.gitignore

@@ -46,5 +46,3 @@ alembic/versions/*.pyc
 test_results/
 
 app/test*
-
-docker-compose.yml

app/integrations/firecrawl.py

@@ -16,7 +16,7 @@ class FirecrawlClient:
             HTTPMethod.POST,
             url=f"{FIRECRAWL_BASE}/scrape",
             headers=self._headers(),
-            json_body={"url": url, "formats": ["json", "links"], "jsonOptions": json_options, "waitFor": wait_for, "maxAge": 0},
+            json_body={"url": url, "formats": ["json", "links"], "jsonOptions": json_options, "waitFor": wait_for},
             label="firecrawl-scrape",
         )
         if not resp or not resp.is_success:
@@ -74,9 +74,9 @@ class FirecrawlClient:
             headers=self._headers(),
             json_body={
                 "url": url,
-                "formats": ["json", "links", "rawHtml"],
+                "formats": ["json", "links", "html"],
                 "jsonOptions": {
-                    "prompt": "Extract: 클리닉 이름 - clinicName (Korean), clinic name (English), address, phone with dash format, business hours, slogan, services offered, doctors with name/title/specialty, brand identity (primary/accent/background/text colors in hex, heading/body fonts, logo URL from the actual header/main <img> src, og:image from <meta property='og:image'> content, favicon URL)",
+                    "prompt": "Extract: clinic name (Korean), clinic name (English), address, phone with dash format, business hours, slogan, services offered, doctors with name/title/specialty, brand identity (primary/accent/background/text colors in hex, heading/body fonts, logo URL from the actual header/main <img> src, og:image from <meta property='og:image'> content, favicon URL)",
                     "schema": {
                         "type": "object",
                         "properties": {
@@ -127,11 +127,8 @@ class FirecrawlClient:
                     },
                 },
                 "waitFor": 5000,
-                "maxAge": 0,
-                "proxy": "auto",     # 기본 엔진이 차단되는 사이트(예: viewclinic.com)는 자동으로 stealth 프록시로 재시도.
-                "timeout": 120000,   # proxy:auto면 60s로도 충분했지만(실측), 혹시 모르니 여유있게 120s.
             },
-            timeout=150,  # 위 Firecrawl 잡 타임아웃보다 길어야 우리 쪽 HTTP 클라이언트가 먼저 끊지 않음.
+            timeout=60,
             label="firecrawl-clinic-info",
         )
         if not resp or not resp.is_success:
@@ -150,7 +147,7 @@ class FirecrawlClient:
             # "socialMedia": info.get("socialMedia", {}),
             "branding":      info.get("branding", {}),
             "siteLinks":     data.get("links", []),
-            "html":          data.get("rawHtml", "") or data.get("html", ""),  # rawHtml = 가공 전 원본 — <script> 등 보존.
+            "html":          data.get("html", ""),  # raw HTML — collect 단계에서 tracking 추출 후 raw_data 에는 저장 안 함.
             "sourceUrl":     url,
         }
 
@@ -190,7 +187,6 @@ class FirecrawlClient:
                     },
                 },
                 "waitFor": 5000,
-                "maxAge": 0,
             },
             timeout=60,
             label="firecrawl-gangnamunni",

app/integrations/llm/gemini_vision.py

@@ -219,7 +219,7 @@ class VisionClient:
             '  "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 가장 두드러진 색 최대 2개의 hex 근사값 배열. 예: [\'#1A2B3C\', \'#D4A017\']. 색이 1개면 1개만, 강한 색이 1개도 없으면 빈 배열."]\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)."
@@ -229,9 +229,14 @@ class VisionClient:
             return {}
         # logo_images는 우리가 직접 채움 (Vision은 묘사만)
         result["logo_images"] = {"circle": None, "horizontal": logo_url, "korean": None}
-        # logo_colors_hex 최대 2개로 제한.
+        # 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("#")]
-        result["logo_colors_hex"] = hex_list[:2]
+        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(

app/integrations/llm/prompt.py

@@ -13,7 +13,7 @@ from integrations.llm.schemas.report import (
     ScoresInput, ScoresOutput,
     OtherChannelsInput, OtherChannelsOutput
 )
-from integrations.llm.schemas.plan import PlanInput, PlanOutput, SummarizeInput, SummarizeOutput
+from integrations.llm.schemas.plan import PlanInput, PlanOutput
 from integrations.llm.schemas.market import (
     MarketCompetitorsInput, MarketCompetitorsOutput,
     MarketKeywordsInput, MarketKeywordsOutput,
@@ -64,13 +64,6 @@ plan_prompt = Prompt(
     output_class=PlanOutput,
 )
 
