n\n"
-                    f"각 이미지를 보이는 그대로 한국어 1문장으로 묘사 (색·형태·텍스트·배경).\n"
-                )
-            schema_lines = ",\n".join(
-                f'    {{"channel": "{c.get("channel","?")}", "logo_description": "...", "is_official": true}}'
-                for c in batch
-            )
-            p = (
-                instruction
-                + "\n아래 JSON으로만 응답 (코드펜스 없이, 순수 JSON):\n{\n"
-                + f'  "channel_logos": [\n{schema_lines}\n  ]\n'
-                + "}\n"
-                + f"channel 필드는 위 매핑 그대로 ({', '.join(c.get('channel','?') for c in batch)}). "
-                + "logo_description은 반드시 한국어 (영어 금지)."
-            )
-            r = await self._ask(urls, p)
-            if not r:
-                return []
-            out = []
-            for c in r.get("channel_logos", []):
-                out.append({
-                    "channel": c.get("channel", ""),
-                    "logo_description": c.get("logo_description", ""),
-                    "is_official": bool(c.get("is_official", False)) if official_logo_url else None,
-                })
-            return out
-
-        # 3개씩 청크 → 병렬
-        chunks = [items[i:i+CHUNK] for i in range(0, len(items), CHUNK)]
-        results = await asyncio.gather(*[_chunk(b) for b in chunks], return_exceptions=True)
-        channel_logos_out: list[dict] = []
-        for r in results:
-            if isinstance(r, Exception):
-                logger.warning("[vision] channel_logo chunk error: %s", r)
-                continue
-            channel_logos_out.extend(r)
-        if not channel_logos_out:
-            return None
-
-        # 일관성 요약 + 권고는 결정적 산출 (LLM 한번 더 안 부름)
-        if official_logo_url:
-            mismatches = [c["channel"] for c in channel_logos_out if not c.get("is_official")]
-            if not mismatches:
-                summary = "모든 채널이 공식 로고를 일관되게 사용하고 있습니다."
-                rec = "현재 일관성 유지."
-            else:
-                summary = f"{len(mismatches)}개 채널({', '.join(mismatches)})이 공식 로고와 다른 이미지를 사용해 브랜드 일관성이 부족합니다."
-                rec = "비공식 채널 프로필을 공식 로고로 통일 권고."
-        else:
-            summary, rec = "", ""
-
-        return {
-            "channel_logos": channel_logos_out,
-            "inconsistency_summary": summary,
-            "recommendation": rec,
-        }

app/integrations/llm/prompt.py

@@ -1,7 +1,7 @@
 import os
 from pydantic import BaseModel
 from common.utils import get_env
-from integrations.llm.schemas.report import ReportInput, ReportOutput, YouTubeDiagnosisInput, YouTubeDiagnosisOutput
+from integrations.llm.schemas.report import ReportInput, ReportOutput
 from integrations.llm.schemas.plan import PlanInput, PlanOutput
 from integrations.llm.schemas.market import (
     MarketCompetitorsInput, MarketCompetitorsOutput,
@@ -80,10 +80,3 @@ market_target_audience_prompt = Prompt(
     input_class=MarketTargetAudienceInput,
     output_class=MarketTargetAudienceOutput,
 )
-
-youtube_diagnosis_prompt = Prompt(
-    file_name="youtube_diagnosis_prompt.txt",
-    prompt_model="REPORT_MODEL",
-    input_class=YouTubeDiagnosisInput,
-    output_class=YouTubeDiagnosisOutput,
-)

app/integrations/llm/schemas/plan.py

@@ -18,9 +18,6 @@ class PlanInput(BaseModel):
     tiktok: str | None = None
     instagram_en: str | None = None
     facebook_en: str | None = None
-    naver_blog: str | None = None
-    naver_cafe: str | None = None
-    kakao_talk: str | None = None
     channel_logos: str | None = None
     brand_assets: str | None = None
 
@@ -59,7 +56,7 @@ class ChannelBrandingRule(BaseModel):
     profile_photo: str
     banner_spec: str
     bio_template: str
-    current_status: Literal["correct", "incorrect", "N/A"]
+    current_status: Literal["correct", "incorrect", "missing"]
 
 
 class BrandPlanInconsistencyValue(BaseModel):

app/integrations/llm/schemas/report.py

@@ -68,18 +68,22 @@ class RegistryData(BaseModel):
 
 
 class ClinicSnapshot(BaseModel):
-    # _build_clinic_snapshot은 source 데이터 있을 때만 채움 (`if x:` 가드).
+    name: str
-    # required면 강남언니/홈페이지 누락 병원에서 ValidationError로 리포트 실패.
+    name_en: str
-    name: str | None = None
+    established: str
-    name_en: str | None = None
+    years_in_business: int
-    staff_count: int | None = None
+    staff_count: int
-    lead_doctor: LeadDoctor | None = None
+    lead_doctor: LeadDoctor
-    overall_rating: float | None = None
+    overall_rating: float
-    total_reviews: int | None = None
+    total_reviews: int
-    certifications: list[str] = []
+    price_range: PriceRange
-    location: str | None = None
+    certifications: list[str]
-    phone: str | None = None
+    media_appearances: list[str]
-    domain: str | None = None
+    medical_tourism: list[str]
+    location: str
+    nearest_station: str
+    phone: str
+    domain: str
     logo_images: LogoImages | None = None
     brand_colors: BrandColors | None = None
     source: DataSource | None = None
@@ -133,6 +137,7 @@ class YouTubeAudit(BaseModel):
     avg_video_length: str
     upload_frequency: str
     channel_created_date: str
+    subscriber_rank: str
     channel_description: str
     linked_urls: list[LinkedUrl]
     playlists: list[str]
@@ -159,8 +164,8 @@ class InstagramAccount(BaseModel):
 
 
 class InstagramAudit(BaseModel):
-    accounts: list[InstagramAccount] = []
+    accounts: list[InstagramAccount]
-    diagnosis: list[DiagnosisItem] = []
+    diagnosis: list[DiagnosisItem]
 
 
 # --- Facebook ---
@@ -193,17 +198,17 @@ class FacebookPage(BaseModel):
     linked_domain: str
     reviews: int
     recent_post_age: str
-    has_whatsapp: bool | None = None
+    has_whatsapp: bool
-    post_frequency: str
+    post_frequency: str | None = None
     top_content_type: str | None = None
-    engagement: str
+    engagement: str | None = None
 
 
 class FacebookAudit(BaseModel):
-    pages: list[FacebookPage] = []
+    pages: list[FacebookPage]
-    diagnosis: list[DiagnosisItem] = []
+    diagnosis: list[DiagnosisItem]
-    brand_inconsistencies: list[BrandInconsistency] = []
+    brand_inconsistencies: list[BrandInconsistency]
-    consolidation_recommendation: str | None = None
+    consolidation_recommendation: str
 
 
 # --- 기타 채널 / 웹사이트 ---
@@ -321,8 +326,6 @@ class ReportInput(BaseModel):
     tiktok: str | None = None
     instagram_en: str | None = None
     facebook_en: str | None = None
-    kakao_talk: str | None = None
-    naver_cafe: str | None = None
     channel_logos: str | None = None
 
 
@@ -348,20 +351,3 @@ class MarketingReport(BaseModel):
 
 
 ReportOutput = MarketingReport
-
-
-# --- YouTubeDiagnosis ---
-
-class YouTubeDiagnosisInput(BaseModel):
-    channel_name: str | None = None
-    subscribers: int | None = None
-    total_videos: int | None = None
-    total_views: int | None = None
-    avg_video_length: str | None = None
-    upload_frequency: str | None = None
-    top_videos: str | None = None
-    playlists: str | None = None
-
-
-class YouTubeDiagnosisOutput(BaseModel):
-    diagnosis: list[DiagnosisItem]

app/integrations/llm/temp-prompt/plan_prompt.txt

@@ -32,11 +32,8 @@
 ## 분석 리포트
 {report}
 
-## 추가 채널 데이터 (네이버 블로그 / 틱톡 / 인스타그램 EN / 페이스북 EN / 네이버 카페 / 카카오톡)
+## 추가 채널 데이터 (틱톡 / 인스타그램 EN / 페이스북 EN)
-아래에 데이터가 있는 채널은 channelStrategies에 **반드시 포함**하세요 (네이버 블로그, 틱톡, 영문 인스타그램, 영문 페이스북, 네이버 카페, 카카오톡). channelBranding은 SNS·블로그·카페까지만 포함(카카오톡은 메신저라 제외). null이면 제외.
+아래에 데이터가 있는 채널은 channelStrategies와 channelBranding에 **반드시 포함**하세요 (틱톡, 영문 인스타그램, 영문 페이스북). null이면 제외.
-
-### 네이버 블로그 (Naver Blog)
-{naver_blog}
 
 ### 틱톡 (TikTok)
 {tiktok}
@@ -47,25 +44,10 @@
 ### 페이스북 (영문 페이지)
 {facebook_en}
 
-### 네이버 카페 (공식 카페 운영 신호)
-{naver_cafe}
-- naver_cafe.cafeName: 카페명, naver_cafe.memberCount: 회원수
-- currentStatus는 "회원 N명" 형태로 간단하게. 게시글 수·최근 활동은 수집 불가 (추측 금지).
-- targetGoal은 회원 확보 목표 수치 + 운영 권장 (예: "회원 5,000명, 주 1~2회 공지 발행").
-
-### 카카오톡 채널 (URL only — 콘텐츠 수집 X, 존재 여부만)
-{kakao_talk}
-- channelStrategies 카드 하나로 포함. currentStatus는 "공식 카카오톡 채널 운영 중" 정도, targetGoal은 친구 추가 유도·상담 전환·자동응답 시나리오 구체화 등.
-
 ## 채널별 로고 분석 (Gemini Vision) — 채널룰/일관성의 근거
 {channel_logos}
 - 위 channel_logos[]의 각 항목: channel(채널명), logo_description(프로필이 어떻게 생겼는지), is_official(공식 로고와 일치 여부).
-- **channelBranding[]은 "어떻게 해야 하는지 권장 가이드라인" 섹션입니다.** 채널 통일 전략 기준으로 권장값 박을 것:
+- **channelBranding[]를 이 데이터로 채우세요**: 채널별로 profilePhoto=해당 채널의 logo_description, currentStatus=is_official이 true면 "correct" / false면 "incorrect" (데이터 없는 채널은 "missing"). bannerSpec은 권장 배너 규격(크기/디자인)을 작성.
-  - profilePhoto: **빈 문자열 ""로 두세요.** 시스템이 brand_assets.logo_description으로 직접 채우므로 LLM은 만들지 마세요.
-  - bannerSpec: 권장 배너 규격 (크기·디자인 가이드)
-  - bioTemplate: 권장 bio 템플릿 (구조·필수 요소·예약 링크 포함 여부)
-  - currentStatus: is_official=true면 "correct" / false면 "incorrect" (데이터 없는 채널은 "N/A") — 현재 상태 마커는 이 필드 하나로만.
-- 현재 채널 프로필 이미지의 실제 묘사(channel_logos.channel_logos[].logo_description)는 brandInconsistencies에서만 사용. channelBranding에서 채널별로 다른 묘사를 박지 마세요.
 - **brandInconsistencies[]에 "로고" 항목을 반드시 만드세요**: values[]에 채널마다 channel(채널명) / value(logo_description 그대로) / is_correct(is_official 값) 세 필드를 넣고, impact는 inconsistency_summary, recommendation은 channel_logos.recommendation 기반으로 작성 (공식 로고로 통일 권고 포함).
 
 ## 브랜드 자산 (홈페이지 CSS에서 추출 — 결정적 데이터)
@@ -87,10 +69,10 @@
 - brandInconsistencies: 채널 간 브랜딩 불일치 항목 및 개선 권고
 
 ### Section 2: channelStrategies
-- 메인 SNS 채널(Instagram, Facebook, YouTube, TikTok, 네이버 블로그) + 영문 계정(Instagram EN, Facebook EN) + **네이버 카페 / 카카오톡** (URL 있을 때) 카드를 **모두 포함**. 데이터 없는 채널도 빠뜨리지 말 것.
+- 리포트에 데이터가 있는 채널만 포함
-- **currentStatus**: 데이터 있는 채널은 실제 수치로 서술 (예: "14,047 팔로워, Reels 0개", "104K 구독자, 주 2~3회 업로드"). **데이터 없는 채널은 "계정 없음"** 으로 표시. `excellent`/`warning`/`good` 같은 등급·평가어 금지.
+- **currentStatus는 현재 채널 상태를 실제 수치로 서술** (예: "14,047 팔로워, Reels 0개", "104K 구독자, 주 2~3회 업로드"). `excellent`/`warning`/`good` 같은 등급·평가어를 절대 쓰지 마세요.
-- **targetGoal은 모든 채널에 반드시 채울 것** — 구체적 목표 수치(예: "50K 팔로워, Reels 주 5개"). 데이터 없는 채널도 시작 시 권장 목표를 작성하고 비우지 말 것.
+- targetGoal은 구체적 목표 수치로 작성 (예: "50K 팔로워, Reels 주 5개")
-- 각 채널의 우선순위(P0/P1/P2), 콘텐츠 유형, 게시 빈도, 포맷 가이드라인 모두 권장값으로 작성 — 데이터 없어도 시작 권장값으로 채울 것.
+- 각 채널의 우선순위(P0/P1/P2), 콘텐츠 유형, 게시 빈도, 포맷 가이드라인 작성
 - customerJourneyStage는 해당 채널의 주요 기여 단계로 설정
 
 ### Section 3: contentStrategy

app/integrations/llm/temp-prompt/report_prompt.txt

@@ -63,43 +63,26 @@
 ### 페이스북 (영문 페이지)
 {facebook_en}
 
-### 카카오톡 채널 (URL only — 수집 데이터 없음, 존재 여부만 확인)
-{kakao_talk}
-
-### 네이버 카페 (공식 카페 운영 신호)
-{naver_cafe}
-- naver_cafe.cafeName: 카페명
-- naver_cafe.memberCount: 회원수
-- 게시글 총 수·최근 게시일은 로그인 필요라 수집 불가. 추측 금지. 위 두 값만 사용.
-
 ### 채널별 로고 분석 (Gemini Vision)
 {channel_logos}
 - channel_logos.channel_logos[]에 각 채널의 로고 설명(logo_description)과 공식 로고 일치 여부(is_official)가 있습니다.
 - **facebook_audit.pages[].logo** 는 짧은 판정 타이틀로: is_official=true면 `"일치 (공식 로고)"`, false면 `"불일치 (비공식 변형)"`. 그리고 **facebook_audit.pages[].logo_description** 에 해당 채널의 logo_description(설명문)을 넣으세요.
 - 위 값들은 channel_logos 데이터 기반으로만 작성하고 추측하지 마세요.
 - 채널 간 로고 불일치(is_official=false)는 brand 일관성 진단(problem_diagnosis/weaknesses)에 반영하세요.
-- **brand_inconsistencies[]에 "로고" 항목을 반드시 만드세요**: values[]에 channel_logos.channel_logos[] 각 채널마다 다음 3필드를 **그대로** 박을 것 — channel(채널명 그대로), value(해당 채널의 logo_description 문자열 그대로 복붙), is_correct(해당 채널의 is_official 값 그대로). ❗ **채널-묘사 매핑을 절대 swap·재해석·임의 변형 금지**. channel_logos에 적힌 그대로 사용. impact는 channel_logos.inconsistency_summary 사용, recommendation은 channel_logos.recommendation 사용.
 
