app/integrations/llm/prompt.py

@@ -1,14 +1,7 @@
 import os
 from pydantic import BaseModel
 from common.utils import get_env
-from integrations.llm.schemas.report import (
+from integrations.llm.schemas.report import ReportInput, ReportOutput, YouTubeDiagnosisInput, YouTubeDiagnosisOutput
-    ReportInput, ReportOutput,
-    CriticalIssuesInput, CriticalIssuesOutput,
-    YouTubeDiagnosisInput, YouTubeDiagnosisOutput,
-    BrandConsistencyInput, BrandConsistencyOutput,
-    TransformationInput, TransformationProposal,
-    RoadmapInput, RoadmapOutput,
-)
 from integrations.llm.schemas.plan import PlanInput, PlanOutput
 from integrations.llm.schemas.market import (
     MarketCompetitorsInput, MarketCompetitorsOutput,
@@ -94,31 +87,3 @@ youtube_diagnosis_prompt = Prompt(
     input_class=YouTubeDiagnosisInput,
     output_class=YouTubeDiagnosisOutput,
 )
-
-brand_consistency_prompt = Prompt(
-    file_name="brand_consistency_prompt.txt",
-    prompt_model="REPORT_MODEL",
-    input_class=BrandConsistencyInput,
-    output_class=BrandConsistencyOutput,
-)
-
-critical_issues_prompt = Prompt(
-    file_name="critical_issues_prompt.txt",
-    prompt_model="REPORT_MODEL",
-    input_class=CriticalIssuesInput,
-    output_class=CriticalIssuesOutput,
-)
-
-transformation_prompt = Prompt(
-    file_name="transformation_prompt.txt",
-    prompt_model="REPORT_MODEL",
-    input_class=TransformationInput,
-    output_class=TransformationProposal,
-)
-
-roadmap_prompt = Prompt(
-    file_name="roadmap_prompt.txt",
-    prompt_model="REPORT_MODEL",
-    input_class=RoadmapInput,
-    output_class=RoadmapOutput,
-)

app/integrations/llm/schemas/report.py

@@ -355,47 +355,3 @@ class YouTubeDiagnosisInput(BaseModel):
 
 class YouTubeDiagnosisOutput(BaseModel):
     diagnosis: list[DiagnosisItem]
-
-
-# --- Diagnosis ---
-
-class CriticalIssuesInput(BaseModel):
-    clinic_name: str | None = None
-    data: str | None = None
-
-
-class CriticalIssuesOutput(BaseModel):
-    diagnosis: list[DiagnosisItem]
-
-
-# --- Roadmap ---
-
-class RoadmapInput(BaseModel):
-    clinic_name: str | None = None
-    data: str | None = None
-
-
-class RoadmapOutput(BaseModel):
-    roadmap: list[RoadmapMonth]
-
-
-# --- Transformation ---
-
-class TransformationInput(BaseModel):
-    clinic_name: str | None = None
-    data: str | None = None
-
-
-# --- BrandConsistency ---
-
-class BrandConsistencyInput(BaseModel):
-    clinic_name: str | None = None
-    mainpage: str | None = None
-    instagram: str | None = None
-    facebook: str | None = None
-    youtube: str | None = None
-    gangnam_unni: str | None = None
-
-
-class BrandConsistencyOutput(BaseModel):
-    brand_inconsistencies: list[BrandInconsistency]

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

@@ -1,18 +0,0 @@
-다음은 성형외과/피부과 {clinic_name} 의 채널별 브랜드 데이터입니다.
-
-공식 홈페이지: {mainpage}
-인스타그램: {instagram}
-페이스북: {facebook}
-유튜브: {youtube}
-강남언니: {gangnam_unni}
-
-위 채널들 간의 브랜드 불일치 항목을 분석해줘.
-비교 대상 필드 예시: 병원명(한글/영문), 전화번호, 주소, 로고, 슬로건, 소개 문구 등.
-
-각 항목은 다음 JSON 형식의 배열로 출력해줘:
-- field: 불일치 필드명
-- values: 채널별 실제 값 목록 (channel, value, is_correct)
-- impact: 불일치가 브랜드에 미치는 영향
-- recommendation: 개선 권고사항
-
-출처 번호([1], [2] 등)는 포함하지 마.

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

@@ -1,13 +0,0 @@
-다음은 성형외과/피부과 {clinic_name} 의 전 채널 수집 데이터입니다.
-
-{data}
-
-위 데이터를 바탕으로 이 병원의 마케팅 전반에 걸친 핵심 문제점과 개선사항을 진단해줘.
-각 항목은 category(진단 카테고리), detail(상세 설명), severity(critical/warning/info) 형식의 JSON 배열로 출력해줘.
-
-현재 주요 진단 카테고리는 3개야.
-브랜드 아이덴티티 파편화
-콘텐츠 전략 부재
-플랫폼 간 유입 단절
-
-출처 번호([1], [2] 등)는 포함하지 마.