-summarize_prompt = Prompt(
-    file_name="summarize_prompt.txt",
-    prompt_model="PLAN_MODEL",
-    input_class=SummarizeInput,
-    output_class=SummarizeOutput,
-)
-
 market_competitors_prompt = Prompt(
     file_name="market_competitors_prompt.txt",
     prompt_model="MARKET_MODEL",

app/integrations/llm/schemas/plan.py

@@ -2,15 +2,6 @@ from typing import Literal
 from pydantic import BaseModel
 
 
-class SummarizeInput(BaseModel):
-    label: str
-    data: str
-
-
-class SummarizeOutput(BaseModel):
-    summary: str
-
-
 class PlanInput(BaseModel):
     clinic_name: str | None = None
     clinic_name_en: str | None = None

app/integrations/llm/schemas/report.py

@@ -134,7 +134,7 @@ class YouTubeAudit(BaseModel):
     channel_description: str
     linked_urls: list[LinkedUrl]
     playlists: list[str]
-    top_videos: list[TopVideo] = []
+    top_videos: list[TopVideo]
     diagnosis: list[DiagnosisItem]
 
 
@@ -184,11 +184,11 @@ class FacebookPage(BaseModel):
     followers: int
     following: int
     category: str
-    bio: str = ""
+    bio: str
     logo: str
     logo_description: str
     link: str
-    linked_domain: str = ""
+    linked_domain: str
     reviews: int
     recent_post_age: str
     has_whatsapp: bool | None = None
@@ -245,7 +245,7 @@ class AdditionalDomain(BaseModel):
 
 class WebsiteAudit(BaseModel):
     primary_domain: str
-    additional_domains: list[AdditionalDomain] = []
+    additional_domains: list[AdditionalDomain]
     sns_links_on_site: bool
     sns_links_detail: list[SnsLink] | None = None
     tracking_pixels: list[TrackingPixel]

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

@@ -1,7 +0,0 @@
-다음은 "{label}" 원본 데이터입니다.
-
-{data}
-
-위 데이터를 마케팅 플랜 생성에 필요한 핵심 정보만 남기고 간결하게 요약하세요.
-구체적인 수치·날짜·고유명사(채널명, 게시물 제목 등)는 그대로 보존하고, 중복되거나 플랜 작성에 불필요한 메타데이터는 제거하세요.
-요약문 하나의 문자열로만 출력하세요.

app/integrations/youtube.py

@@ -48,7 +48,7 @@ class YouTubeClient:
         resp = await http_request(
             HTTPMethod.GET,
             url=f"{YT}/channels",
-            params={"part": "snippet,statistics,contentDetails", "id": channel_id, "key": self.api_key},
+            params={"part": "snippet,statistics", "id": channel_id, "key": self.api_key},
             label="yt-channel",
         )
         if not resp or not resp.is_success:
@@ -58,52 +58,26 @@ class YouTubeClient:
             return None
         channel = items[0]
 