 ## clinic_snapshot / 채널 audit 작성 지침 (수집 데이터 그대로, 추측 금지)
 - clinic_snapshot.name 은 {clinic_name} 을 **그대로** 사용 (강남언니 표기명 '-본원' 등으로 바꾸지 말 것).
 - clinic_snapshot 의 overall_rating/total_reviews/staff_count/location/certifications/lead_doctor 는 강남언니({gangnam_unni}) 데이터의 값을 그대로 사용.
 - **instagram_audit.accounts 는 반드시 빈 배열 []로 두세요.** 계정 정보는 시스템이 수집 데이터로 직접 채우니 LLM은 만들지 말고, instagram_audit.diagnosis(진단)만 작성하세요.
 - facebook_audit.pages: KR 페북({facebook})·영문 페북({facebook_en}) 데이터가 있으면 **각각 별도 페이지**로 넣고, url/page_name/followers 등은 그 데이터 그대로. language/label 동일 규칙.
-- facebook_audit.pages[].top_content_type 은 해당 페이지 latestPosts의 **캡션·미디어를 읽고** 주로 올리는 콘텐츠를 의미 기반으로 짧게 묘사하세요 (예: "Before/After 사진 + 환자 여정 Reels", "이벤트·프로모션 카드뉴스", "다국어 시술 소개"). 단순 "동영상/이미지 위주"가 아니라 **무슨 주제**인지 쓰세요. (recent_post_age·post_frequency·engagement 수치는 시스템이 덮어쓰니 대략 적어도 됩니다.)
 - 위 수치·URL·이름은 제공된 데이터에서 그대로 쓰고 절대 지어내지 마세요.
 
 ## 기타 채널 현황 (other_channels) 작성 지침
 - other_channels에는 메인 audit(YouTube/Instagram/Facebook/Website)에 **포함되지 않은** 채널만 넣으세요.
 - 위 '채널 데이터'에 **실제 수집된 데이터가 있는 채널만** status=active와 실제 url로 일관되게 포함: 네이버 블로그, 강남언니, 틱톡, 영문 인스타그램({instagram_en}), 영문 페이스북({facebook_en}).
 - **영문 인스타그램·영문 페이스북은 KR 메인 audit(Instagram/Facebook)과 별개 계정이므로, 데이터가 있으면 반드시 other_channels에 "Instagram EN" / "Facebook EN"으로 각각 포함하세요 (절대 누락 금지).**
-- **카카오톡·네이버 카페**: {kakao_talk} 또는 {naver_cafe}에 url이 있으면 other_channels에 각각 "KakaoTalk" / "Naver Cafe"로 status=active + 해당 url로 포함. 수집된 콘텐츠 데이터는 없으므로 URL 존재 자체가 활성 채널 신호. **둘 다 null/빈 값이면 절대 만들지 마세요.**
+- **수집 데이터에 없는 채널(카카오톡/네이버플레이스/네이버카페/Threads 등)은 절대 임의로 만들지 마세요.** 데이터 없으면 그 채널은 생략 (랜덤 생성·추측 금지).
-- **그 외 데이터 없는 채널(네이버플레이스/Threads 등)은 절대 임의로 만들지 마세요.** 데이터 없으면 그 채널은 생략 (랜덤 생성·추측 금지).
 - url은 수집 데이터의 실제 URL만 사용. 없으면 빈 문자열.
-- **URL에 'https://www.facebook.com/' 같은 prefix를 절대 직접 만들지 마세요.** 수집 데이터의 URL을 그대로 사용. 이미 'https://...' 가 붙은 URL에 또 prefix 붙이면 'https://www.facebook.com/https://facebook.com/X' 같이 깨집니다. 받은 URL = 출력 URL.
-
-## registry_data 작성 지침 (clinic_snapshot 안)
-- **registry_data.website_en / district / branches / brand_group / naver_place_url / gangnam_unni_url / google_maps_url 모두 제공된 데이터에 명시되지 않으면 반드시 null로 두세요.**
-- 영문 사이트 URL, 영문명, 지점 정보 같은 거 데이터에 없으면 **절대 추측하거나 그럴듯해 보이는 도메인을 지어내지 마세요** (예: 'thepsclinic.com', '*-eng.com' 같은 거).
 
 ## 분석 지침
 
@@ -111,5 +94,5 @@
   - 데이터가 null인 계정은 항목을 만들지 마세요. icon은 instagram/facebook/video 등 플랫폼에 맞게 설정.
 - strengths와 weaknesses는 각 3개 이상 작성하세요.
 - roadmap은 우선순위 순으로 실행 가능한 액션으로 작성하세요.
-- kpi_dashboard는 코드가 결정적으로 산출해 후처리 강제 치환하므로 LLM 출력 무시됩니다. 빈 배열 또는 placeholder로 두세요.
+- kpis는 실제 수집된 수치 기반으로 현실적인 측정 가능 지표로 작성하세요.
 - conversion_strategy의 actions는 구체적인 실행 방안으로 작성하세요.

app/integrations/llm/temp-prompt/youtube_diagnosis_prompt.txt

@@ -1,24 +0,0 @@
-다음은 성형외과/피부과 유튜브 채널 데이터입니다.
-
-채널명: {channel_name}
-구독자 수: {subscribers}
-총 영상 수: {total_videos}
-총 조회수: {total_views}
-평균 영상 길이: {avg_video_length}
-업로드 주기: {upload_frequency}
-인기 영상 목록: {top_videos}
-플레이리스트: {playlists}
-
-위 데이터를 바탕으로 이 채널의 마케팅 문제점과 개선사항을 진단해줘.
-각 항목은 category(진단 카테고리), detail(상세 설명), severity(critical/warning/info) 형식의 JSON 배열로 출력해줘.
-
-진단 카테고리들은 다음과 같아. :
-구독자 대비 조회수 비율,
-최근 롱폼 조회수,
-Shorts 조회수,
-업로드 빈도,
-콘텐츠 톤앤매너,
-썸네일 디자인,
-최고 성과 Shorts
-
-출처 번호([1], [2] 등)는 굳이 포함하지 마.

app/integrations/naver.py

@@ -1,5 +1,4 @@
 import re
-import httpx
 from http import HTTPMethod
 from urllib.parse import urlparse
 from common.utils import http_request
@@ -65,20 +64,6 @@ class NaverClient:
             return None
         return resp.text
 
-    async def fetch_blog_total_count(self, handle: str) -> int | None:
-        """블로그 전체 글 수는 RSS에 없어서 PostList HTML에서 '554개의 글' 패턴 추출.
-        <h4 class="category_title pcol2">... 554개의 글</h4> 구조."""
-        resp = await http_request(
-            HTTPMethod.GET,
-            url=f"https://blog.naver.com/PostList.naver?blogId={handle}&from=postList&directAccess=true",
-            timeout=15,
-            label="naver-blog-postlist",
-        )
-        if not resp or not resp.is_success:
-            return None
-        m = re.search(r"(\d+)개의 글", resp.text)
-        return int(m.group(1)) if m else None
-
     async def get_blog_rss(self, url: str) -> dict | None:
         blog_handle = urlparse(url).path.strip("/").split("/")[0] if "://" in url else url
         xml = await self.fetch_blog_rss(blog_handle)
@@ -97,71 +82,10 @@ class NaverClient:
                 "postDate": date.group(1) if date else "",
                 "description": re.sub(r"<[^>]*>", "", desc.group(1) if desc else "").strip()[:150],
             })
-        # RSS의 totalCount 우선, 없으면 블로그 PostList 페이지에서 "N개의 글" 파싱, 그것도 없으면 RSS 글수
         total_match = re.search(r"<totalCount>(\d+)</totalCount>", xml)
-        if total_match:
-            total = int(total_match.group(1))
-        else:
-            total = await self.fetch_blog_total_count(blog_handle) or len(posts)
         return {
             "officialBlogUrl": f"https://blog.naver.com/{blog_handle}",
             "officialBlogHandle": blog_handle,
-            "totalResults": total,
+            "totalResults": int(total_match.group(1)) if total_match else len(posts),
             "posts": posts[:10],
         }
-
-    async def get_cafe_info(self, cafe_url: str, *_args, **_kwargs) -> dict | None:
-        """네이버 카페 운영 신호 수집. 2단계 fetch:
-        1) https://cafe.naver.com/{handle} → cafeId 추출
-        2) ArticleList.nhn?search.clubid={cafeId} → memberCount + cafeName 추출
-        본문/게시글은 로그인 필요라 못 가져옴. 회원수·카페명만 잡히면 충분.
-        common.http_request는 redirect 안 따라가서 카페 페이지에 안 맞아 httpx 직접 사용."""
-        handle = urlparse(cafe_url).path.strip("/").split("/")[0] if "://" in cafe_url else cafe_url.split("/")[-1]
-        if not handle:
-            return None
-
-        async with httpx.AsyncClient(
-            timeout=10, follow_redirects=True,
-            headers={"User-Agent": "Mozilla/5.0"},
-        ) as c:
-            # 1. cafeId 추출
-            try:
-                main = await c.get(f"https://cafe.naver.com/{handle}")
-            except Exception:
-                return {"url": f"https://cafe.naver.com/{handle}", "cafeHandle": handle, "accessible": False}
-            if main.status_code != 200:
-                return {"url": f"https://cafe.naver.com/{handle}", "cafeHandle": handle, "accessible": False}
-            cid_match = re.search(r'cafeId["\']?\s*[:=]\s*["\']?(\d+)', main.text)
-            cafe_id = cid_match.group(1) if cid_match else None
-
-            result: dict = {
-                "url": f"https://cafe.naver.com/{handle}",
-                "cafeHandle": handle,
-                "cafeId": cafe_id,
-                "accessible": True,
-                "cafeName": None,
-                "memberCount": None,
-            }
-            if not cafe_id:
-                return result
-
-            # 2. ArticleList 페이지에서 회원수 + 카페명 추출 (로그인 없이 접근 가능한 유일한 endpoint)
-            try:
-                listing = await c.get(
-                    f"https://cafe.naver.com/ArticleList.nhn?search.clubid={cafe_id}&search.menuid=&search.boardtype=L",
-                    headers={"Referer": f"https://cafe.naver.com/{handle}"},
-                )
-            except Exception:
-                return result
-            if listing.status_code != 200:
-                return result
-            mc = re.search(r'memberCount[^0-9]+(\d[\d,]*)', listing.text)
-            if mc:
-                result["memberCount"] = int(mc.group(1).replace(",", ""))
-            tm = re.search(r"<title>(.+?)\s*:\s*네이버 카페</title>", listing.text)
-            if tm:
-                name = re.sub(r"&amp;", "&", tm.group(1)).strip()
-                if "," in name:
-                    name = name.split(",", 1)[1].strip()
-                result["cafeName"] = name
-            return result

app/integrations/site_fetcher.py

@@ -1,66 +0,0 @@
-"""홈페이지 HTML + 외부 CSS 를 가져오는 fetch 전용 모듈.
-
-오래된 한국 의료 사이트들이 SSL DH_KEY_TOO_SMALL / cipher 약함 / host mismatch 등으로
-표준 fetch 에 차단되는 케이스가 많아 단계별 SSL fallback 으로 받는다.
-파싱·도메인 로직은 들어가지 않음 — 순수 HTTP 응답 본문 반환.
-"""
-import logging
-import re
-import ssl
-from urllib.parse import urljoin
-import httpx
-
-logger = logging.getLogger(__name__)
-
-CSS_LINK = re.compile(
-    r'<link[^>]+rel=["\']stylesheet["\'][^>]+href=["\']([^"\']+)["\']',
-    re.IGNORECASE,
-)
-
-
-def _make_ssl_context() -> ssl.SSLContext:
-    """보안 등급 1로 낮춤 + cert 검증 유지 (옛 한국 의료 사이트 cipher 약함 회피)."""
-    ctx = ssl.create_default_context()
-    try:
-        ctx.set_ciphers("DEFAULT@SECLEVEL=1")
-    except ssl.SSLError:
-        pass
-    return ctx
-
-
-async def fetch_html(url: str, timeout: float = 20.0) -> tuple[int, str]:
-    """SSL 검증 단계별 fallback 으로 HTML 본문 받기. 실패 시 (0, "")."""
-    headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36"}
-    try:
-        async with httpx.AsyncClient(timeout=timeout, follow_redirects=True, headers=headers) as c:
-            r = await c.get(url)
-            return r.status_code, r.text
-    except (httpx.ConnectError, httpx.ReadError, ssl.SSLError) as e:
-        logger.info("[fetch] %s standard SSL failed: %s — fallback to weak cipher", url, e)
-    try:
-        async with httpx.AsyncClient(timeout=timeout, follow_redirects=True, headers=headers, verify=_make_ssl_context()) as c:
-            r = await c.get(url)
-            return r.status_code, r.text
-    except (httpx.ConnectError, httpx.ReadError, ssl.SSLError) as e:
-        logger.info("[fetch] %s weak cipher failed: %s — fallback to verify=False", url, e)
-    try:
-        async with httpx.AsyncClient(timeout=timeout, follow_redirects=True, headers=headers, verify=False) as c:
-            r = await c.get(url)
-            return r.status_code, r.text
-    except Exception as e:
-        logger.warning("[fetch] %s all fallbacks failed: %s", url, e)
-        return 0, ""
-
-
-async def fetch_html_and_css(homepage_url: str, max_css_files: int = 8) -> tuple[str, list[str]]:
-    """홈페이지 HTML + 외부 CSS(Top N) 한 번에 fetch. 실패 시 ("", [])."""
-    status, html = await fetch_html(homepage_url)
-    if status != 200 or not html:
-        logger.warning("[fetch] homepage fetch failed status=%s url=%s", status, homepage_url)
-        return "", []
-    css_texts: list[str] = []
-    for css_href in CSS_LINK.findall(html)[:max_css_files]:
-        cstatus, ctext = await fetch_html(urljoin(homepage_url, css_href), timeout=15.0)
-        if cstatus == 200 and ctext:
-            css_texts.append(ctext)
-    return html, css_texts