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

@@ -1,14 +0,0 @@
-다음은 성형외과/피부과 {clinic_name} 의 전 채널 수집 데이터입니다.
-
-{data}
-
-위 데이터를 바탕으로 이 병원의 3개월 마케팅 실행 로드맵을 수립해줘.
-month 1, 2, 3 각각 하나씩, 총 3개 항목을 포함한 roadmap JSON 배열로 출력해줘.
-
-각 항목은 아래 형식을 따라줘:
-- month: 월 번호 (1, 2, 3)
-- title: 해당 월의 핵심 테마 (예: "브랜드 정비")
-- subtitle: 한 줄 부제 (예: "기반 구축 — 로고·계정 통일")
-- tasks: 실행 과제 목록, 각 과제는 task(string)와 completed(false)로 구성
-
-출처 번호([1], [2] 등)는 포함하지 마.

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

@@ -1,14 +0,0 @@
-다음은 성형외과/피부과 {clinic_name} 의 전 채널 수집 데이터입니다.
-
-{data}
-
-위 데이터를 바탕으로 이 병원의 마케팅 전환 전략을 수립해줘.
-아래 5개 항목을 포함한 JSON을 출력해줘.
-
-1. brand_identity: 브랜드 아이덴티티 개선 항목 (area, as_is, to_be)
-2. content_strategy: 콘텐츠 전략 개선 항목 (area, as_is, to_be)
-3. platform_strategies: 플랫폼별 전략 (platform, icon, current_metric, target_metric, strategies[{strategy, detail}])
-4. website_improvements: 웹사이트 개선 항목 (area, as_is, to_be)
-5. new_channel_proposals: 신규 채널 제안 (channel, priority, rationale)
-
-출처 번호([1], [2] 등)는 포함하지 마.

app/services/analysis.py

@@ -8,9 +8,10 @@ from common.db.run import select_run, update_run_report, update_run_plan
 from common.db.source import select_run_raw_data, select_run_mainpage_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, brand_consistency_prompt, critical_issues_prompt, transformation_prompt, roadmap_prompt
+from integrations.llm.prompt import report_prompt, plan_prompt, youtube_diagnosis_prompt
-from integrations.llm.schemas.report import ReportOutput, ClinicSnapshot, YouTubeAudit, BrandConsistencyOutput, CriticalIssuesOutput, DiagnosisItem, TransformationProposal, RoadmapOutput, RoadmapMonth
+from integrations.llm.schemas.report import ReportOutput, ClinicSnapshot, YouTubeAudit
 from integrations.llm.schemas.plan import PlanOutput
+from models.status import AnalysisStatus
 
 logger = logging.getLogger(__name__)
 
@@ -208,39 +209,6 @@ async def _build_youtube_audit(youtube: dict) -> dict:
     return YouTubeAudit.model_validate(yt_patch).model_dump()
 
 
-async def _build_roadmap(analysis_run_id: str, raw: dict) -> list[dict]:
-    result: RoadmapOutput = await LLMService(provider="perplexity").generate(
-        roadmap_prompt,
-        {
-            "clinic_name": (raw.get("mainpage") or {}).get("clinicName"),
-            "data":        json.dumps(raw, ensure_ascii=False),
-        },
-    )
-    return [RoadmapMonth.model_validate(item).model_dump() for item in result.roadmap]
-
-
-async def _build_transformation(analysis_run_id: str, raw: dict) -> dict:
-    result: TransformationProposal = await LLMService(provider="perplexity").generate(
-        transformation_prompt,
-        {
-            "clinic_name": (raw.get("mainpage") or {}).get("clinicName"),
-            "data":        json.dumps(raw, ensure_ascii=False),
-        },
-    )
-    return result.model_dump()
-
-
-async def _build_critical_issues(analysis_run_id: str, raw: dict) -> list[dict]:
-    result: CriticalIssuesOutput = await LLMService(provider="perplexity").generate(
-        critical_issues_prompt,
-        {
-            "clinic_name": (raw.get("mainpage") or {}).get("clinicName"),
-            "data":        json.dumps(raw, ensure_ascii=False),
-        },
-    )
-    return [DiagnosisItem.model_validate(item).model_dump() for item in result.diagnosis]
-
-
 async def _build_overrides(analysis_run_id: str) -> dict:
     raw = await select_run_raw_data(analysis_run_id)
     if not raw:
@@ -265,26 +233,15 @@ async def _build_overrides(analysis_run_id: str) -> dict:
     if instagram.get("username"):  ig_patch["profile_link"] = f"https://www.instagram.com/{instagram['username']}/"
 