-        async def _video_details(video_ids: list[str]) -> list[dict]:
+        video_ids: list[str] = []
-            """video_ids 순서를 보존한 채 snippet/statistics/contentDetails 채워서 반환."""
+        resp = await http_request(
-            if not video_ids:
+            HTTPMethod.GET,
-                return []
+            url=f"{YT}/search",
+            params={"part": "snippet", "channelId": channel_id, "order": "viewCount", "type": "video", "maxResults": 10, "key": self.api_key},
+            label="yt-search",
+        )
+        if resp and resp.is_success:
+            video_ids = [i["id"]["videoId"] for i in resp.json().get("items", []) if i.get("id", {}).get("videoId")]
+
+        videos: list[dict] = []
+        if video_ids:
             resp = await http_request(
                 HTTPMethod.GET,
                 url=f"{YT}/videos",
                 params={"part": "snippet,statistics,contentDetails", "id": ",".join(video_ids), "key": self.api_key},
                 label="yt-videos",
             )
-            if not resp or not resp.is_success:
-                return []
-            video_map = {v["id"]: v for v in resp.json().get("items", [])}
-            return [video_map[vid] for vid in video_ids if vid in video_map]
-
-        # 인기 영상 top 10 (조회수순) — search index 기반, 정확한 정렬 보장.
-        resp = await http_request(
-            HTTPMethod.GET,
-            url=f"{YT}/search",
-            params={"part": "snippet", "channelId": channel_id, "order": "viewCount", "type": "video", "maxResults": 10, "key": self.api_key},
-            label="yt-search-top",
-        )
-        top_ids: list[str] = []
             if resp and resp.is_success:
-            top_ids = [i["id"]["videoId"] for i in resp.json().get("items", []) if i.get("id", {}).get("videoId")]
+                videos = resp.json().get("items", [])[:10]
-        videos = await _video_details(top_ids)
-
-        # 최근 영상 10개 — search index는 누락이 흔해 채널의 실제 uploads 재생목록에서 직접 읽고
-        # publishedAt 기준으로 코드에서 직접 정렬 (재생목록 순서 자체는 보장되지 않으므로).
-        recents: list[dict] = []
-        uploads_id = (channel.get("contentDetails") or {}).get("relatedPlaylists", {}).get("uploads")
-        if uploads_id:
-            resp = await http_request(
-                HTTPMethod.GET,
-                url=f"{YT}/playlistItems",
-                params={"part": "snippet", "playlistId": uploads_id, "maxResults": 10, "key": self.api_key},
-                label="yt-uploads",
-            )
-            if resp and resp.is_success:
-                entries = resp.json().get("items", [])
-                entries.sort(key=lambda i: i.get("snippet", {}).get("publishedAt") or "", reverse=True)
-                recent_ids = [
-                    vid for e in entries
-                    if (vid := e.get("snippet", {}).get("resourceId", {}).get("videoId"))
-                ]
-                recents = await _video_details(recent_ids)
 
         playlists: list[dict] = []
         resp = await http_request(
@@ -115,19 +89,7 @@ class YouTubeClient:
         if resp and resp.is_success:
             playlists = resp.json().get("items", [])
 
-        return {"channelId": channel_id, "channel": channel, "videos": videos, "recents": recents, "playlists": playlists}
+        return {"channelId": channel_id, "channel": channel, "videos": videos, "playlists": playlists}
-
-    @staticmethod
-    def _video_item(v: dict) -> dict:
-        return {
-            "title": v.get("snippet", {}).get("title"),
-            "views": int(v.get("statistics", {}).get("viewCount", 0)),
-            "likes": int(v.get("statistics", {}).get("likeCount", 0)),
-            "comments": int(v.get("statistics", {}).get("commentCount", 0)),
-            "date": v.get("snippet", {}).get("publishedAt"),
-            "duration": v.get("contentDetails", {}).get("duration"),
-            "url": f"https://www.youtube.com/watch?v={v['id']}",
-        }
 
     async def get_channel(self, url: str) -> dict | None:
         raw = await self.fetch_channel(url)
@@ -148,8 +110,18 @@ class YouTubeClient:
             "subscribers": int(stats.get("subscriberCount", 0)),
             "totalViews": int(stats.get("viewCount", 0)),
             "totalVideos": int(stats.get("videoCount", 0)),
-            "videos": [self._video_item(v) for v in raw["videos"]],
+            "videos": [
-            "recents": [self._video_item(v) for v in raw["recents"]],
+                {
+                    "title": v.get("snippet", {}).get("title"),
+                    "views": int(v.get("statistics", {}).get("viewCount", 0)),
+                    "likes": int(v.get("statistics", {}).get("likeCount", 0)),
+                    "comments": int(v.get("statistics", {}).get("commentCount", 0)),
+                    "date": v.get("snippet", {}).get("publishedAt"),
+                    "duration": v.get("contentDetails", {}).get("duration"),
+                    "url": f"https://www.youtube.com/watch?v={v['id']}",
+                }
+                for v in raw["videos"]
+            ],
             "playlists": [
                 p.get("snippet", {}).get("title")
                 for p in raw["playlists"]

app/models/report.py

@@ -130,7 +130,7 @@ class YouTubeAudit(CamelModel):
     channel_description: str
     linked_urls: list[LinkedUrl]
     playlists: list[str]
-    top_videos: list[TopVideo] = []
+    top_videos: list[TopVideo]
     diagnosis: list[DiagnosisItem]
 