app/integrations/vision.py

@@ -0,0 +1,173 @@
+"""Gemini Vision — 로고/브랜드 비주얼 자동 분석 (OpenAI 호환 모드).
+
+정확한 hex 색상은 color_extractor가 CSS에서 직접 뽑음 (Vision은 근사값밖에 못 냄).
+Vision은 사람이 봐야 알 수 있는 정성 정보 — 심볼 형태/워드마크/톤 — 를 담당.
+"""
+import base64
+import json
+import logging
+import re
+import httpx
+from openai import AsyncOpenAI
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_MODEL = "gemini-2.5-flash"
+
+
+class VisionClient:
+    """Gemini Vision을 OpenAI 호환 endpoint로 호출. GEMINI_API_KEY만 필요."""
+
+    def __init__(self, api_key: str, model: str = DEFAULT_MODEL, timeout: float = 30.0, max_retries: int = 2):
+        self.client = AsyncOpenAI(
+            api_key=api_key,
+            base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
+            timeout=timeout,
+            max_retries=max_retries,
+        )
+        self.model = model
+
+    @staticmethod
+    def _extract_json(text: str) -> dict | None:
+        if not text:
+            return None
+        m = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", text, re.DOTALL)
+        if m:
+            try:
+                return json.loads(m.group(1))
+            except json.JSONDecodeError:
+                pass
+        m = re.search(r"\{.*\}", text, re.DOTALL)
+        if m:
+            try:
+                return json.loads(m.group(0))
+            except json.JSONDecodeError:
+                return None
+        return None
+
+    @staticmethod
+    async def _fetch_as_data_url(url: str) -> str | None:
+        """Gemini는 URL 직접 fetch가 막힌 호스트가 많아 base64 인라인으로 변환.
+        + 'image does not exist' 같은 placeholder 이미지 거부 (작은 bytes / 잘못된 content-type)."""
+        try:
+            async with httpx.AsyncClient(timeout=15.0, follow_redirects=True) as c:
+                resp = await c.get(url)
+                if resp.status_code != 200:
+                    logger.warning("[vision] fetch %s status=%s", url, resp.status_code)
+                    return None
+                mime = resp.headers.get("content-type", "").split(";")[0].strip()
+                # 실제 이미지가 아니면 거부 (HTML 페이지가 404 대신 200으로 리다이렉트 되는 경우)
+                if not mime.startswith("image/"):
+                    logger.warning("[vision] %s not an image (content-type=%s)", url, mime)
+                    return None
+                size = len(resp.content)
+                if size < 500:
+                    logger.warning("[vision] %s too small (%d bytes) — likely placeholder", url, size)
+                    return None
+                b64 = base64.b64encode(resp.content).decode("ascii")
+                return f"data:{mime};base64,{b64}"
+        except Exception as e:
+            logger.warning("[vision] fetch error %s: %s", url, e)
+            return None
+
+    async def _ask(self, image_urls: list[str], prompt: str, max_tokens: int = 4000) -> dict | None:
+        content: list[dict] = []
+        for u in image_urls:
+            if not u:
+                continue
+            data_url = await self._fetch_as_data_url(u)
+            if not data_url:
+                continue
+            content.append({"type": "image_url", "image_url": {"url": data_url}})
+        if not any(c.get("type") == "image_url" for c in content):
+            logger.warning("[vision] no images could be fetched")
+            return None
+        content.append({"type": "text", "text": prompt})
+
+        try:
+            resp = await self.client.chat.completions.create(
+                model=self.model,
+                messages=[{"role": "user", "content": content}],
+                max_tokens=max_tokens,
+            )
+            choice = resp.choices[0]
+            if choice.finish_reason != "stop":
+                logger.warning("[vision] unexpected finish_reason=%s", choice.finish_reason)
+            return self._extract_json(choice.message.content or "")
+        except Exception as e:
+            logger.warning("[vision] error: %s", e)
+            return None
+
+    async def analyze_brand_assets(
+        self,
+        logo_url: str | None,
+        homepage_url: str | None,
+        additional_images: list[str] | None = None,
+    ) -> dict:
+        """로고 이미지를 보고 정성 분석. 정확한 hex는 color_extractor가 따로 처리하므로 여기선 안 뽑음."""
+        urls = [u for u in [logo_url] + list(additional_images or []) if u]
+        if not urls:
+            return {}
+        prompt = (
+            "당신은 브랜드 로고 시각 분석가입니다. 첨부된 이미지(첫 번째가 병원의 대표 로고)를 보고 "
+            "아래 JSON 스키마로만 응답하세요. 코드펜스 없이 순수 JSON만 출력.\n"
+            "{\n"
+            '  "logo_description": "로고를 1~2문장으로 설명 (심볼 형태 + 워드마크 + 전반적 톤). 예: \'둥근 잎사귀를 감싼 추상 심볼에 세리프 한글 워드마크, 차분하고 고급스러운 톤\'",\n'
+            '  "logo_style": "minimal | illustrative | typographic | abstract 중 하나",\n'
+            '  "has_symbol": "심볼/아이콘이 있으면 true, 글자만 있으면 false (boolean)",\n'
+            '  "logo_symbol": "심볼이 묘사하는 대상 (예: \'잎사귀\', \'추상 곡선\'). 없으면 빈 문자열",\n'
+            '  "logo_text": "로고에 보이는 워드마크 텍스트 그대로 (한글/영문). 없으면 빈 문자열",\n'
+            '  "logo_colors_desc": "로고에 쓰인 색감을 사람이 부르는 이름으로 서술 (예: \'딥네이비 + 골드\'). 정확한 hex는 출력하지 말 것"\n'
+            "}\n"
+            "주의: 색상 hex 값이나 logo URL 같은 필드는 출력하지 마세요 (별도 추출 로직이 처리).\n"
+            "모든 설명/텍스트 값은 반드시 한국어로 작성하세요 (영어 금지)."
+        )
+        result = await self._ask(urls, prompt)
+        if not result:
+            return {}
+        # logo_images는 우리가 직접 채움 (Vision은 묘사만)
+        result["logo_images"] = {"circle": None, "horizontal": logo_url, "korean": None}
+        return result
+
+    async def describe_channel_logos(
+        self,
+        official_logo_url: str | None,
+        channel_logos: list[dict],
+    ) -> dict | None:
+        """채널별 프로필 이미지(로고)를 보고 각각 설명 + 공식 로고와 일치 여부 평가.
+        channel_logos: [{"channel": "Instagram", "url": "..."}, ...]
+        반환: {"channel_logos": [{"channel","logo_description","is_official"}], "inconsistency_summary", "recommendation"}"""
+        items = [c for c in channel_logos if c.get("url")]
+        if not items:
+            return None
+
+        # 공식 로고가 있으면 맨 앞에 두고 기준으로 삼음
+        urls: list[str] = []
+        if official_logo_url:
+            urls.append(official_logo_url)
+        urls.extend(c["url"] for c in items)
+        channel_order = ", ".join(c.get("channel", "?") for c in items)
+
+        if official_logo_url:
+            header = (
+                "첨부 이미지 중 **첫 번째가 이 병원의 공식 로고**입니다. "
+                f"이어지는 이미지들은 채널별 프로필 이미지이며 순서는: {channel_order}.\n"
+                "각 채널 로고를 1문장으로 설명하고, 공식 로고(첫 번째)와 일치하면 is_official=true, "
+                "비공식 변형/모델사진/다른 이미지면 false로 평가하세요.\n"
+            )
+        else:
+            header = (
+                f"첨부 이미지는 한 병원의 채널별 프로필 이미지입니다. 순서: {channel_order}.\n"
+                "각 채널 로고를 1문장으로 설명하세요 (공식 로고 기준이 없으므로 is_official은 판단 가능하면만).\n"
+            )
+        prompt = (
+            header
+            + "아래 JSON으로만 응답 (코드펜스 없이 순수 JSON):\n"
+            "{\n"
+            '  "channel_logos": [{"channel": "...", "logo_description": "...", "is_official": true}],\n'
+            '  "inconsistency_summary": "채널 간 로고 일관성 1~2문장 요약",\n'
+            '  "recommendation": "통합 권고 1문장"\n'
+            "}\n"
+            "모든 logo_description·inconsistency_summary·recommendation은 반드시 한국어로 작성하세요 (영어 금지)."
+        )
+        return await self._ask(urls, prompt)

app/integrations/youtube.py

@@ -79,17 +79,7 @@ class YouTubeClient:
             if resp and resp.is_success:
                 videos = resp.json().get("items", [])[:10]
 
-        playlists: list[dict] = []
+        return {"channelId": channel_id, "channel": channel, "videos": videos}
-        resp = await http_request(
-            HTTPMethod.GET,
-            url=f"{YT}/playlists",
-            params={"part": "snippet", "channelId": channel_id, "maxResults": 50, "key": self.api_key},
-            label="yt-playlists",
-        )
-        if resp and resp.is_success:
-            playlists = resp.json().get("items", [])
-
-        return {"channelId": channel_id, "channel": channel, "videos": videos, "playlists": playlists}
 
     async def get_channel(self, url: str) -> dict | None:
         raw = await self.fetch_channel(url)
@@ -121,11 +111,6 @@ class YouTubeClient:
                 }
                 for v in raw["videos"]
             ],
-            "playlists": [
-                p.get("snippet", {}).get("title")
-                for p in raw["playlists"]
-                if p.get("snippet", {}).get("title")
-            ],
         }
 
     async def search_channels(self, query: str, max_results: int = 3) -> list[str]:

app/models/analysis.py

@@ -11,8 +11,6 @@ class Channels(BaseModel):
     tiktok: str | None = None
     instagram_en: str | None = None
     facebook_en: str | None = None
-    kakao_talk: str | None = None
-    naver_cafe: str | None = None
 
 
 class AnalysisOptions(BaseModel):

app/models/clinic.py

@@ -10,7 +10,9 @@ class ClinicResponse(BaseModel):
     hospital_name: str
     hospital_name_en: str | None
     road_address: str | None
+    url: str | None
     status: str
+    raw_data: dict | None
     created_at: str
     updated_at: str
 

app/models/plan.py

@@ -49,7 +49,7 @@ class ChannelBrandingRule(CamelModel):
     profile_photo: str
     banner_spec: str
     bio_template: str
-    current_status: Literal["correct", "incorrect", "N/A"]
+    current_status: Literal["correct", "incorrect", "missing"]
 
 
 class BrandGuide(CamelModel):

app/models/report.py

@@ -66,18 +66,22 @@ class RegistryData(CamelModel):
 
 
 class ClinicSnapshot(CamelModel):
-    # _build_clinic_snapshot은 source 데이터 있을 때만 필드 추가 (`if x:` 가드).
+    name: str
-    # 강남언니/홈페이지 수집 누락된 병원에서 required면 ValidationError로 리포트 전체 실패.
+    name_en: str
-    name: str | None = None
+    established: str
-    name_en: str | None = None
+    years_in_business: int
-    staff_count: int | None = None
+    staff_count: int
-    lead_doctor: LeadDoctor | None = None
+    lead_doctor: LeadDoctor
-    overall_rating: float | None = None
+    overall_rating: float
-    total_reviews: int | None = None
+    total_reviews: int
-    certifications: list[str] = []
+    price_range: PriceRange
-    location: str | None = None
+    certifications: list[str]
-    phone: str | None = None
+    media_appearances: list[str]
-    domain: str | None = None
+    medical_tourism: list[str]
+    location: str
+    nearest_station: str
+    phone: str
+    domain: str
     logo_images: LogoImages | None = None
     brand_colors: BrandColors | None = None
     source: DataSource | None = None
@@ -127,6 +131,7 @@ class YouTubeAudit(CamelModel):
     avg_video_length: str
     upload_frequency: str
     channel_created_date: str
+    subscriber_rank: str
     channel_description: str
     linked_urls: list[LinkedUrl]
     playlists: list[str]
@@ -151,8 +156,8 @@ class InstagramAccount(CamelModel):
 
 
 class InstagramAudit(CamelModel):
-    accounts: list[InstagramAccount] = []
+    accounts: list[InstagramAccount]
-    diagnosis: list[DiagnosisItem] = []
+    diagnosis: list[DiagnosisItem]
 
 
 class BrandInconsistencyValue(CamelModel):
@@ -183,17 +188,17 @@ class FacebookPage(CamelModel):
     linked_domain: str
     reviews: int
     recent_post_age: str
-    has_whatsapp: bool | None = None
+    has_whatsapp: bool
-    post_frequency: str
+    post_frequency: str | None = None
     top_content_type: str | None = None
-    engagement: str
+    engagement: str | None = None
 
 
 class FacebookAudit(CamelModel):
-    pages: list[FacebookPage] = []
+    pages: list[FacebookPage]
-    diagnosis: list[DiagnosisItem] = []
+    diagnosis: list[DiagnosisItem]
-    brand_inconsistencies: list[BrandInconsistency] = []
+    brand_inconsistencies: list[BrandInconsistency]
-    consolidation_recommendation: str | None = None
+    consolidation_recommendation: str
 
 
 class OtherChannel(CamelModel):

app/models/status.py

@@ -36,24 +36,9 @@ class DataSource(StrEnum):
     SCRAPE = "scrape"
 
 
-class SourceType(StrEnum):
-    MAINPAGE = "mainpage"
-    INSTAGRAM = "instagram"
-    FACEBOOK = "facebook"
-    NAVER_BLOG = "naver_blog"
-    YOUTUBE = "youtube"
-    TIKTOK = "tiktok"
-    GANGNAM_UNNI = "gangnam_unni"
-    KAKAOTALK = "kakaotalk"
-    NAVER_CAFE = "naver_cafe"
-    # 부가 수집/분석 (HTML/CSS 재크롤 + Vision 로고 매칭) — 한 raw_info entry 에 brandAssets/channelLogos 같이 보관.
-    BRANDING = "branding"
-
-
 class Language(StrEnum):
     KR = "KR"
     EN = "EN"
-    WW = "WW"
 
 
 class VideoType(StrEnum):

app/services/analysis.py

@@ -1,28 +1,29 @@
 import json
 import logging
-import re
+from common.db import fetchone, execute, fetch_raw, get_analysis_raw_data, save_analysis_report, get_market_analysis
-from datetime import datetime
-from urllib.parse import urlparse
-from common.db.run import update_run_report, update_run_plan, select_run_report_data
-from common.db.source import select_run_raw_data, select_mainpage_logo_url
-from common.db.market import select_market
 from integrations.llm.llm_service import LLMService
-from integrations.llm.prompt import report_prompt, plan_prompt, youtube_diagnosis_prompt
+from integrations.llm.prompt import report_prompt, plan_prompt
-from integrations.llm.schemas.report import ReportOutput, ClinicSnapshot, YouTubeAudit
+from integrations.llm.schemas.report import ReportOutput
-from services.branding import analyze_branding
+from services.instagram_audit import build_instagram_accounts
-from services.instagram_audit import build_instagram_audit
-from services.facebook_audit import build_facebook_audit
-from services.kpi_dashboard import build_kpi_dashboard
 from integrations.llm.schemas.plan import PlanOutput
+from models.status import AnalysisStatus
 
 logger = logging.getLogger(__name__)
 
 
 async def generate_report(analysis_run_id: str) -> ReportOutput:
-    raw = await select_run_raw_data(analysis_run_id)
+    run = await fetchone(
-    clinic   = raw.get("mainpage") or {}
+        "SELECT hospital_id FROM analysis_runs WHERE analysis_run_id = %s",
-    branding = raw.get("branding") or {}
+        (analysis_run_id,),
-    market   = await select_market(analysis_run_id)
+    )
+    clinic_row = await fetchone(
+        "SELECT raw_data FROM hospital_baseinfo WHERE hospital_id = %s",
+        (run["hospital_id"],),
+    )
+    raw_data = clinic_row["raw_data"] if clinic_row else None
+    clinic = json.loads(raw_data) if isinstance(raw_data, str) else (raw_data or {})
+    raw = await get_analysis_raw_data(analysis_run_id)
+    market = await get_market_analysis(analysis_run_id)
 
     def _json(v) -> str | None:
         return json.dumps(v, ensure_ascii=False) if v else None
@@ -39,36 +40,34 @@ async def generate_report(analysis_run_id: str) -> ReportOutput:
         "market_keywords":        _json(market.get("keywords")),
         "market_trend":           _json(market.get("trend")),
         "market_target_audience": _json(market.get("target_audience")),
-        # firecrawl 이 mainpage 에서 뽑은 branding 메타(logoUrl/ogImage/faviconUrl) + Vision/CSS 산출물
         "branding":               _json(clinic.get("branding")),
-        "brand_assets":           _json(branding.get("brandAssets")),
+        "brand_assets":           _json(clinic.get("brandAssets")),
-        "channel_logos":          _json(branding.get("channelLogos")),
+        "tiktok":                 _json(clinic.get("tiktok")),
-        # 부가 채널 (raw_info entry) — raw dict 의 한국식 key 그대로
+        "instagram_en":           _json(clinic.get("instagramEn")),
-        "tiktok":                 _json(raw.get("tiktok")),
+        "facebook_en":            _json(clinic.get("facebookEn")),
-        "instagram_en":           _json(raw.get("instagram_en")),
+        "channel_logos":          _json(clinic.get("channelLogos")),
-        "facebook_en":            _json(raw.get("facebook_en")),
-        "kakao_talk":             _json(raw.get("kakaotalk")),
-        "naver_cafe":             _json(raw.get("naver_cafe")),
-        # 메인 5채널은 raw dict 그대로 펼쳐서 prompt placeholder 와 매칭
         **{
-            source_type: _json(data)
+            channel: _json(data)
-            for source_type, data in raw.items()
+            for channel, data in raw.items()
-            if source_type not in {
-                "mainpage", "branding",
-                "tiktok", "instagram_en", "facebook_en", "kakaotalk", "naver_cafe",
-            }
         },
     }
 
     return await LLMService(provider="perplexity").generate(report_prompt, input_data)
 
