roadmap 섹션 정보 출력

app/integrations/llm/prompt.py

@@ -1,7 +1,14 @@
 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,
+    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,
@@ -87,3 +94,31 @@ 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

@@ -355,3 +355,47 @@ 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

@@ -0,0 +1,18 @@
+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

@@ -0,0 +1,13 @@
+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

@@ -0,0 +1,14 @@
+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

@@ -0,0 +1,14 @@
+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

@@ -8,10 +8,9 @@ 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
+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.schemas.report import ReportOutput, ClinicSnapshot, YouTubeAudit
+from integrations.llm.schemas.report import ReportOutput, ClinicSnapshot, YouTubeAudit, BrandConsistencyOutput, CriticalIssuesOutput, DiagnosisItem, TransformationProposal, RoadmapOutput, RoadmapMonth
 from integrations.llm.schemas.plan import PlanOutput
-from models.status import AnalysisStatus
 
 logger = logging.getLogger(__name__)
 
@@ -209,6 +208,39 @@ 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:
@@ -233,15 +265,26 @@ 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_patch: dict = {}
+    fb_pages: dict = {}
-    if facebook.get("pageUrl"):    fb_patch["url"]           = facebook["pageUrl"]
+    if facebook.get("pageUrl"):    fb_pages["url"]           = facebook["pageUrl"]
-    if facebook.get("pageUrl"):    fb_patch["link"]          = facebook["pageUrl"]
+    if facebook.get("pageUrl"):    fb_pages["link"]          = facebook["pageUrl"]
-    if facebook.get("pageName"):   fb_patch["page_name"]     = facebook["pageName"]
+    if facebook.get("pageName"):   fb_pages["page_name"]     = facebook["pageName"]
-    if facebook.get("followers"):  fb_patch["followers"]     = facebook["followers"]
+    if facebook.get("followers"):  fb_pages["followers"]     = facebook["followers"]
-    if facebook.get("intro"):      fb_patch["bio"]           = facebook["intro"]
+    if facebook.get("intro"):      fb_pages["bio"]           = facebook["intro"]
-    if facebook.get("categories"): fb_patch["category"]     = ", ".join(facebook["categories"])
+    if facebook.get("categories"): fb_pages["category"]      = ", ".join(facebook["categories"])
-    if facebook.get("website"):    fb_patch["linked_domain"] = facebook["website"]
+    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:
+        fb_patch["pages"] = [fb_pages]
+    if brand_patch:
+        fb_patch["brand_inconsistencies"] = brand_patch
 
     overrides: dict = {}
     if snapshot:
@@ -249,9 +292,15 @@ async def _build_overrides(analysis_run_id: str) -> dict:
     if ig_patch:
         overrides["instagram_audit"] = {"accounts": [ig_patch]}
     if fb_patch:
-        overrides["facebook_audit"] = {"pages": [fb_patch]}
+        overrides["facebook_audit"] = 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
 
 
@@ -268,8 +317,10 @@ def _deep_merge(base: dict, overrides: dict) -> dict:
     return base
 
 def _patch_report(result: ReportOutput, overrides: dict) -> ReportOutput:
-    merged = _deep_merge(result.model_dump(), overrides)
+    base = result.model_dump()
-    return ReportOutput(**merged)
+    for key in overrides:
+        base.pop(key, None)
+    return ReportOutput(**_deep_merge(base, overrides))
 
 
 _MOCK_DOMAINS = {"viewclinic.com"}
@@ -294,6 +345,24 @@ 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):
@@ -302,6 +371,7 @@ 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)