 
@@ -176,11 +176,11 @@ class FacebookPage(CamelModel):
     followers: int
     following: int
     category: str
-    bio: str = ""
+    bio: str
     logo: str
     logo_description: str
     link: str
-    linked_domain: str = ""
+    linked_domain: str
     reviews: int
     recent_post_age: str
     has_whatsapp: bool | None = None
@@ -222,7 +222,7 @@ class AdditionalDomain(CamelModel):
 
 class WebsiteAudit(CamelModel):
     primary_domain: str
-    additional_domains: list[AdditionalDomain] = []
+    additional_domains: list[AdditionalDomain]
     sns_links_on_site: bool
     sns_links_detail: list[SnsLink] | None = None
     tracking_pixels: list[TrackingPixel]

app/services/analysis.py

@@ -1,4 +1,3 @@
-import asyncio
 import json
 import logging
 from urllib.parse import urlparse
@@ -8,7 +7,7 @@ from common.db.run import update_run_report, update_run_plan, select_run_report_
 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, summarize_prompt, youtube_diagnosis_prompt, brand_consistency_prompt, critical_issues_prompt, transformation_prompt, roadmap_prompt, scores_prompt, other_channels_prompt
+from integrations.llm.prompt import report_prompt, plan_prompt, youtube_diagnosis_prompt, brand_consistency_prompt, critical_issues_prompt, transformation_prompt, roadmap_prompt, scores_prompt, other_channels_prompt
 from integrations.llm.schemas.report import ReportOutput, ClinicSnapshot, YouTubeAudit, BrandConsistencyOutput, CriticalIssuesOutput, DiagnosisItem, TransformationProposal, RoadmapOutput, RoadmapMonth, ScoresOutput, ChannelScore, WebsiteAudit, OtherChannelsOutput, OtherChannel
 from services.branding import analyze_branding
 from services.instagram_audit import build_instagram_audit
@@ -50,26 +49,6 @@ async def generate_plan(analysis_run_id: str) -> PlanOutput:
     def _json(v) -> str | None:
         return json.dumps(v, ensure_ascii=False) if v else None
 
-    # map: 큰 입력은 LLM으로 압축 요약해 100KB 초과 에러를 방지하고, 작은 입력은 그대로 둔다.
-    large_fields = {
-        "report":                 _json(report),
-        "market_competitors":     _json(market.get("competitors")),
-        "market_keywords":        _json(market.get("keywords")),
-        "market_trend":           _json(market.get("trend")),
-        "market_target_audience": _json(market.get("target_audience")),
-        "tiktok":                 _json(tiktok),
-        "instagram":              _json(instagram),
-        "facebook":               _json(facebook),
-        "naver_cafe":             _json(naver_cafe),
-        "channel_logos":          _json(branding.get("channelLogos")),
-        "brand_assets":           _json(branding.get("brandAssets")),
-    }
-    summarized = dict(zip(
-        large_fields.keys(),
-        await asyncio.gather(*(_summarize(label, data) for label, data in large_fields.items())),
-    ))
-
-    # reduce: 요약된 입력을 모아 최종 플랜 생성.
     input_data = {
         "clinic_name":            clinic.get("clinicName"),
         "clinic_name_en":         clinic.get("clinicNameEn"),
@@ -78,24 +57,24 @@ async def generate_plan(analysis_run_id: str) -> PlanOutput:
         "slogan":                 clinic.get("slogan"),
         "services":               json.dumps(clinic.get("services", []), ensure_ascii=False),
         "doctors":                json.dumps(clinic.get("doctors", []), ensure_ascii=False),
+        "report":                 _json(report),
+        "market_competitors":     _json(market.get("competitors")),
+        "market_keywords":        _json(market.get("keywords")),
+        "market_trend":           _json(market.get("trend")),
+        "market_target_audience": _json(market.get("target_audience")),
+        "tiktok":                 _json(tiktok),
+        "instagram":           _json(instagram),
+        "facebook":            _json(facebook),
         "naver_blog":             _json(_naver_blog_summary(naver_blog)),
+        "naver_cafe":             _json(naver_cafe),
         "kakao_talk":             _json(kakaotalk),
-        **summarized,
+        "channel_logos":          _json(branding.get("channelLogos")),
+        "brand_assets":           _json(branding.get("brandAssets")),
     }
 
     return await LLMService(provider="perplexity").generate(plan_prompt, input_data)
 