-
 async def generate_plan(analysis_run_id: str) -> PlanOutput:
-    raw = await select_run_raw_data(analysis_run_id)
+    run = await fetchone(
-    clinic   = raw.get("mainpage") or {}
+        "SELECT hospital_id, report_data FROM analysis_runs WHERE analysis_run_id = %s",
-    branding = raw.get("branding") or {}
+        (analysis_run_id,),
-    report = await select_run_report_data(analysis_run_id)
+    )
-    market = await select_market(analysis_run_id)
+    clinic_row = await fetchone(
+        "SELECT raw_data FROM hospital_baseinfo WHERE hospital_id = %s",
+        (run["hospital_id"],),
+    )
+    raw_data = clinic_row["raw_data"] if clinic_row else None
+    clinic = json.loads(raw_data) if isinstance(raw_data, str) else (raw_data or {})
+    report_data = run["report_data"]
+    report = json.loads(report_data) if isinstance(report_data, str) else report_data
+    market = await get_market_analysis(analysis_run_id)
 
     def _json(v) -> str | None:
         return json.dumps(v, ensure_ascii=False) if v else None
@@ -86,28 +85,43 @@ async def generate_plan(analysis_run_id: str) -> PlanOutput:
         "market_keywords":        _json(market.get("keywords")),
         "market_trend":           _json(market.get("trend")),
         "market_target_audience": _json(market.get("target_audience")),
-        "tiktok":                 _json(raw.get("tiktok")),
+        "tiktok":                 _json(clinic.get("tiktok")),
-        "instagram_en":           _json(raw.get("instagram_en")),
+        "instagram_en":           _json(clinic.get("instagramEn")),
-        "facebook_en":            _json(raw.get("facebook_en")),
+        "facebook_en":            _json(clinic.get("facebookEn")),
-        "naver_blog":             _json(_naver_blog_summary(raw.get("naver_blog"))),
+        "channel_logos":          _json(clinic.get("channelLogos")),
-        "naver_cafe":             _json(raw.get("naver_cafe")),
+        "brand_assets":           _json(clinic.get("brandAssets")),
-        "kakao_talk":             _json(raw.get("kakaotalk")),
-        "channel_logos":          _json(branding.get("channelLogos")),
-        "brand_assets":           _json(branding.get("brandAssets")),
     }
 
     return await LLMService(provider="perplexity").generate(plan_prompt, input_data)
 
 
-def _build_clinic_snapshot(gangnam_unni: dict, mainpage: dict, brand_assets: dict, logo_url: str | None) -> dict:
+async def _build_overrides(analysis_run_id: str) -> dict:
+    run = await fetchone(
+        "SELECT hospital_id, instagram_data_id, facebook_data_id,"
+        " naver_blog_data_id, youtube_data_id, gangnam_unni_data_id"
+        " FROM analysis_runs WHERE analysis_run_id = %s",
+        (analysis_run_id,),
+    )
+    if not run:
+        return {}
+
+    hospital_row = await fetchone(
+        "SELECT raw_data FROM hospital_baseinfo WHERE hospital_id = %s",
+        (run["hospital_id"],),
+    )
+    hospital     = json.loads(hospital_row["raw_data"]) if hospital_row and isinstance(hospital_row.get("raw_data"), str) else (hospital_row or {}).get("raw_data") or {}
+    instagram    = await fetch_raw("instagram_data",    run["instagram_data_id"])    or {}
+    facebook     = await fetch_raw("facebook_data",     run["facebook_data_id"])     or {}
+    naver_blog   = await fetch_raw("naver_blog_data",   run["naver_blog_data_id"])   or {}
+    youtube      = await fetch_raw("youtube_data",      run["youtube_data_id"])      or {}
+    gangnam_unni = await fetch_raw("gangnam_unni_data", run["gangnam_unni_data_id"]) or {}
+
     snapshot: dict = {}
+
+    # ── gangnam_unni ──────────────────────────────────────────────────────────
     doctors = gangnam_unni.get("doctors", [])
     lead = max(doctors, key=lambda d: d.get("reviews", 0)) if doctors else None
     if gangnam_unni.get("name"):         snapshot["name"]           = gangnam_unni["name"]
-    if mainpage.get("clinicNameEn"):         snapshot["name_en"]        = mainpage["clinicNameEn"]
-    if mainpage.get("phone"):                snapshot["phone"]          = mainpage["phone"]
-    domain = mainpage.get("domain") or urlparse(mainpage.get("sourceUrl") or "").netloc
-    if domain:                               snapshot["domain"]         = domain
     if gangnam_unni.get("rating"):       snapshot["overall_rating"] = gangnam_unni["rating"]
     if gangnam_unni.get("totalReviews"): snapshot["total_reviews"]  = gangnam_unni["totalReviews"]
     if gangnam_unni.get("address"):      snapshot["location"]       = gangnam_unni["address"]
@@ -120,98 +134,24 @@ def _build_clinic_snapshot(gangnam_unni: dict, mainpage: dict, brand_assets: dic
             "rating":       lead.get("rating"),
             "review_count": lead.get("reviews"),
         }
-    # logo URL 은 raw_info.logo_url 컬럼에서, brand_colors 는 JSON 에서 강제 주입. LLM 의 null 처리 차단.
-    if logo_url:
-        snapshot["logo_images"] = {"circle": None, "horizontal": logo_url, "korean": None}
-    if brand_assets.get("brand_colors"): snapshot["brand_colors"] = brand_assets["brand_colors"]
-    return ClinicSnapshot.model_validate(snapshot).model_dump()
 
-
+    # ── instagram (KR·EN 계정을 코드에서 구성 → LLM 출력 무시하고 교체) ──────────────
-def _naver_blog_summary(blog: dict | None) -> dict | None:
+    ig_patch = build_instagram_accounts(
-    """plan 카드 한 장에 들어가는 건 전체 포스트 수와 최근 활동 시점뿐. 그 외(본문·링크·제목)는
+        instagram, hospital.get("instagramEn") or {}, hospital.get("channelLogos") or {},
-    던져봐야 토큰만 늘고 LLM 이 무관 정보로 hallucinate 함."""
-    if not blog:
-        return None
-    posts = blog.get("posts") or []
-    return {
-        "totalPosts":     blog.get("totalResults"),
-        "latestPostDate": posts[0].get("postDate") if posts else None,
-    }
-
-
-def _parse_iso_duration_seconds(iso: str) -> int:
-    m = re.match(r"PT(?:(\d+)H)?(?:(\d+)M)?(?:(\d+)S)?", iso or "")
-    if not m:
-        return 0
-    h, mins, s = (int(x or 0) for x in m.groups())
-    return h * 3600 + mins * 60 + s
-
-
-def _format_seconds(seconds: int) -> str:
-    m, s = divmod(seconds, 60)
-    h, m = divmod(m, 60)
-    return f"{h}시간 {m}분" if h else f"{m}분 {s}초"
-
-
-def _format_clock(seconds: int) -> str:
-    m, s = divmod(seconds, 60)
-    h, m = divmod(m, 60)
-    return f"{h}:{m:02d}:{s:02d}" if h else f"{m}:{s:02d}"
-
-
-def _calc_avg_video_length(videos: list[dict]) -> str:
-    durations = [_parse_iso_duration_seconds(v.get("duration", "")) for v in videos]
-    durations = [d for d in durations if d > 0]
-    if not durations:
-        return ""
-    return _format_seconds(sum(durations) // len(durations))
-
-
-def _relative_date(date_str: str) -> str:
-    if not date_str:
-        return ""
-    try:
-        past = datetime.fromisoformat(date_str[:10])
-    except ValueError:
-        return ""
-    days = (datetime.now() - past).days
-    if days < 1:
-        return "오늘"
-    if days < 30:
-        return f"{days}일 전"
-    if days < 365:
-        return f"{days // 30}개월 전"
-    return f"{days // 365}년 전"
-
-
-def _calc_upload_frequency(videos: list[dict]) -> str:
-    dates = sorted(
-        [v["date"][:10] for v in videos if v.get("date")],
-        reverse=True,
     )
-    if len(dates) < 2:
-        return ""
-    gaps = [
-        (datetime.fromisoformat(dates[i]) - datetime.fromisoformat(dates[i + 1])).days
-        for i in range(len(dates) - 1)
-    ]
-    avg_days = sum(gaps) // len(gaps)
-    if avg_days <= 7:
-        return f"주 {7 // max(avg_days, 1)}회"
-    if avg_days <= 30:
-        return f"월 {30 // avg_days}회"
-    return f"{avg_days}일에 1회"
 
+    # ── facebook ──────────────────────────────────────────────────────────────
+    fb_patch: dict = {}
+    if facebook.get("pageUrl"):    fb_patch["url"]           = facebook["pageUrl"]
+    if facebook.get("pageUrl"):    fb_patch["link"]          = facebook["pageUrl"]
+    if facebook.get("pageName"):   fb_patch["page_name"]     = facebook["pageName"]
+    if facebook.get("followers"):  fb_patch["followers"]     = facebook["followers"]
+    if facebook.get("intro"):      fb_patch["bio"]           = facebook["intro"]
+    if facebook.get("categories"): fb_patch["category"]     = ", ".join(facebook["categories"])
+    if facebook.get("website"):    fb_patch["linked_domain"] = facebook["website"]
 
-async def _build_youtube_audit(youtube: dict) -> dict:
+    # ── youtube ───────────────────────────────────────────────────────────────
-    videos = youtube.get("videos", [])
+    yt_patch: dict = {}
-    yt_patch: dict = {
-        "weekly_view_growth":        {"absolute": 0, "percentage": 0.0},
-        "estimated_monthly_revenue": {"min": 0, "max": 0},
-        "linked_urls":               [],
-        "avg_video_length":          _calc_avg_video_length(videos),
-        "upload_frequency":          _calc_upload_frequency(videos),
-    }
     if youtube.get("channelName"):  yt_patch["channel_name"]        = youtube["channelName"]
     if youtube.get("handle"):       yt_patch["handle"]              = youtube["handle"]
     if youtube.get("subscribers"):  yt_patch["subscribers"]         = youtube["subscribers"]
@@ -219,117 +159,62 @@ async def _build_youtube_audit(youtube: dict) -> dict:
     if youtube.get("totalViews"):   yt_patch["total_views"]         = youtube["totalViews"]
     if youtube.get("publishedAt"):  yt_patch["channel_created_date"] = youtube["publishedAt"][:10]
     if youtube.get("description"):  yt_patch["channel_description"] = youtube["description"]
-    if youtube.get("playlists"):    yt_patch["playlists"]            = youtube["playlists"]
+    if youtube.get("videos"):
-    if videos:
         yt_patch["top_videos"] = [
             {
                 "title":        v["title"],
                 "views":        v["views"],
-                "duration":     _format_clock(_parse_iso_duration_seconds(v.get("duration", ""))),
+                "duration":     v.get("duration"),
                 "type":         "Short" if "M" not in v.get("duration", "") else "Long",
-                "uploaded_ago": _relative_date(v.get("date", "")),
+                "uploaded_ago": v.get("date", "")[:10],
             }
-            for v in videos
+            for v in youtube["videos"]
         ]
 
-    diagnosis_result = await LLMService(provider="perplexity").generate(
+    overrides: dict = {}
-        youtube_diagnosis_prompt,
+    if snapshot:
-        {
+        overrides["clinic_snapshot"] = snapshot
-            "channel_name":    yt_patch.get("channel_name"),
+    if ig_patch:
-            "subscribers":     yt_patch.get("subscribers"),
+        overrides["instagram_audit"] = {"accounts": ig_patch}
-            "total_videos":    yt_patch.get("total_videos"),
+    if fb_patch:
-            "total_views":     yt_patch.get("total_views"),
+        overrides["facebook_audit"] = {"pages": [fb_patch]}
-            "avg_video_length": yt_patch.get("avg_video_length"),
+    if yt_patch:
-            "upload_frequency": yt_patch.get("upload_frequency"),
+        overrides["youtube_audit"] = yt_patch
-            "top_videos":      json.dumps(yt_patch.get("top_videos", []), ensure_ascii=False),
+    return overrides
-            "playlists":       json.dumps(yt_patch.get("playlists", []), ensure_ascii=False),
-        },
-    )
-    yt_patch["diagnosis"] = [item.model_dump() for item in diagnosis_result.diagnosis]
-
-    return YouTubeAudit.model_validate(yt_patch).model_dump()
 
 
 def _deep_merge(base: dict, overrides: dict) -> dict:
-    """dict 끼리 만나면 재귀로 안쪽까지 합치고, 그 외(list/scalar/None) 는 override 값으로 통째 치환."""
     for k, v in overrides.items():
         if isinstance(v, dict) and isinstance(base.get(k), dict):
             _deep_merge(base[k], v)
+        elif isinstance(v, list) and isinstance(base.get(k), list):
+            for i, item in enumerate(v):
+                if i < len(base[k]) and isinstance(item, dict) and isinstance(base[k][i], dict):
+                    _deep_merge(base[k][i], item)
         else:
             base[k] = v
     return base
 
-
+def _patch_report(result: ReportOutput, overrides: dict) -> ReportOutput:
-async def _build_overrides(analysis_run_id: str, result: ReportOutput) -> ReportOutput:
-    raw = await select_run_raw_data(analysis_run_id)
-    if not raw:
-        return result
-
-    mainpage     = raw.get("mainpage", {}) or {}
-    branding     = raw.get("branding", {}) or {}
-    instagram    = raw.get("instagram", {}) or {}
-    facebook     = raw.get("facebook", {}) or {}
-    youtube      = raw.get("youtube", {}) or {}
-    gangnam_unni = raw.get("gangnam_unni", {}) or {}
-    naver_blog   = raw.get("naver_blog", {}) or {}
-    instagram_en = raw.get("instagram_en", {}) or {}
-    facebook_en  = raw.get("facebook_en", {}) or {}
-    tiktok       = raw.get("tiktok", {}) or {}
-    naver_cafe   = raw.get("naver_cafe", {}) or {}
-    brand_assets  = branding.get("brandAssets") or {}
-    channel_logos = branding.get("channelLogos") or {}
-    logo_url      = await select_mainpage_logo_url(analysis_run_id)
-
-    llm_fb_pages = result.model_dump().get("facebook_audit", {}).get("pages", [])
-
-    snapshot: dict = _build_clinic_snapshot(gangnam_unni, mainpage, brand_assets, logo_url)
-    yt_patch: dict = await _build_youtube_audit(youtube)
-    ig_patch        = build_instagram_audit(instagram, instagram_en, channel_logos)
-    fb_patch        = build_facebook_audit(facebook, facebook_en, llm_fb_pages)
-    kpi_extras = {
-        "instagramEn": instagram_en,
-        "facebookEn":  facebook_en,
-        "tiktok":      tiktok,
-        "naverCafe":   naver_cafe,
-    }
-    kpi = build_kpi_dashboard(instagram, facebook, youtube, gangnam_unni, kpi_extras, naver_blog)
-
-    overrides: dict = {}
-    if snapshot:  overrides["clinic_snapshot"]  = snapshot
-    if ig_patch:  overrides["instagram_audit"]  = ig_patch
-    if fb_patch:  overrides["facebook_audit"]   = fb_patch
-    if yt_patch:  overrides["youtube_audit"]    = yt_patch
-    if kpi:       overrides["kpi_dashboard"]    = kpi
-
     merged = _deep_merge(result.model_dump(), overrides)
+    # 인스타 계정은 프롬프트에서 LLM이 []로 두게 했고, 코드가 수집 데이터로 채운다 (데이터 없으면 빈 리스트)
+    merged.setdefault("instagram_audit", {})["accounts"] = (overrides.get("instagram_audit") or {}).get("accounts") or []
     return ReportOutput(**merged)
 
 
 async def run_report_task(analysis_run_id: str) -> None:
     logger.info("[report] start run=%s", analysis_run_id)
-    await analyze_branding(analysis_run_id)
     result = await generate_report(analysis_run_id)
-    result = await _build_overrides(analysis_run_id, result)
+    result = _patch_report(result, await _build_overrides(analysis_run_id))
-    await update_run_report(analysis_run_id, result.model_dump())
+    await save_analysis_report(analysis_run_id, result.model_dump())
     logger.info("[report] done run=%s", analysis_run_id)
 