     # ── facebook ──────────────────────────────────────────────────────────────
-    fb_pages: dict = {}
-    if facebook.get("pageUrl"):    fb_pages["url"]           = facebook["pageUrl"]
-    if facebook.get("pageUrl"):    fb_pages["link"]          = facebook["pageUrl"]
-    if facebook.get("pageName"):   fb_pages["page_name"]     = facebook["pageName"]
-    if facebook.get("followers"):  fb_pages["followers"]     = facebook["followers"]
-    if facebook.get("intro"):      fb_pages["bio"]           = facebook["intro"]
-    if facebook.get("categories"): fb_pages["category"]      = ", ".join(facebook["categories"])
-    if facebook.get("website"):    fb_pages["linked_domain"] = facebook["website"]
-
-    brand = await generate_brand_consistency(analysis_run_id)
-    brand_patch = brand.model_dump()["brand_inconsistencies"]
-    critical_issues = await _build_critical_issues(analysis_run_id, raw)
-    transformation = await _build_transformation(analysis_run_id, raw)
-    roadmap = await _build_roadmap(analysis_run_id, raw)
-
     fb_patch: dict = {}
-    if fb_pages:
+    if facebook.get("pageUrl"):    fb_patch["url"]           = facebook["pageUrl"]
-        fb_patch["pages"] = [fb_pages]
+    if facebook.get("pageUrl"):    fb_patch["link"]          = facebook["pageUrl"]
-    if brand_patch:
+    if facebook.get("pageName"):   fb_patch["page_name"]     = facebook["pageName"]
-        fb_patch["brand_inconsistencies"] = brand_patch
+    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"]
+
 
     overrides: dict = {}
     if snapshot:
@@ -292,15 +249,9 @@ async def _build_overrides(analysis_run_id: str) -> dict:
     if ig_patch:
         overrides["instagram_audit"] = {"accounts": [ig_patch]}
     if fb_patch:
-        overrides["facebook_audit"] = fb_patch
+        overrides["facebook_audit"] = {"pages": [fb_patch]}
     if yt_patch:
         overrides["youtube_audit"] = yt_patch
-    if critical_issues:
-        overrides["problem_diagnosis"] = critical_issues
-    if transformation:
-        overrides["transformation"] = transformation
-    if roadmap:
-        overrides["roadmap"] = roadmap
     return overrides
 
 
@@ -317,10 +268,8 @@ def _deep_merge(base: dict, overrides: dict) -> dict:
     return base
 
 def _patch_report(result: ReportOutput, overrides: dict) -> ReportOutput:
-    base = result.model_dump()
+    merged = _deep_merge(result.model_dump(), overrides)
-    for key in overrides:
+    return ReportOutput(**merged)
-        base.pop(key, None)
-    return ReportOutput(**_deep_merge(base, overrides))
 
 
 _MOCK_DOMAINS = {"viewclinic.com"}
@@ -345,24 +294,6 @@ def _load_mock_plan() -> PlanOutput:
         return PlanOutput(**json.load(f))
 
 
-async def generate_brand_consistency(analysis_run_id: str) -> BrandConsistencyOutput:
-    raw = await select_run_raw_data(analysis_run_id)
-
-    def _json(v) -> str | None:
-        return json.dumps(v, ensure_ascii=False) if v else None
-
-    mainpage = raw.get("mainpage") or {}
-    input_data = {
-        "clinic_name":  mainpage.get("clinicName"),
-        "mainpage":     _json(mainpage),
-        "instagram":    _json(raw.get("instagram")),
-        "facebook":     _json(raw.get("facebook")),
-        "youtube":      _json(raw.get("youtube")),
-        "gangnam_unni": _json(raw.get("gangnam_unni")),
-    }
-    return await LLMService(provider="perplexity").generate(brand_consistency_prompt, input_data)
-
-
 async def run_report_task(analysis_run_id: str) -> None:
     logger.info("[report] start run=%s", analysis_run_id)
     if await _is_mock(analysis_run_id):
@@ -371,7 +302,6 @@ async def run_report_task(analysis_run_id: str) -> None:
         result.youtube_audit.linked_urls = []
     else:
         result = await generate_report(analysis_run_id)
-
     result = _patch_report(result, await _build_overrides(analysis_run_id))
     await update_run_report(analysis_run_id, result.model_dump())
     logger.info("[report] done run=%s", analysis_run_id)