 
-_SUMMARIZE_THRESHOLD = 4000  # 이 길이(문자 수)를 넘는 입력만 요약 LLM 호출 (불필요한 호출 방지)
-
-
-async def _summarize(label: str, data: str | None) -> str | None:
-    if not data or len(data) <= _SUMMARIZE_THRESHOLD:
-        return data
-    result = await LLMService(provider="perplexity").generate(summarize_prompt, {"label": label, "data": data})
-    return result.summary
-
-
 def _build_clinic_snapshot(mainpage: dict, gangnam_unni: dict, brand_assets: dict, logo_url: str | None) -> dict:
     snapshot: dict = {}
     doctors = gangnam_unni.get("doctors", [])
@@ -135,13 +114,12 @@ def _naver_blog_summary(blog: dict | None) -> dict | None:
 
 async def _build_youtube_audit(youtube: dict) -> dict: # 기획상 1개의 input channel, 다중 채널은 기획에 없음.
     videos = youtube.get("videos", [])
-    recents = youtube.get("recents", [])
     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(recents),
+        "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"]
@@ -363,9 +341,8 @@ async def run_plan_task(analysis_run_id: str) -> None:
     result = await generate_plan(analysis_run_id)
     # profile_photo 는 brand_assets.logo_description 으로 코드가 박음 (LLM "(가이드 미보유)" 같은 hallucination 차단).
     raw = await select_run_raw_data(analysis_run_id)
-    branding_list = raw.get(SourceType.BRANDING) or []
+    branding = raw.get(SourceType.BRANDING) or []
-    branding_data = branding_list[0]["raw_data"] if branding_list else {}
+    logo_desc = (((branding[0] if branding else {}).get("brandAssets") or {}).get("logo_description")) or ""
-    logo_desc = (branding_data.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/branding.py

@@ -39,11 +39,7 @@ async def _describe_logo(analysis_run_id: str, info_id: int, vc: VisionClient) -
         if result:
             break
     if result:
-        # collect_brand_basics 가 미리 채운 brand_colors/color_palette/color_source 보존하면서 logo_* 덧붙이기.
+        await update_raw_info_merge(info_id, {"brandAssets": result})
-        raw = await select_run_raw_data(analysis_run_id)
-        existing = ((raw.get("branding") or [{}])[0].get("raw_data") or {}).get("brandAssets") or {}
-        merged = {**existing, **result}
-        await update_raw_info_merge(info_id, {"brandAssets": merged})
     logger.info("[brand_logo] done keys=%s", list(result.keys()) if result else None)
 
 

app/services/collect.py

@@ -86,15 +86,15 @@ async def collect_gangnam_unni(analysis_run_id: str, info_id: int, url: str) ->
     logger.info("[gangnam_unni] done run=%s", analysis_run_id)
 
 
-def _extract_website_audit(html: str, url: str) -> dict:
+def _extract_website_audit(html: str, site_links: list[str], url: str) -> dict:
-    """raw HTML 에서 main CTA / tracking pixels / SNS / additional domains 정규식 추출."""
+    """raw HTML + siteLinks 에서 main CTA / tracking pixels / SNS / additional domains 정규식 추출."""
     if not html:
         return {}
     primary_host = urlparse(url).netloc
     result: dict = {
         "trackingPixels":    extract_tracking_pixels(html),
-        "snsLinks":          extract_sns_links(html),
+        "snsLinks":          extract_sns_links(site_links, html),
-        "additionalDomains": extract_additional_domains(html, primary_host),
+        "additionalDomains": extract_additional_domains(site_links, primary_host),
     }
     html_cta = extract_main_cta(html)
     if html_cta:
@@ -115,7 +115,7 @@ async def collect_mainpage(analysis_run_id: str, info_id: int, hospital_id: str,
     html = data.pop("html", "") or ""  # raw_data 에는 저장 안 함 — 여기서만 사용하고 버림.
     # 홈페이지 URL 자체도 raw_data 에 박아둬야 brand_assets / 분석 단계에서 mainpage URL 재조회 없이 사용 가능.
     data = {**data, "sourceUrl": url}
-    data.update(_extract_website_audit(html, url))
+    data.update(_extract_website_audit(html, data.get("siteLinks", []), url))
 