 
-def _patch_plan(result: PlanOutput, logo_desc: str) -> PlanOutput:
-    """brand_guide.channel_branding[].profile_photo 는 LLM 안 맡기고 코드가 박는다
-    (모든 채널 동일값 = brand_assets.logo_description). LLM 이 fallback 문구 hallucinate 방지."""
-    p = result.model_dump()
-    for ch in (p.get("brand_guide") or {}).get("channel_branding") or []:
-        ch["profile_photo"] = logo_desc
-    return PlanOutput(**p)
-
-
 async def run_plan_task(analysis_run_id: str) -> None:
     logger.info("[plan] start run=%s", analysis_run_id)
     result = await generate_plan(analysis_run_id)
-    # profile_photo 는 brand_assets.logo_description 으로 코드가 박음 (LLM "(가이드 미보유)" 같은 hallucination 차단).
+    await execute(
-    raw = await select_run_raw_data(analysis_run_id)
+        "UPDATE analysis_runs SET plan_data = %s WHERE analysis_run_id = %s",
-    branding = raw.get("branding") or {}
+        (json.dumps(result.model_dump(), ensure_ascii=False), analysis_run_id),
-    logo_desc = ((branding.get("brandAssets") or {}).get("logo_description")) or ""
+    )
-    result = _patch_plan(result, logo_desc)
-    await update_run_plan(analysis_run_id, result.model_dump())
     logger.info("[plan] done run=%s", analysis_run_id)

app/services/brand_parser.py

@@ -1,172 +0,0 @@
-"""collect 단계 - HTML/CSS 텍스트에서 brand 로고 URL + 색상 추출"""
-import logging
-import re
-from collections import Counter
-from urllib.parse import urljoin
-
-logger = logging.getLogger(__name__)
-
-
-# ── 로고 URL 추출 ─────────────────────────────────────────────────────────────
-
-
-LOGO_IMG_PATTERNS = [
-    re.compile(r'<img[^>]*\bclass=["\'][^"\']*\blogo\b[^"\']*["\'][^>]*\bsrc=["\']([^"\']+)["\']', re.IGNORECASE),
-    re.compile(r'<img[^>]*\bsrc=["\']([^"\']+)["\'][^>]*\bclass=["\'][^"\']*\blogo\b[^"\']*["\']', re.IGNORECASE),
-    re.compile(r'<img[^>]*\bid=["\'][^"\']*\blogo\b[^"\']*["\'][^>]*\bsrc=["\']([^"\']+)["\']', re.IGNORECASE),
-    re.compile(r'<img[^>]*\balt=["\'][^"\']*\blogo\b[^"\']*["\'][^>]*\bsrc=["\']([^"\']+)["\']', re.IGNORECASE),
-    re.compile(r'<(?:a|h[1-6]|div|span)[^>]*\b(?:class|id)=["\'][^"\']*\blogo\b[^"\']*["\'][^>]*>(?:[^<]|<(?!img))*<img[^>]*\bsrc=["\']([^"\']+)["\']', re.IGNORECASE | re.DOTALL),
-    re.compile(r'<(?:a|div|span|h[1-6])[^>]*\b(?:class|id)=["\'][^"\']*\blogo\b[^"\']*["\'][^>]*\bstyle=["\'][^"\']*background(?:-image)?\s*:\s*url\(\s*["\']?([^"\')\s]+)', re.IGNORECASE),
-    re.compile(r'<(?:a|div|span|h[1-6])[^>]*\bstyle=["\'][^"\']*background(?:-image)?\s*:\s*url\(\s*["\']?([^"\')\s]+)[^"\']*["\'][^>]*\b(?:class|id)=["\'][^"\']*\blogo\b', re.IGNORECASE),
-    re.compile(r'<img[^>]*\bsrc=["\']([^"\']*\blogo\b[^"\']*\.(?:png|svg|jpe?g|webp)[^"\']*)["\']', re.IGNORECASE),
-    re.compile(r'<header\b[^>]*>(?:[^<]|<(?!img))*<img[^>]*\bsrc=["\']([^"\']+\.(?:png|svg|jpe?g|webp)[^"\']*)["\']', re.IGNORECASE | re.DOTALL),
-    re.compile(r'<nav\b[^>]*>(?:[^<]|<(?!img))*<img[^>]*\bsrc=["\']([^"\']+\.(?:png|svg|jpe?g|webp)[^"\']*)["\']', re.IGNORECASE | re.DOTALL),
-    re.compile(r'<meta[^>]*\bproperty=["\']og:image["\'][^>]*\bcontent=["\']([^"\']+)["\']', re.IGNORECASE),
-    re.compile(r'<meta[^>]*\bcontent=["\']([^"\']+)["\'][^>]*\bproperty=["\']og:image["\']', re.IGNORECASE),
-]
-
-LOGO_CSS_PATTERN = re.compile(
-    r'\.[\w-]*\blogo\b[\w-]*\s*(?:,\s*\.[\w-]+\s*)*\{[^}]*background(?:-image)?\s*:\s*url\(\s*["\']?([^"\')\s]+)',
-    re.IGNORECASE | re.DOTALL,
-)
-
-
-def find_logo_url_in_html(html: str, base_url: str, css_texts: list[str] | None = None) -> str | None:
-    """HTML 에서 logo URL 찾기. 우선순위: 1) class/id/alt 명시 img 2) 외부 CSS .logo bg 3) header/nav 첫 img."""
-
-    def _is_noise(src: str) -> bool:
-        if not src or src.startswith("data:"):
-            return True
-        if re.search(r"(blank|spacer|pixel|transparent|1x1)\b", src, re.IGNORECASE):
-            return True
-        if re.search(r"(lang[-_]?(kor|eng|chn|jpn|rus|jp|en|ko|cn|ar|in)|flag|country|icon-|btn-|arrow|prev|next|search)\b", src, re.IGNORECASE):
-            return True
-        return False
-
-    for pat in LOGO_IMG_PATTERNS[:8]:
-        for m in pat.finditer(html):
-            src = m.group(1)
-            if _is_noise(src):
-                continue
-            return urljoin(base_url, src)
-
-    for css in (css_texts or []):
-        m = LOGO_CSS_PATTERN.search(css)
-        if m:
-            src = m.group(1)
-            if not _is_noise(src):
-                return urljoin(base_url, src)
-
-    for pat in LOGO_IMG_PATTERNS[8:]:
-        for m in pat.finditer(html):
-            src = m.group(1)
-            if _is_noise(src):
-                continue
-            return urljoin(base_url, src)
-
-    return None
-
-
-# ── 색상 추출 ────────────────────────────────────────────────────────────────
-
-
-HEX6 = re.compile(r"#([0-9a-fA-F]{6})\b")
-HEX3 = re.compile(r"#([0-9a-fA-F]{3})\b(?![0-9a-fA-F])")
-RGB  = re.compile(r"rgba?\(\s*(\d{1,3})\s*,\s*(\d{1,3})\s*,\s*(\d{1,3})\s*(?:,\s*[\d.]+\s*)?\)")
-STYLE_BLOCK = re.compile(r"<style[^>]*>(.*?)</style>", re.IGNORECASE | re.DOTALL)
-
-NOISE = {
-    "#ffffff", "#000000", "#fff", "#000",
-    "#333", "#222", "#111", "#444", "#555", "#666", "#777", "#888", "#999",
-    "#aaa", "#bbb", "#ccc", "#ddd", "#eee", "#f0f0f0", "#f5f5f5", "#fafafa",
-}
-
-
-def _normalize(hex_str: str) -> str:
-    h = hex_str.lstrip("#").lower()
-    if len(h) == 3:
-        h = "".join(c * 2 for c in h)
-    if len(h) == 8:
-        h = h[:6]
-    return f"#{h}"
-
-
-def _rgb_to_hex(r: int, g: int, b: int) -> str:
-    return f"#{r:02x}{g:02x}{b:02x}"
-
-
-def _hex_to_rgb(h: str) -> tuple[int, int, int]:
-    h = h.lstrip("#")
-    return int(h[0:2], 16), int(h[2:4], 16), int(h[4:6], 16)
-
-
-def _distance(a: str, b: str) -> float:
-    ar, ag, ab = _hex_to_rgb(a)
-    br, bg, bb = _hex_to_rgb(b)
-    return ((ar - br) ** 2 + (ag - bg) ** 2 + (ab - bb) ** 2) ** 0.5
-
-
-def _is_grayscale(h: str, tol: int = 12) -> bool:
-    r, g, b = _hex_to_rgb(h)
-    return max(r, g, b) - min(r, g, b) < tol
-
-
-def _extract_hex(text: str) -> list[str]:
-    out: list[str] = []
-    out.extend(_normalize(m.group(0)) for m in HEX6.finditer(text))
-    out.extend(_normalize(m.group(0)) for m in HEX3.finditer(text))
-    for m in RGB.finditer(text):
-        r, g, b = int(m.group(1)), int(m.group(2)), int(m.group(3))
-        if 0 <= r <= 255 and 0 <= g <= 255 and 0 <= b <= 255:
-            out.append(_rgb_to_hex(r, g, b))
-    return out
-
-
-def _cluster(colors: Counter, threshold: float = 25.0) -> list[tuple[str, int]]:
-    ranked = colors.most_common()
-    clusters: list[tuple[str, int]] = []
-    for color, count in ranked:
-        merged = False
-        for i, (rep, rep_count) in enumerate(clusters):
-            if _distance(color, rep) < threshold:
-                clusters[i] = (rep, rep_count + count)
-                merged = True
-                break
-        if not merged:
-            clusters.append((color, count))
-    return clusters
-
-
-def extract_brand_colors_from_text(html: str, css_texts: list[str], source_url: str = "") -> dict:
-    """HTML + CSS 텍스트에서 hex 빈도 분석 → primary/accent/text + palette. (fetch 없음)"""
-    all_text_chunks: list[str] = list(STYLE_BLOCK.findall(html))
-    all_text_chunks.append(html)
-    all_text_chunks.extend(css_texts)
-
-    counter: Counter = Counter()
-    for text in all_text_chunks:
-        for color in _extract_hex(text):
-            if color in NOISE:
-                continue
-            counter[color] += 1
-
-    if not counter:
-        logger.info("[brand_parser] no colors extracted from %s", source_url)
-        return {}
-
-    clustered = _cluster(counter)
-    chromatic = [c for c, _ in clustered if not _is_grayscale(c)]
-    grayscale = [c for c, _ in clustered if _is_grayscale(c)]
-
-    palette_top = clustered[:8]
-    palette = [{"name": f"색상 {i+1}", "hex": h, "usage": f"빈도 {n}"} for i, (h, n) in enumerate(palette_top)]
-
-    return {
-        "brand_colors": {
-            "primary": chromatic[0] if chromatic else None,
-            "accent": chromatic[1] if len(chromatic) > 1 else None,
-            "text": grayscale[0] if grayscale else None,
-        },
-        "color_palette": palette,
-        "extracted_from": "html+css",
-    }

app/services/branding.py

@@ -1,86 +0,0 @@
-"""report 단계 - Gemini Vision 으로 로고 묘사 + 채널 로고 매칭."""
-import logging
-import os
-from urllib.parse import urlparse
-from common.db.source import (
-    select_run_raw_data, update_raw_info_merge,
-    select_branding_info_id, select_mainpage_logo_url,
-)
-from common.utils import _run_optional_step
-from integrations.llm.gemini_vision import VisionClient
-
-logger = logging.getLogger(__name__)
-
-
-async def _describe_logo(analysis_run_id: str, info_id: int, vc: VisionClient) -> None:
-    """공식 로고 정성 묘사. branding raw_info["brandAssets"] 머지.
-    호출 우선순위: raw_info.logo_url 컬럼 (HTML parser canonical) → firecrawl 메타 fallback."""
-    raw = await select_run_raw_data(analysis_run_id)
-    mainpage = raw.get("mainpage") or {}
-    homepage_url = mainpage.get("sourceUrl") or ""
-    branding_meta = mainpage.get("branding") or {}
-    column_logo = await select_mainpage_logo_url(analysis_run_id)
-    candidates = [u for u in [
-        column_logo,
-        branding_meta.get("logoUrl"),
-        branding_meta.get("faviconUrl"),
-    ] if u]
-    if homepage_url:
-        parsed = urlparse(homepage_url)
-        if parsed.scheme and parsed.netloc:
-            candidates.append(f"{parsed.scheme}://{parsed.netloc}/favicon.ico")
-    if not candidates:
-        logger.info("[brand_logo] skip — no candidates")
-        return
-    logger.info("[brand_logo] start run=%s candidates=%d", analysis_run_id, len(candidates))
-    result: dict = {}
-    for cand in candidates:
-        result = await vc.analyze_brand_assets(logo_url=cand, homepage_url=homepage_url)
-        if result:
-            break
-    if result:
-        await update_raw_info_merge(info_id, {"brandAssets": result})
-    logger.info("[brand_logo] done keys=%s", list(result.keys()) if result else None)
-
-
-async def _describe_channel_logos(analysis_run_id: str, info_id: int, vc: VisionClient) -> None:
-    """채널 프로필 로고를 공식 로고와 비교. branding raw_info["channelLogos"] 머지."""
-    raw = await select_run_raw_data(analysis_run_id)
-    official = await select_mainpage_logo_url(analysis_run_id)
-    _label = {
-        "instagram":    "Instagram",
-        "facebook":     "Facebook",
-        "youtube":      "YouTube",
-        "instagram_en": "Instagram EN",
-        "facebook_en":  "Facebook EN",
-        "tiktok":       "TikTok",
-    }
-    logos = [{"channel": label, "url": img}
-             for key, label in _label.items()
-             if (img := (raw.get(key) or {}).get("_logo_url"))]
-    if not logos:
-        logger.info("[channel_logos] skip — no channel profileImages")
-        return
-    logger.info("[channel_logos] start run=%s channels=%s official=%s",
-                analysis_run_id, [l["channel"] for l in logos], bool(official))
-    result = await vc.describe_channel_logos(official, logos)
-    if result:
-        await update_raw_info_merge(info_id, {"channelLogos": result})
-    logger.info("[channel_logos] done keys=%s", list(result.keys()) if result else None)
-
-
-async def analyze_branding(analysis_run_id: str) -> None:
-    """report build 직전 호출 — 로고 묘사 + 채널 로고 매칭 (Gemini). 둘 다 격리."""
-    api_key = os.getenv("GEMINI_API_KEY")
-    if not api_key:
-        logger.info("[branding] skip — GEMINI_API_KEY 없음")
-        return
-    branding_info_id = await select_branding_info_id(analysis_run_id)
-    if branding_info_id is None:
-        logger.info("[branding] skip — branding source 없음 run=%s", analysis_run_id)
-        return
-    vc = VisionClient(api_key)
-    logger.info("[branding] start run=%s", analysis_run_id)
-    await _run_optional_step(_describe_logo(analysis_run_id, branding_info_id, vc), "brand_logo")
-    await _run_optional_step(_describe_channel_logos(analysis_run_id, branding_info_id, vc), "channel_logos")
-    logger.info("[branding] done run=%s", analysis_run_id)