     await update_raw_info(info_id, data)
     await update_hospital(hospital_id, data, analysis_run_id=analysis_run_id)

app/services/facebook_audit.py

@@ -89,8 +89,7 @@ def _logo_data(channel_logos: dict, channel: str) -> dict:
     }
 
 def _page_patch(item: dict, channel_logos) -> dict:
-    p: dict = {"page_name": "", "followers": 0, "category": "", "reviews": 0,
+    p: dict = {}
-               "following": 0, "recent_post_age": "", "post_frequency": "", "engagement": ""}
     fb = item["raw_data"]
     language = item.get("language") if item.get("language") else "KR"
     label = "페이스북 " + language

app/services/kpi_dashboard.py

@@ -64,8 +64,8 @@ def build_kpi_dashboard(
     print("facebook_kpis", facebook_kpis)
     print("instagram_kpis", instagram_kpis)
 
-    kpis += [k for k in facebook_kpis if k]
+    kpis += facebook_kpis
-    kpis += [k for k in instagram_kpis if k]
+    kpis += instagram_kpis
     for k in [
         _follower_kpi("YouTube 구독자",     youtube.get("subscribers")),
         _follower_kpi("TikTok 팔로워",       tiktok.get("followers")),

app/services/website_parser.py

@@ -1,11 +1,13 @@
-"""HTML 정규식 기반 deterministic 추출 — tracking pixels / SNS / additional domains / main CTA."""
+"""collect 단계 - HTML / siteLinks 에서 tracking pixels / SNS links / additional domains 추출.
+모두 정규식·도메인 매칭 기반 deterministic 추출 (LLM 미경유)."""
 import re
 from urllib.parse import urlparse
 
 
 # ── tracking pixels ──────────────────────────────────────────────────────────
 
-# 픽셀별 시그니처: 하나라도 매치되면 installed, ID 는 첫 non-empty 그룹.
+# 픽셀별 시그니처: 이름 → 정규식 리스트. installed 판정은 OR (하나라도 매치되면 설치된 것).
+# ID 캡처는 모든 패턴 시도 후 첫 non-empty 그룹 사용 (signature-only 패턴과 ID-capture 패턴 혼재 OK).
 _TRACKING_PIXEL_PATTERNS: dict[str, list[re.Pattern]] = {
     "Google Analytics": [
         re.compile(r"googletagmanager\.com/gtag/js\?id=(G-[A-Z0-9]+)", re.IGNORECASE),
@@ -37,7 +39,8 @@ _TRACKING_PIXEL_PATTERNS: dict[str, list[re.Pattern]] = {
 
 
 def extract_tracking_pixels(html: str) -> list[dict]:
-    """HTML 에서 트래킹 픽셀 설치 여부 + ID 추출."""
+    """HTML 에서 트래킹 픽셀 설치 여부 + ID 추출. 환각 0 — 정규식 매치만 신뢰.
+    모든 패턴 시도 → 하나라도 매치되면 installed. ID 는 캡처된 첫 non-empty 그룹 사용."""
     if not html:
         return []
     pixels: list[dict] = []
@@ -63,15 +66,9 @@ def extract_tracking_pixels(html: str) -> list[dict]:
 # ── main CTA ─────────────────────────────────────────────────────────────────
 
 _CTA_KEYWORDS = re.compile(
-    r"(상담|예약|문의|신청|등록|Book\s*Now|Consult|Reservation|Contact|Apply)",
+    r"(상담|예약|문의|신청|등록|진료|Book\s*Now|Consult|Reservation|Contact|Apply)",
     re.IGNORECASE,
 )
-# CTA 카테고리 우선순위 (전화>카톡>예약). 매치 없으면 결과에 포함 X.
-_CTA_CATEGORIES: list[tuple[str, re.Pattern]] = [
-    ("전화 상담",     re.compile(r"전화|\bCall\b|\bPhone\b", re.IGNORECASE)),
-    ("카카오톡 상담", re.compile(r"카(카오)?톡|Kakao", re.IGNORECASE)),
-    ("온라인 예약",   re.compile(r"예약|Reserv|Book", re.IGNORECASE)),
-]
 _BUTTON_TAG = re.compile(r"<button\b[^>]*>(.*?)</button>", re.IGNORECASE | re.DOTALL)
 _ANCHOR_TAG = re.compile(r"<a\b[^>]*>(.*?)</a>", re.IGNORECASE | re.DOTALL)
 
@@ -81,37 +78,22 @@ def _clean_text(inner: str) -> str:
     return re.sub(r"\s+", " ", text).strip()
 
 
-_MAIN_CTA_LIMIT = 3
-
-
-def _classify_cta(text: str) -> str | None:
-    """CTA 텍스트 → 카테고리 (전화/카톡/예약). 매치 없으면 None."""
-    for label, pat in _CTA_CATEGORIES:
-        if pat.search(text):
-            return label
-    return None
-
-
 def extract_main_cta(html: str) -> str:
-    """<button>·<a> 라벨을 카테고리로 분류 후 우선순위 순서로 최대 _MAIN_CTA_LIMIT 개 join."""
+    """HTML 에서 primary CTA 텍스트 추출. 우선순위: <button> 첫 매치 → <a> 첫 매치.
+    CTA 키워드 매칭 + 길이 1~20자 (버튼 라벨 추정)."""
     if not html:
         return ""
-    found: set[str] = set()
     for pat in (_BUTTON_TAG, _ANCHOR_TAG):
         for m in pat.finditer(html):
             text = _clean_text(m.group(1))
             if text and 1 <= len(text) <= 20 and _CTA_KEYWORDS.search(text):
-                cat = _classify_cta(text)
+                return text
-                if cat:
+    return ""
-                    found.add(cat)
-    ordered_labels = [label for label, _ in _CTA_CATEGORIES]
-    result = [label for label in ordered_labels if label in found]
-    return " + ".join(result[:_MAIN_CTA_LIMIT])
 
 
 # ── SNS links ────────────────────────────────────────────────────────────────
 
-# SNS 도메인 → 표준 platform 이름.
+# SNS 도메인 → 표준 platform 이름. siteLinks 필터링 + DOM 위치 판정에 공유.
 _SNS_DOMAINS: dict[str, str] = {
     "facebook.com":   "Facebook",
     "instagram.com":  "Instagram",
@@ -128,16 +110,11 @@ _SNS_DOMAINS: dict[str, str] = {
 
 _FOOTER_BLOCK = re.compile(r"<footer\b[^>]*>(.*?)</footer>", re.IGNORECASE | re.DOTALL)
 _HEADER_BLOCK = re.compile(r"<header\b[^>]*>(.*?)</header>", re.IGNORECASE | re.DOTALL)
-# <a> 태그 안의 href 만 매치 — <link>/<script> 등 리소스 호출 제외 (CDN 노이즈 차단).
-_ANCHOR_HREF_PATTERN = re.compile(
-    r"""<a\b[^>]*\bhref\s*=\s*['"](https?://[^'"\s]+)['"]""",
-    re.IGNORECASE,
-)
 
 
 def _platform_for(url: str) -> str | None:
     try:
-        host = urlparse(url).netloc.lower().removeprefix("www.")
+        host = urlparse(url).netloc.lower().lstrip("www.")
     except Exception:
         return None
     for domain, name in _SNS_DOMAINS.items():
@@ -147,7 +124,7 @@ def _platform_for(url: str) -> str | None:
 
 
 def _location_for(url: str, html: str) -> str:
-    """url 이 <header>/<footer> 블록 안에 있는지 판정 — 둘 다 아니면 'body'."""
+    """url 이 HTML 의 <header>/<footer> 안에 들어있는지로 위치 판정. 둘 다 아니면 'body'."""
     if not html:
         return ""
     needle = re.escape(url)
@@ -160,21 +137,11 @@ def _location_for(url: str, html: str) -> str:
     return "body"
 
 
-def _extract_anchor_hrefs(html: str) -> list[str]:
+def extract_sns_links(site_links: list[str], html: str = "") -> list[dict]:
-    """<a href="http..."> 만 추출 — 순서 보존, 중복 제거."""
+    """siteLinks 에서 SNS 도메인 매칭. location 은 HTML 의 footer/header tag 블록으로 판정.
-    if not html:
+    중복 platform 은 첫 URL 만 유지."""
-        return []
-    seen: dict[str, None] = {}
-    for url in _ANCHOR_HREF_PATTERN.findall(html):
-        if url not in seen:
-            seen[url] = None
-    return list(seen.keys())
-
-
-def extract_sns_links(html: str) -> list[dict]:
-    """anchor href 에서 SNS 도메인 매칭. 중복 platform 은 첫 URL 만 유지."""
     seen: dict[str, dict] = {}
-    for url in _extract_anchor_hrefs(html):
+    for url in site_links or []:
         platform = _platform_for(url)
         if not platform or platform in seen:
             continue
@@ -186,63 +153,26 @@ def extract_sns_links(html: str) -> list[dict]:
     return list(seen.values())
 