app/services/collect.py

@@ -1,199 +1,115 @@
 import asyncio
 import logging
-from common.db.hospital import update_hospital_status, update_hospital
+from common.db import (
-from common.db.source import select_run_sources, update_raw_info_status, update_raw_info
+    fetchone,
+    set_instagram_status, save_instagram_raw_data,
+    set_facebook_status, save_facebook_raw_data,
+    set_naver_blog_status, save_naver_blog_raw_data,
+    set_youtube_status, save_youtube_raw_data,
+    set_gangnam_unni_status, save_gangnam_unni_raw_data,
+    execute, save_hospital_raw_data,
+)
 from common.utils import get_env, _run_optional_step
 from integrations.apify import ApifyClient
 from integrations.naver import NaverClient
 from integrations.youtube import YouTubeClient
 from integrations.firecrawl import FirecrawlClient
-from models.status import SourceType
+from services.enrichment import collect_brand_assets, collect_extra_channels, collect_channel_logos
-from integrations.site_fetcher import fetch_html_and_css
-from services.brand_parser import find_logo_url_in_html, extract_brand_colors_from_text
-from common.db.source import update_raw_info_merge, update_raw_info_logo_url, select_run_raw_data
-from common.db.base import fetchone
-from services.facebook_audit import transform_for_storage as transform_facebook
 
 logger = logging.getLogger(__name__)
 
 
-async def _save_with_logo(info_id: int, data: dict) -> None:
+async def collect_instagram(analysis_run_id: str, row_id: int, url: str) -> None:
-    await update_raw_info(info_id, data)
-    if data.get("profileImage"):
-        await update_raw_info_logo_url(info_id, data["profileImage"])
-
-
-async def collect_instagram(analysis_run_id: str, info_id: int, url: str) -> None:
     logger.info("[instagram] start run=%s url=%s", analysis_run_id, url)
-    await update_raw_info_status(info_id, "processing")
+    await set_instagram_status(row_id, "processing")
     data = await ApifyClient(get_env("APIFY_API_TOKEN")).get_instagram_profile(url)
-    if data is None:
+    await save_instagram_raw_data(row_id, data)
-        await update_raw_info_status(info_id, "failed")
-        logger.warning("[instagram] failed run=%s", analysis_run_id)
-        return
-    await _save_with_logo(info_id, data)
     logger.info("[instagram] done run=%s", analysis_run_id)
 
 
-async def collect_facebook(analysis_run_id: str, info_id: int, url: str) -> None:
+async def collect_facebook(analysis_run_id: str, row_id: int, url: str) -> None:
     logger.info("[facebook] start run=%s url=%s", analysis_run_id, url)
-    await update_raw_info_status(info_id, "processing")
+    await set_facebook_status(row_id, "processing")
     data = await ApifyClient(get_env("APIFY_API_TOKEN")).get_facebook_page(url)
-    if data is None:
+    await save_facebook_raw_data(row_id, data)
-        await update_raw_info_status(info_id, "failed")
-        logger.warning("[facebook] failed run=%s", analysis_run_id)
-        return
-    data = transform_facebook(data)
-    await _save_with_logo(info_id, data)
     logger.info("[facebook] done run=%s", analysis_run_id)
 
 
-async def collect_naver_blog(analysis_run_id: str, info_id: int, url: str) -> None:
+async def collect_naver_blog(analysis_run_id: str, row_id: int, url: str) -> None:
     logger.info("[naver_blog] start run=%s url=%s", analysis_run_id, url)
-    await update_raw_info_status(info_id, "processing")
+    await set_naver_blog_status(row_id, "processing")
     data = await NaverClient(get_env("NAVER_CLIENT_ID"), get_env("NAVER_CLIENT_SECRET")).get_blog_rss(url)
-    if data is None:
+    await save_naver_blog_raw_data(row_id, data)
-        await update_raw_info_status(info_id, "failed")
-        logger.warning("[naver_blog] failed run=%s", analysis_run_id)
-        return
-    await update_raw_info(info_id, data)
     logger.info("[naver_blog] done run=%s", analysis_run_id)
 
 
-async def collect_youtube(analysis_run_id: str, info_id: int, url: str) -> None:
+async def collect_youtube(analysis_run_id: str, row_id: int, url: str) -> None:
     logger.info("[youtube] start run=%s url=%s", analysis_run_id, url)
-    await update_raw_info_status(info_id, "processing")
+    await set_youtube_status(row_id, "processing")
     data = await YouTubeClient(get_env("YOUTUBE_API_KEY")).get_channel(url)
-    if data is None:
+    await save_youtube_raw_data(row_id, data)
-        await update_raw_info_status(info_id, "failed")
-        logger.warning("[youtube] failed run=%s", analysis_run_id)
-        return
-    await _save_with_logo(info_id, data)
     logger.info("[youtube] done run=%s", analysis_run_id)
 
 
-async def collect_gangnam_unni(analysis_run_id: str, info_id: int, url: str) -> None:
+async def collect_gangnam_unni(analysis_run_id: str, row_id: int, url: str) -> None:
     logger.info("[gangnam_unni] start run=%s url=%s", analysis_run_id, url)
-    await update_raw_info_status(info_id, "processing")
+    await set_gangnam_unni_status(row_id, "processing")
     data = await FirecrawlClient(get_env("FIRECRAWL_API_KEY")).get_gangnam_unni(url)
-    if data is None:
+    await save_gangnam_unni_raw_data(row_id, data)
-        await update_raw_info_status(info_id, "failed")
-        logger.warning("[gangnam_unni] failed run=%s", analysis_run_id)
-        return
-    await update_raw_info(info_id, data)
     logger.info("[gangnam_unni] done run=%s", analysis_run_id)
 
 
-async def collect_mainpage(analysis_run_id: str, info_id: int, hospital_id: str, url: str) -> None:
+async def collect_clinic_info(analysis_run_id: str, hospital_id: str, url: str) -> None:
-    logger.info("[mainpage] start run=%s url=%s", analysis_run_id, url)
+    logger.info("[clinic] start run=%s url=%s", analysis_run_id, url)
-    await update_raw_info_status(info_id, "processing")
+    await execute("UPDATE hospital_baseinfo SET status = 'processing' WHERE hospital_id = %s", (hospital_id,))
-    await update_hospital_status(hospital_id, "processing")
     data = await FirecrawlClient(get_env("FIRECRAWL_API_KEY")).fetch_clinic_info(url)
-    if data is None:
+    await save_hospital_raw_data(hospital_id, data, analysis_run_id=analysis_run_id)
-        await update_raw_info_status(info_id, "failed")
+    logger.info("[clinic] done run=%s", analysis_run_id)
-        logger.warning("[mainpage] failed run=%s", analysis_run_id)
-        return
-    # 홈페이지 URL 자체도 raw_data 에 박아둬야 brand_assets / 분석 단계에서 mainpage URL 재조회 없이 사용 가능.
-    data = {**data, "sourceUrl": url}
-    await update_raw_info(info_id, data)
-    await update_hospital(hospital_id, data, analysis_run_id=analysis_run_id)
-    logger.info("[mainpage] done run=%s", analysis_run_id)
 
 
-async def collect_tiktok(analysis_run_id: str, info_id: int, url: str) -> None:
+async def collect_all(
-    logger.info("[tiktok] start run=%s url=%s", analysis_run_id, url)
+    analysis_run_id: str,
-    await update_raw_info_status(info_id, "processing")
+    hospital_id: str,
-    data = await ApifyClient(get_env("APIFY_API_TOKEN")).get_tiktok_profile(url)
+    instagram_id: int | None = None,
-    if data is None:
+    facebook_id: int | None = None,
-        await update_raw_info_status(info_id, "failed")
+    naver_blog_id: int | None = None,
-        logger.warning("[tiktok] failed run=%s", analysis_run_id)
+    youtube_id: int | None = None,
-        return
+    gangnam_unni_id: int | None = None,
-    await _save_with_logo(info_id, data)
+    tiktok_url: str | None = None,
-    logger.info("[tiktok] done run=%s", analysis_run_id)
+    instagram_en_url: str | None = None,
+    facebook_en_url: str | None = None,
+) -> None:
+    async def _url(table: str, row_id: int) -> str:
+        row = await fetchone(f"SELECT url FROM {table} WHERE id = %s", (row_id,))
+        return row["url"] if row else ""
 
+    hospital = await fetchone("SELECT url FROM hospital_baseinfo WHERE hospital_id = %s", (hospital_id,))
+    tasks = [collect_clinic_info(analysis_run_id, hospital_id, hospital["url"])]
 
-async def collect_naver_cafe(analysis_run_id: str, info_id: int, url: str) -> None:
+    if instagram_id:
-    """카페는 로그인 필요라 본문 못 봄. URL 활성·cafeId·이름 언급수만 신호로 수집."""
+        tasks.append(collect_instagram(analysis_run_id, instagram_id, await _url("instagram_data", instagram_id)))
-    logger.info("[naver_cafe] start run=%s url=%s", analysis_run_id, url)
+    if facebook_id:
-    await update_raw_info_status(info_id, "processing")
+        tasks.append(collect_facebook(analysis_run_id, facebook_id, await _url("facebook_data", facebook_id)))
-    data = await NaverClient(get_env("NAVER_CLIENT_ID"), get_env("NAVER_CLIENT_SECRET")).get_cafe_info(url)
+    if naver_blog_id:
-    if data is None:
+        tasks.append(collect_naver_blog(analysis_run_id, naver_blog_id, await _url("naver_blog_data", naver_blog_id)))
-        await update_raw_info_status(info_id, "failed")
+    if youtube_id:
-        logger.warning("[naver_cafe] failed run=%s", analysis_run_id)
+        tasks.append(collect_youtube(analysis_run_id, youtube_id, await _url("youtube_data", youtube_id)))
-        return
+    if gangnam_unni_id:
-    await update_raw_info(info_id, data)
+        tasks.append(collect_gangnam_unni(analysis_run_id, gangnam_unni_id, await _url("gangnam_unni_data", gangnam_unni_id)))
-    logger.info("[naver_cafe] done run=%s", analysis_run_id)
-
-
-async def collect_kakaotalk(analysis_run_id: str, info_id: int, url: str) -> None:
-    """카카오톡은 수집 X — URL 보관만. LLM이 채널 존재 신호로만 사용."""
-    logger.info("[kakaotalk] url-only run=%s url=%s", analysis_run_id, url)
-    await update_raw_info(info_id, {"url": url})
-
-
-async def collect_brand_basics(analysis_run_id: str, info_id: int) -> None:
-    logger.info("[brand_basics] start run=%s info=%s", analysis_run_id, info_id)
-    raw = await select_run_raw_data(analysis_run_id)
-    mainpage = raw.get("mainpage") or {}
-    homepage_url = mainpage.get("sourceUrl") or ""
-    branding_meta = mainpage.get("branding") or {}
-
-    html, css_texts = await fetch_html_and_css(homepage_url) if homepage_url else ("", [])
-    html_logo_url = find_logo_url_in_html(html, homepage_url, css_texts) if html else None
-    css_colors    = extract_brand_colors_from_text(html, css_texts, homepage_url) if html else {}
-
-    logo_url = html_logo_url or branding_meta.get("logoUrl") or branding_meta.get("ogImage")
-    if logo_url:
-        mainpage_row = await fetchone(
-            "SELECT ri.info_id FROM raw_info ri JOIN remote_source rs USING (source_id)"
-            " WHERE ri.analysis_run_id = %s AND rs.source_type = 'mainpage' LIMIT 1",
-            (analysis_run_id,),
-        )
-        if mainpage_row:
-            await update_raw_info_logo_url(mainpage_row["info_id"], logo_url)
-
-    payload: dict = {}
-    if css_colors:
-        if css_colors.get("brand_colors"):  payload["brand_colors"]  = css_colors["brand_colors"]
-        if css_colors.get("color_palette"): payload["color_palette"] = css_colors["color_palette"]
-        payload["color_source"] = "html+css"
-    if payload:
-        await update_raw_info_merge(info_id, {"brandAssets": payload})
-    logger.info("[brand_basics] done logo_url=%s colors=%s", bool(logo_url), bool(payload))
-
-
-async def collect_all(analysis_run_id: str, hospital_id: str) -> None:
-    rows = await select_run_sources(analysis_run_id)
-
-    # source_type → collector. KR/EN 구분은 collector 입장에서 동일, language 컬럼만 다름.
-    _collectors = {
-        SourceType.INSTAGRAM:    collect_instagram,
-        SourceType.FACEBOOK:     collect_facebook,
-        SourceType.NAVER_BLOG:   collect_naver_blog,
-        SourceType.YOUTUBE:      collect_youtube,
-        SourceType.GANGNAM_UNNI: collect_gangnam_unni,
-        SourceType.TIKTOK:       collect_tiktok,
-        SourceType.NAVER_CAFE:   collect_naver_cafe,
-        SourceType.KAKAOTALK:    collect_kakaotalk,
-    }
-
-    tasks = []
-    branding_info_id: int | None = None
-    for row in rows:
-        info_id     = row["info_id"]
-        source_type = row["source_type"]
-        url         = row["url"]
-        if source_type == SourceType.BRANDING:
-            branding_info_id = info_id  # mainpage·채널 수집 끝난 뒤 2단계에서 사용
-            continue
-        if source_type == SourceType.MAINPAGE:
-            tasks.append(collect_mainpage(analysis_run_id, info_id, hospital_id, url))
-        elif source_type in _collectors:
-            tasks.append(_collectors[source_type](analysis_run_id, info_id, url))
 
     await asyncio.gather(*tasks, return_exceptions=True)
 
-    # 2단계: branding (brandAssets → channelLogos 한 raw_info 안에 머지). mainpage·채널 raw_data 의존이라 순차.
+    # 아래 3단계는 모두 hospital raw_data를 read-modify-write 하므로 race 방지 위해 순차.
-    # 부가 기능이라 실패해도 리포트는 나와야 하므로 _run_optional_step 으로 격리.
+    #   brand_assets  : clinic_info가 채운 branding.logoUrl로 공식 로고/hex 추출
-    if branding_info_id is not None:
+    #   extra_channels: 틱톡/인스타EN/페북EN 수집
-        await _run_optional_step(collect_brand_basics(analysis_run_id, branding_info_id), "brand_basics")
+    #   channel_logos : 공식 로고(brand_assets)+채널 profileImage(extra_channels) 채워진 뒤 Vision 비교
+    # 부가 기능이라 실패해도 리포트는 나와야 하므로 _run_optional_step으로 각각 격리.
+    await _run_optional_step(collect_brand_assets(analysis_run_id, hospital_id), "brand_assets")
+    await _run_optional_step(
+        collect_extra_channels(
+            analysis_run_id, hospital_id,
+            tiktok_url=tiktok_url, instagram_en_url=instagram_en_url, facebook_en_url=facebook_en_url,
+        ),
+        "extra_channels",
+    )
+    await _run_optional_step(collect_channel_logos(analysis_run_id, hospital_id), "channel_logos")

app/services/enrichment.py

@@ -0,0 +1,175 @@
+import asyncio
+import json
+import logging
+import os
+from urllib.parse import urlparse
+from common.db import fetchone, fetch_raw, merge_hospital_raw_data
+from common.utils import get_env
+from integrations.apify import ApifyClient
+from integrations.vision import VisionClient
+from integrations.color_extractor import extract_brand_assets_from_site
+
+logger = logging.getLogger(__name__)
+
+
+async def collect_brand_assets(analysis_run_id: str, hospital_id: str) -> None:
+    """홈페이지에서 로고 URL + brand hex 색상을 뽑아 raw_data["brandAssets"]에 저장.
+    - 로고 URL/hex: HTML·CSS 정규식 (color_extractor) — Vision 의존 X, 사이트 전체 컬러 시스템이 더 정확.
+    - 로고 정성 묘사(심볼/워드마크/톤): Gemini Vision (GEMINI_API_KEY 없으면 색상만 저장하고 skip).
+    """
+    logger.info("[brand_assets] start run=%s", analysis_run_id)
+    row = await fetchone(
+        "SELECT raw_data, url FROM hospital_baseinfo WHERE hospital_id = %s",
+        (hospital_id,),
+    )
+    if not row:
+        return
+    raw = row["raw_data"]
+    raw_data = json.loads(raw) if isinstance(raw, str) else (raw or {})
+    branding = raw_data.get("branding") or {}
+    homepage_url = row["url"]
+
+    # 0~1. 사이트 1회 fetch로 logo URL + brand hex 동시 추출 (img/background-image/CSS .logo, Vision 의존 X)
+    site = await extract_brand_assets_from_site(homepage_url) if homepage_url else {}
+    html_logo_url = site.get("logo_url")
+    css_colors = site.get("colors") or {}
+    if html_logo_url:
+        logger.info("[brand_assets] HTML logo found: %s", html_logo_url)
+    if css_colors:
+        logger.info("[brand_assets] css colors: %s", css_colors.get("brand_colors"))
+
+    # 2. 로고/대표 이미지 후보 (logo → og:image → favicon 순)
+    logo_url = html_logo_url or branding.get("logoUrl")
+    og_image = branding.get("ogImage")
+    favicon = branding.get("faviconUrl")
+    candidates: list[tuple[str, str]] = []
+    if logo_url: candidates.append(("logo", logo_url))
+    if og_image: candidates.append(("og", og_image))
+    if favicon:  candidates.append(("favicon", favicon))
+    if homepage_url:
+        parsed = urlparse(homepage_url)
+        if parsed.scheme and parsed.netloc:
+            candidates.append(("favicon", f"{parsed.scheme}://{parsed.netloc}/favicon.ico"))
+
+    if not candidates and not css_colors:
+        logger.info("[brand_assets] skip — no logo/og/favicon candidates and no CSS colors")
+        return
+
+    # 3. Vision은 로고 정성 묘사만 (hex는 CSS 추출이 더 정확). 키 없으면 색상만 저장.
+    result: dict = {}
+    used_kind: str | None = None
+    api_key = os.getenv("GEMINI_API_KEY")
+    if api_key and candidates:
+        vc = VisionClient(api_key)
+        for kind, cand in candidates:
+            result = await vc.analyze_brand_assets(logo_url=cand, homepage_url=homepage_url)
+            if result:
+                used_kind = kind
+                break
+        # favicon으로만 분석된 경우 진짜 로고가 아니므로 logo URL은 박지 않음 (묘사는 OK)
+        if result and used_kind == "favicon" and result.get("logo_images"):
+            result["logo_images"] = {"circle": None, "horizontal": None, "korean": None}
+    elif not api_key:
+        logger.info("[brand_assets] GEMINI_API_KEY not set — 색상만 저장, Vision 묘사 skip")
+
+    # 4. CSS에서 추출한 brand_colors/palette를 Vision보다 우선 사용
+    if css_colors:
+        if css_colors.get("brand_colors"):
+            result["brand_colors"] = css_colors["brand_colors"]
+        if css_colors.get("color_palette"):
+            result["color_palette"] = css_colors["color_palette"]
+        result["color_source"] = "html+css"
+    elif result:
+        result["color_source"] = "vision"
+
+    if result:
+        result["logo_source"] = used_kind or "none"
+        await merge_hospital_raw_data(hospital_id, {"brandAssets": result})
+    logger.info("[brand_assets] done keys=%s", list(result.keys()) if result else None)
+
+
+async def collect_extra_channels(
+    analysis_run_id: str,
+    hospital_id: str,
+    tiktok_url: str | None = None,
+    instagram_en_url: str | None = None,
+    facebook_en_url: str | None = None,
+) -> None:
+    """틱톡 / 인스타 EN / 페북 EN 수집 → hospital raw_data에 저장 (별도 테이블 없이).
+    인스타EN·페북EN은 기존 Apify 수집기 재사용, 틱톡은 신규 액터."""
+    apify = ApifyClient(get_env("APIFY_API_TOKEN"))
+    jobs: dict = {}
+    if instagram_en_url:
+        jobs["instagramEn"] = apify.get_instagram_profile(instagram_en_url)
+    if facebook_en_url:
+        jobs["facebookEn"] = apify.get_facebook_page(facebook_en_url)
+    if tiktok_url:
+        jobs["tiktok"] = apify.get_tiktok_profile(tiktok_url)
+    if not jobs:
+        return
+
+    logger.info("[extra_channels] start run=%s channels=%s", analysis_run_id, list(jobs))
+    done = await asyncio.gather(*jobs.values(), return_exceptions=True)
+    results: dict = {}
+    for key, res in zip(jobs.keys(), done):
+        if isinstance(res, Exception):
+            logger.warning("[extra_channels] %s 수집 실패: %s", key, res)
+        elif res:
+            results[key] = res
+    if not results:
+        logger.info("[extra_channels] 수집 결과 없음 run=%s", analysis_run_id)
+        return
+
+    await merge_hospital_raw_data(hospital_id, results)
+    logger.info("[extra_channels] done run=%s keys=%s", analysis_run_id, list(results))
+
+
+async def collect_channel_logos(analysis_run_id: str, hospital_id: str) -> None:
+    """채널별 프로필 이미지(로고)를 모아 Gemini Vision으로 설명 + 공식 로고 일치 여부 평가.
+    → hospital raw_data["channelLogos"]에 저장. GEMINI_API_KEY 없으면 skip.
+    brand_assets(공식 로고)·extra_channels(틱톡/EN profileImage) 다음에 실행돼야 함."""
+    api_key = os.getenv("GEMINI_API_KEY")
+    if not api_key:
+        logger.info("[channel_logos] skip — GEMINI_API_KEY 없음")
+        return
+
+    hrow = await fetchone("SELECT raw_data FROM hospital_baseinfo WHERE hospital_id = %s", (hospital_id,))
+    raw = hrow["raw_data"] if hrow else None
+    raw_data = json.loads(raw) if isinstance(raw, str) else (raw or {})
+    official = ((raw_data.get("brandAssets") or {}).get("logo_images") or {}).get("horizontal")
+
+    run = await fetchone(
+        "SELECT instagram_data_id, facebook_data_id, youtube_data_id"
+        " FROM analysis_runs WHERE analysis_run_id = %s",
+        (analysis_run_id,),
+    )
+    logos: list[dict] = []
+    # 전용 테이블 채널 (KR)
+    for ch, table, col in [
+        ("Instagram", "instagram_data", "instagram_data_id"),
+        ("Facebook", "facebook_data", "facebook_data_id"),
+        ("YouTube", "youtube_data", "youtube_data_id"),
+    ]:
+        rid = (run or {}).get(col)
+        if rid:
+            d = await fetch_raw(table, rid) or {}
+            if d.get("profileImage"):
+                logos.append({"channel": ch, "url": d["profileImage"]})
+    # 추가 채널 (hospital raw_data)
+    for ch, key in [("Instagram EN", "instagramEn"), ("Facebook EN", "facebookEn"), ("TikTok", "tiktok")]:
+        img = (raw_data.get(key) or {}).get("profileImage")
+        if img:
+            logos.append({"channel": ch, "url": img})
+
+    if not logos:
+        logger.info("[channel_logos] skip — 채널 프로필 이미지 없음")
+        return
+
+    logger.info("[channel_logos] start run=%s channels=%s official=%s", analysis_run_id,
+                [l["channel"] for l in logos], bool(official))
+    result = await VisionClient(api_key).describe_channel_logos(official, logos)
+    if result:
+        # Vision이 못 본 채널도 url은 채워둠 (프론트에서 이미지 표시용)
+        result["logos"] = logos
+        await merge_hospital_raw_data(hospital_id, {"channelLogos": result})
+    logger.info("[channel_logos] done run=%s keys=%s", analysis_run_id, list(result.keys()) if result else None)

app/services/facebook_audit.py

@@ -1,106 +0,0 @@
-"""Facebook audit 페이지(KR·EN)를 수집 데이터로 구성.
-수치 지표(최근 게시일·게시 빈도·참여율)는 **수집 시점에** 결정적으로 산출해 DB에 박는다 (transform_for_storage).
-콘텐츠 주제(top_content_type)는 캡션 본문 이해가 필요해 LLM이 채운다 (리포트 프롬프트 지시)."""
-
-from datetime import datetime, timezone
-
-from common.utils import parse_ts
-from integrations.llm.schemas.report import FacebookAudit
-
-
-def _humanize_age(days: int) -> str:
-    days = max(days, 0)
-    if days == 0:  return "오늘"
-    if days < 7:   return f"{days}일 전"
-    if days < 30:  return f"{days // 7}주 전"
-    if days < 365: return f"{days // 30}개월 전"
-    return f"{days // 365}년 전"
-
-
-def _frequency_label(avg_gap_days: float) -> str:
-    """게시물 사이 평균 간격(일) → 빈도 라벨."""
-    if avg_gap_days <= 1.5: return "거의 매일"
-    if avg_gap_days <= 10:  return f"주 {7 / avg_gap_days:.1f}회"
-    if avg_gap_days <= 45:  return f"월 {30 / avg_gap_days:.1f}회"
-    return "비정기 (분기 이상 간격)"
-
-
-def _engagement_text(posts: list[dict]) -> str:
-    """게시물당 좋아요/반응/공유/조회를 min~max 범위로. 전부 0인 지표는 제외.
-    댓글은 posts actor가 안 줘서 '댓글 거의 없음' 고정 부가 (FB 페이지는 댓글 희박이 일반적)."""
-    def _rng(vals: list[int], label: str, unit: str) -> str | None:
-        lo, hi = min(vals), max(vals)
-        if hi == 0:
-            return None
-        return f"{label} {lo}{unit}" if lo == hi else f"{label} {lo}~{hi}{unit}"
-
-    parts = [
-        _rng([p.get("likes", 0)     for p in posts], "좋아요", "개"),
-        _rng([p.get("reactions", 0) for p in posts], "반응",   "개"),
-        _rng([p.get("shares", 0)    for p in posts], "공유",   "개"),
-    ]
-    vid_views = [p.get("views", 0) for p in posts if p.get("isVideo")]
-    if vid_views:
-        parts.append(_rng(vid_views, "영상 조회", "회"))
-    parts = [x for x in parts if x]
-    if not parts:
-        return "게시물당 참여 거의 없음"
-    return "게시물당 " + " · ".join(parts) + " · 댓글 거의 없음"
-
-
-def transform_for_storage(fb: dict | None) -> dict | None:
-    """apify 원본 → DB에 저장할 최종 형태.
-    - 수치 지표(recent_post_age·post_frequency·engagement)를 그 자리에서 계산해 박음.
-    - 게시물은 캡션·타입만 남김 (raw 숫자/timestamp는 어차피 재계산 안 하므로 버림).
-    수집 시점에 한 번 계산 → 리포트 생성 때는 그대로 갖다 박기만 함."""
-    if not isinstance(fb, dict):
-        return fb
-    posts = fb.get("latestPosts") or []
-    out = {k: v for k, v in fb.items() if k != "latestPosts"}
-    if posts:
-        dts = sorted((d for d in (parse_ts(p.get("timestamp")) for p in posts) if d), reverse=True)
-        if dts:
-            out["recent_post_age"] = _humanize_age((datetime.now(timezone.utc) - dts[0]).days)
-            if len(dts) > 1:
-                avg_gap = ((dts[0] - dts[-1]).days or 1) / (len(dts) - 1)
-                out["post_frequency"] = _frequency_label(avg_gap)
-        out["engagement"] = _engagement_text(posts)
-        out["latestPosts"] = [
-            {"caption": (p.get("text") or "")[:160],
-             "type": "video" if p.get("isVideo") else "image"}
-            for p in posts
-        ]
-    else:
-        out["latestPosts"] = []
-    return out
-
-
-def _page_patch(fb: dict, language: str, label: str) -> dict:
-    """저장된 페북 페이지 → FacebookPage 스키마 필드 패치. 수치 지표는 수집 시점에 박혀있어 그대로 복사.
-    language/label 은 데이터 있을 때만 명시적으로 박음 — template-copy 가 KR 값을 EN 슬롯에 잘못 상속시키는 것 방지."""
-    p: dict = {}
-    if fb.get("pageUrl"):           p["url"] = p["link"] = fb["pageUrl"]
-    if fb.get("pageName"):          p["page_name"]      = fb["pageName"]
-    if fb.get("followers"):         p["followers"]      = fb["followers"]
-    if fb.get("intro"):             p["bio"]            = fb["intro"]
-    if fb.get("categories"):        p["category"]       = ", ".join(fb["categories"])
-    if fb.get("website"):           p["linked_domain"]  = fb["website"]
-    if fb.get("reviews") is not None:   p["reviews"]    = fb["reviews"]
-    if fb.get("following") is not None: p["following"]  = fb["following"]
-    for key in ("recent_post_age", "post_frequency", "engagement"):
-        if fb.get(key):             p[key]              = fb[key]
-    if p:
-        p["language"] = language
-        p["label"]    = label
-    return p
-
-
-def build_facebook_audit(facebook: dict, facebook_en: dict, llm_pages: list[dict] | None = None) -> dict:
-    """KR·EN 페북 페이지 구성. logo/logo_description 은 LLM Vision 결과(첫 페이지) 모든 페이지에 공통 적용,
-    나머지 필드는 코드가 수집 데이터로 계산."""
-    llm_logo = {k: v for k, v in ((llm_pages or [{}])[0]).items() if k in {"logo", "logo_description"} and v}
-    pages = [{**llm_logo, **p} for p in (
-        _page_patch(facebook,    "KR", "페이스북 KR"),
-        _page_patch(facebook_en, "EN", "페이스북 EN"),
-    ) if p]
-    return FacebookAudit.model_validate({"pages": pages}).model_dump(exclude_unset=True)

app/services/file.py

@@ -2,8 +2,7 @@ import logging
 
 from fastapi import HTTPException, UploadFile
 
-from common.db.run import select_run
+from common.db import execute, fetchall, fetchone, insert_file_row
-from common.db.file_data import insert_file, select_run_files, select_file, delete_file
 from integrations.azure_blob import AzureBlobUploader
 from models.file import FileListItem, FileType, FileUploadResponse
 