 
-# ── additional domains (글로벌/다국어 사이트만) ─────────────────────────────────
+# ── additional domains ───────────────────────────────────────────────────────
 
-# 언어 코드 → 한국어 라벨.
+def extract_additional_domains(site_links: list[str], primary_host: str) -> list[dict]:
-_LANG_LABEL: dict[str, str] = {
+    """siteLinks 의 host 중 primary 도메인이 아닌 외부 도메인 모으기. SNS·맵 등 utility 도메인 제외.
-    "en": "영어", "eng": "영어",
+    purpose 분류는 LLM 필요 — 여기선 빈 문자열."""
-    "zh": "중국어", "cn": "중국어", "chn": "중국어",
+    skip_domains = set(_SNS_DOMAINS) | {
-    "ja": "일본어", "jp": "일본어",
+        "map.kakao.com", "map.naver.com", "maps.google.com", "goo.gl",
-    "ko": "한국어", "kor": "한국어", "kr": "한국어",
+    }
-    "vi": "베트남어", "vn": "베트남어",
+    primary = (primary_host or "").lower().lstrip("www.")
-    "th": "태국어", "thai": "태국어",
-    "ru": "러시아어",
-    "es": "스페인어",
-    "mn": "몽골어",
-    "ar": "아랍어", "arab": "아랍어",
-    "id": "인도네시아어",
-    "de": "독일어",
-    "fr": "프랑스어",
-    "pt": "포르투갈어",
-}
-
-_LANG_LI_PATTERN = re.compile(
-    r'<li\b[^>]*\bdata-lang\s*=\s*[\'"]([^\'"]+)[\'"][^>]*>(.*?)</li>',
-    re.IGNORECASE | re.DOTALL,
-)
-
-
-def extract_additional_domains(html: str, primary_host: str) -> list[dict]:
-    """글로벌/다국어 사이트 anchor 수집 — data-lang 마커 + URL 서브도메인 prefix 로 식별."""
-    if not html:
-        return []
-    primary = (primary_host or "").lower().removeprefix("www.")
     seen: dict[str, dict] = {}
-
+    for url in site_links or []:
-    def add(host: str, lang_kr: str):
+        if not url or not url.startswith("http"):
-        host = host.lower().removeprefix("www.")
-        if not host or host == primary or host in seen:
-            return
-        seen[host] = {"domain": host, "purpose": lang_kr}
-
-    # 1) <li data-lang="xx"> 안의 anchor
-    for m in _LANG_LI_PATTERN.finditer(html):
-        lang_kr = _LANG_LABEL.get(m.group(1).lower())
-        if not lang_kr:
             continue
-        a_m = re.search(r'<a[^>]*href\s*=\s*[\'"](https?://[^\'"\s]+)[\'"]', m.group(2), re.IGNORECASE)
+        try:
-        if a_m:
+            host = urlparse(url).netloc.lower().lstrip("www.")
-            add(urlparse(a_m.group(1)).netloc, lang_kr)
+        except Exception:
-
+            continue
-    # 2) URL 서브도메인 prefix 가 언어 코드
+        if not host or host == primary or host in seen:
-    for url in _extract_anchor_hrefs(html):
+            continue
-        host = urlparse(url).netloc.lower().removeprefix("www.")
+        if any(host == d or host.endswith("." + d) for d in skip_domains):
-        parts = host.split(".")
+            continue
-        if len(parts) >= 3:
+        seen[host] = {"domain": host, "purpose": ""}
-            lang_kr = _LANG_LABEL.get(parts[0])
-            if lang_kr:
-                add(host, lang_kr)
-
     return list(seen.values())
-
-

docker-compose.yml

@@ -12,8 +12,3 @@ services:
     volumes:
       - ./app:/app
     restart: unless-stopped
-    networks:
-       - o2o-net
-networks:
-  o2o-net:
-    external: true