@@ -32,7 +31,10 @@ async def upload_analysis_file(
     content_type: str | None = None,
 ) -> tuple[int, str]:
     """analysis_run에 딸린 파일 업로드. Blob 업로드 + file_data row 생성. (file_id, url) 반환."""
-    run = await select_run(analysis_run_id)
+    run = await fetchone(
+        "SELECT hospital_id FROM analysis_runs WHERE analysis_run_id = %s",
+        (analysis_run_id,),
+    )
     if not run:
         raise HTTPException(status_code=404, detail="analysis_run not found")
     hospital_id = run["hospital_id"]
@@ -45,7 +47,7 @@ async def upload_analysis_file(
         content_type=content_type,
     )
 
-    file_id = await insert_file(
+    file_id = await insert_file_row(
         analysis_run_id=analysis_run_id,
         hospital_id=hospital_id,
         file_type=file_type,
@@ -59,7 +61,12 @@ async def upload_analysis_file(
 
 async def list_analysis_files(analysis_run_id: str) -> list[dict]:
     """analysis_run에 딸린 (삭제 안 된) 파일 목록."""
-    return await select_run_files(analysis_run_id)
+    return await fetchall(
+        "SELECT id, file_type, file_name, file_url, size_bytes, created_at FROM file_data"
+        " WHERE analysis_run_id = %s AND is_deleted = FALSE"
+        " ORDER BY created_at DESC",
+        (analysis_run_id,),
+    )
 
 
 async def handle_analysis_file_upload(
@@ -95,7 +102,7 @@ async def handle_analysis_file_upload(
 
 async def get_analysis_files_response(analysis_run_id: str) -> list[FileListItem]:
     """run 존재 확인 + 응답 모델 생성."""
-    if not await select_run(analysis_run_id):
+    if not await fetchone("SELECT 1 FROM analysis_runs WHERE analysis_run_id = %s", (analysis_run_id,)):
         raise HTTPException(status_code=404, detail="analysis_run not found")
     rows = await list_analysis_files(analysis_run_id)
     return [FileListItem(**{**r, "created_at": str(r["created_at"])}) for r in rows]
@@ -103,8 +110,14 @@ async def get_analysis_files_response(analysis_run_id: str) -> list[FileListItem
 
 async def soft_delete_analysis_file(analysis_run_id: str, file_id: int) -> None:
     """analysis_run에 딸린 파일을 소프트 삭제. 멱등성 보장."""
-    row = await select_file(file_id, analysis_run_id)
+    row = await fetchone(
+        "SELECT id FROM file_data WHERE id = %s AND analysis_run_id = %s",
+        (file_id, analysis_run_id),
+    )
     if not row:
         raise HTTPException(status_code=404, detail="file not found")
-    await delete_file(file_id)
+    await execute(
+        "UPDATE file_data SET is_deleted = TRUE WHERE id = %s AND is_deleted = FALSE",
+        (file_id,),
+    )
     logger.info("soft-deleted analysis file run=%s file_id=%s", analysis_run_id, file_id)

app/services/instagram_audit.py

@@ -1,8 +1,6 @@
 """Instagram audit 계정(KR·EN)을 수집 데이터로 구성.
 fix 값(handle/followers/highlights/content_format 등)은 전부 코드에서 박는다 — LLM 출력 무시."""
 
-from integrations.llm.schemas.report import InstagramAudit
-
 _MEDIA = {"GraphImage": "이미지", "GraphSidecar": "카드뉴스", "GraphVideo": "영상/릴스"}
 
 
@@ -40,11 +38,11 @@ def _account(data: dict, language: str, label: str, channel: str, channel_logos:
     }
 
 
-def build_instagram_audit(instagram: dict, instagram_en: dict, channel_logos: dict) -> dict:
+def build_instagram_accounts(instagram: dict, instagram_en: dict, channel_logos: dict) -> list[dict]:
     """KR·EN 인스타 계정 리스트 구성 (username 있는 것만)."""
     accounts: list[dict] = []
     if instagram.get("username"):
         accounts.append(_account(instagram, "KR", "인스타그램 KR", "Instagram", channel_logos))
     if instagram_en.get("username"):
         accounts.append(_account(instagram_en, "EN", "인스타그램 EN", "Instagram EN", channel_logos))
-    return InstagramAudit.model_validate({"accounts": accounts}).model_dump()
+    return accounts

app/services/kpi_dashboard.py

@@ -1,96 +0,0 @@
-"""mockup 7개 역분석 — 채널 규모별 3개월/12개월 target 성장률 공식."""
-
-from integrations.llm.schemas.report import KPIMetric
-
-
-def _round_clean(n: int) -> int:
-    if n < 100:        return n
-    if n < 1000:       return round(n / 100) * 100
-    if n < 10_000:     return round(n / 500) * 500
-    if n < 100_000:    return round(n / 1000) * 1000
-    if n < 1_000_000:  return round(n / 5000) * 5000
-    return round(n / 50_000) * 50_000
-
-
-def _target_multiplier(current: int) -> tuple[float, float]:
-    if current < 1_000:     return (2.5, 9.0)
-    if current < 5_000:     return (1.7, 4.0)
-    if current < 25_000:    return (1.5, 2.5)
-    if current < 50_000:    return (1.3, 2.2)
-    return (1.1, 1.9)
-
-
-def _follower_kpi(metric: str, val: int | None, unit: str = "명") -> dict | None:
-    if not val: return None
-    m3, m12 = _target_multiplier(val)
-    return {
-        "metric":          metric,
-        "current":         f"{val:,}{unit}",
-        "target_3_month":  f"{_round_clean(int(val * m3)):,}{unit}",
-        "target_12_month": f"{_round_clean(int(val * m12)):,}{unit}",
-    }
-
-
-def _blog_frequency(posts: list) -> tuple[str, str, str] | None:
-    """RSS posts timestamp로 (current, target_3m, target_12m) 라벨 반환. target은 절대 downgrade 안 함."""
-    from common.utils import parse_ts
-    dts = sorted((d for d in (parse_ts(p.get("postDate")) for p in posts) if d), reverse=True)
-    if len(dts) < 2: return None
-    avg_gap = (dts[0] - dts[-1]).days / (len(dts) - 1)
-    if avg_gap > 90:    current = f"방치 ({dts[0].strftime('%Y-%m')})"
-    elif avg_gap <= 1:  current = f"주 {7 // max(int(avg_gap), 1)}회"
-    elif avg_gap <= 3:  current = "주 2~3회"
-    elif avg_gap <= 14: current = "주 1~2회"
-    elif avg_gap <= 30: current = f"월 {max(30 // int(avg_gap), 1)}회"
-    else:               current = "월 1회 미만"
-    if avg_gap > 3:     return current, "주 2회", "주 3회"
-    if avg_gap > 2:     return current, "주 3회", "주 5회"
-    if avg_gap > 1:     return current, "주 5회", "주 7회"
-    return current, f"{current} 유지", f"{current} 유지"
-
-
-def build_kpi_dashboard(
-    instagram: dict, facebook: dict, youtube: dict, gangnam_unni: dict, hospital: dict,
-    naver_blog: dict | None = None,
-) -> list[dict]:
-    ig_en  = hospital.get("instagramEn") or {}
-    fb_en  = hospital.get("facebookEn")  or {}
-    tiktok = hospital.get("tiktok")      or {}
-    cafe   = hospital.get("naverCafe")   or {}
-
-    kpis: list[dict] = []
-    for k in [
-        _follower_kpi("YouTube 구독자",     youtube.get("subscribers")),
-        _follower_kpi("Instagram KR 팔로워", instagram.get("followers")),
-        _follower_kpi("Instagram EN 팔로워", ig_en.get("followers")),
-        _follower_kpi("Facebook KR 팔로워",  facebook.get("followers")),
-        _follower_kpi("Facebook EN 팔로워",  fb_en.get("followers")),
-        _follower_kpi("TikTok 팔로워",       tiktok.get("followers")),
-        _follower_kpi("Naver Cafe 회원 수",  cafe.get("memberCount")),
-    ]:
-        if k: kpis.append(k)
-
-    if naver_blog:
-        freq = _blog_frequency(naver_blog.get("posts") or [])
-        if freq:
-            cur, t3, t12 = freq
-            kpis.append({
-                "metric":          "네이버 블로그 포스팅 빈도",
-                "current":         cur,
-                "target_3_month":  t3,
-                "target_12_month": t12,
-            })
-
-    gu_reviews = gangnam_unni.get("totalReviews")
-    if gu_reviews:
-        if gu_reviews < 1000:    rm3, rm12 = 2.0, 6.0
-        elif gu_reviews < 5000:  rm3, rm12 = 1.10, 1.50
-        else:                    rm3, rm12 = 1.07, 1.27
-        kpis.append({
-            "metric":          "강남언니 리뷰",
-            "current":         f"{gu_reviews:,}개",
-            "target_3_month":  f"{_round_clean(int(gu_reviews * rm3)):,}개",
-            "target_12_month": f"{_round_clean(int(gu_reviews * rm12)):,}개",
-        })
-
-    return [KPIMetric.model_validate(k).model_dump() for k in kpis]

app/services/market.py

@@ -1,9 +1,7 @@
 import asyncio
+import json
 import logging
-from common.db.run import select_run
+from common.db import fetchone, execute
-from common.db.hospital import select_hospital
-from common.db.market import upsert_market_status, upsert_market_result
-from common.db.source import select_run_raw_data
 from integrations.llm.llm_service import LLMService
 from integrations.llm.prompt import (
     market_competitors_prompt,
@@ -20,27 +18,49 @@ _TYPES = ["competitors", "keywords", "trend", "target_audience"]
 async def _save(analysis_run_id: str, analysis_type: str, result, exc: Exception | None) -> None:
     if exc:
         logger.warning("[market] %s failed run=%s: %s", analysis_type, analysis_run_id, exc)
-        await upsert_market_status(analysis_run_id, analysis_type, "failed")
+        await execute(
+            "INSERT INTO market_analysis (analysis_run_id, analysis_type, status)"
+            " VALUES (%s, %s, 'failed')"
+            " ON DUPLICATE KEY UPDATE status = 'failed'",
+            (analysis_run_id, analysis_type),
+        )
     else:
-        await upsert_market_result(analysis_run_id, analysis_type, result.model_dump())
+        await execute(
+            "INSERT INTO market_analysis (analysis_run_id, analysis_type, status, data)"
+            " VALUES (%s, %s, 'done', %s)"
+            " ON DUPLICATE KEY UPDATE status = 'done', data = VALUES(data)",
+            (analysis_run_id, analysis_type, json.dumps(result.model_dump(), ensure_ascii=False)),
+        )
 
 
 async def run_market_analysis(analysis_run_id: str) -> None:
     logger.info("[market] start run=%s", analysis_run_id)
 
-    run = await select_run(analysis_run_id)
+    run = await fetchone(
-    clinic = await select_hospital(run["hospital_id"])
+        "SELECT hospital_id FROM analysis_runs WHERE analysis_run_id = %s",
-    raw = await select_run_raw_data(analysis_run_id)
+        (analysis_run_id,),
-    mainpage = raw.get("mainpage") or {}
+    )
+    clinic = await fetchone(
+        "SELECT hospital_name, road_address, raw_data FROM hospital_baseinfo WHERE hospital_id = %s",
+        (run["hospital_id"],),
+    )
 
-    clinic_name = (clinic or {}).get("hospital_name") or ""
+    raw_data = clinic["raw_data"]
-    address = (clinic or {}).get("road_address") or ""
+    clinic_data = json.loads(raw_data) if isinstance(raw_data, str) else (raw_data or {})
-    services = mainpage.get("services", [])
+
+    clinic_name = clinic["hospital_name"] or ""
+    address = clinic["road_address"] or ""
+    services = clinic_data.get("services", [])
     services_str = ", ".join(services[:3])
     primary_service = services[0] if services else ""
 
     for analysis_type in _TYPES:
-        await upsert_market_status(analysis_run_id, analysis_type, "processing")
+        await execute(
+            "INSERT INTO market_analysis (analysis_run_id, analysis_type, status)"
+            " VALUES (%s, %s, 'processing')"
+            " ON DUPLICATE KEY UPDATE status = 'processing'",
+            (analysis_run_id, analysis_type),
+        )
 
     llm = LLMService(provider="perplexity")
     results = await asyncio.gather(

app/services/pipeline.py

@@ -1,5 +1,5 @@
 import logging
-from common.db.run import select_run, update_run_status
+from common.db import fetchone, execute
 from models.status import AnalysisStatus
 from services.collect import collect_all
 from services.market import run_market_analysis
@@ -8,23 +8,49 @@ from services.analysis import run_report_task, run_plan_task
 logger = logging.getLogger(__name__)
 
 
-async def run_pipeline(analysis_run_id: str) -> None:
+async def run_pipeline(analysis_run_id: str, extra_channels: dict | None = None) -> None:
     logger.info("[pipeline] start run=%s", analysis_run_id)
+    extra_channels = extra_channels or {}
 
     # ── 1. Collect ──────────────────────────────────────────────────────────
-    run = await select_run(analysis_run_id)
+    run = await fetchone(
-    await collect_all(analysis_run_id, hospital_id=run["hospital_id"])
+        "SELECT hospital_id, instagram_data_id, facebook_data_id,"
+        " naver_blog_data_id, youtube_data_id, gangnam_unni_data_id"
+        " FROM analysis_runs WHERE analysis_run_id = %s",
+        (analysis_run_id,),
+    )
+    await collect_all(
+        analysis_run_id,
+        hospital_id=run["hospital_id"],
+        instagram_id=run["instagram_data_id"],
+        facebook_id=run["facebook_data_id"],
+        naver_blog_id=run["naver_blog_data_id"],
+        youtube_id=run["youtube_data_id"],
+        gangnam_unni_id=run["gangnam_unni_data_id"],
+        tiktok_url=extra_channels.get("tiktok"),
+        instagram_en_url=extra_channels.get("instagram_en"),
+        facebook_en_url=extra_channels.get("facebook_en"),
+    )
 
     # ── 2. Market ────────────────────────────────────────────────────────────
-    await update_run_status(analysis_run_id, AnalysisStatus.ANALYZING)
+    await execute(
+        "UPDATE analysis_runs SET status = %s WHERE analysis_run_id = %s",
+        (AnalysisStatus.ANALYZING, analysis_run_id),
+    )
     await run_market_analysis(analysis_run_id)
 
     # ── 3. Report ────────────────────────────────────────────────────────────
     await run_report_task(analysis_run_id)
 
     # ── 4. Plan ──────────────────────────────────────────────────────────────
-    await update_run_status(analysis_run_id, AnalysisStatus.PLANNING)
+    await execute(
+        "UPDATE analysis_runs SET status = %s WHERE analysis_run_id = %s",
+        (AnalysisStatus.PLANNING, analysis_run_id),
+    )
     await run_plan_task(analysis_run_id)
 
-    await update_run_status(analysis_run_id, AnalysisStatus.COMPLETED)
+    await execute(
+        "UPDATE analysis_runs SET status = %s WHERE analysis_run_id = %s",
+        (AnalysisStatus.COMPLETED, analysis_run_id),
+    )
     logger.info("[pipeline] done run=%s", analysis_run_id)

requirements.txt

@@ -10,4 +10,3 @@ passlib[bcrypt]==1.7.4
 python-multipart==0.0.26
 uuid6==2025.0.1
 aiomysql==0.3.2
-resvg-py==0.3.2