366 lines
17 KiB
Python
366 lines
17 KiB
Python
import json
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import logging
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import re
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from datetime import datetime
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from common.db import fetchone, execute, fetch_raw, get_analysis_raw_data, save_analysis_report, get_market_analysis
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from integrations.llm.llm_service import LLMService
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from integrations.llm.prompt import report_prompt, plan_prompt, youtube_diagnosis_prompt
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from integrations.llm.schemas.report import ReportOutput, ClinicSnapshot, YouTubeAudit
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from services.instagram_audit import build_instagram_accounts
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from services.facebook_audit import build_facebook_pages
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from integrations.llm.schemas.plan import PlanOutput
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from models.status import AnalysisStatus
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logger = logging.getLogger(__name__)
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async def generate_report(analysis_run_id: str) -> ReportOutput:
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run = await fetchone(
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"SELECT hospital_id FROM analysis_runs WHERE analysis_run_id = %s",
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(analysis_run_id,),
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)
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clinic_row = await fetchone(
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"SELECT raw_data FROM hospital_baseinfo WHERE hospital_id = %s",
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(run["hospital_id"],),
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)
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raw_data = clinic_row["raw_data"] if clinic_row else None
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clinic = json.loads(raw_data) if isinstance(raw_data, str) else (raw_data or {})
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raw = await get_analysis_raw_data(analysis_run_id)
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market = await get_market_analysis(analysis_run_id)
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def _json(v) -> str | None:
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return json.dumps(v, ensure_ascii=False) if v else None
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input_data = {
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"clinic_name": clinic.get("clinicName"),
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"clinic_name_en": clinic.get("clinicNameEn"),
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"address": clinic.get("address"),
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"phone": clinic.get("phone"),
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"slogan": clinic.get("slogan"),
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"services": json.dumps(clinic.get("services", []), ensure_ascii=False),
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"doctors": json.dumps(clinic.get("doctors", []), ensure_ascii=False),
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"market_competitors": _json(market.get("competitors")),
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"market_keywords": _json(market.get("keywords")),
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"market_trend": _json(market.get("trend")),
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"market_target_audience": _json(market.get("target_audience")),
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"branding": _json(clinic.get("branding")),
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"brand_assets": _json(clinic.get("brandAssets")),
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"tiktok": _json(clinic.get("tiktok")),
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"instagram_en": _json(clinic.get("instagramEn")),
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"facebook_en": _json(clinic.get("facebookEn")),
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"kakao_talk": _json(clinic.get("kakaoTalk")),
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"naver_cafe": _json(clinic.get("naverCafe")),
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"channel_logos": _json(clinic.get("channelLogos")),
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**{
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channel: _json(data)
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for channel, data in raw.items()
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},
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}
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return await LLMService(provider="perplexity").generate(report_prompt, input_data)
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async def generate_plan(analysis_run_id: str) -> PlanOutput:
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run = await fetchone(
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"SELECT hospital_id, report_data FROM analysis_runs WHERE analysis_run_id = %s",
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(analysis_run_id,),
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)
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clinic_row = await fetchone(
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"SELECT raw_data FROM hospital_baseinfo WHERE hospital_id = %s",
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(run["hospital_id"],),
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)
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raw_data = clinic_row["raw_data"] if clinic_row else None
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clinic = json.loads(raw_data) if isinstance(raw_data, str) else (raw_data or {})
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report_data = run["report_data"]
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report = json.loads(report_data) if isinstance(report_data, str) else report_data
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market = await get_market_analysis(analysis_run_id)
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raw = await get_analysis_raw_data(analysis_run_id)
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def _json(v) -> str | None:
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return json.dumps(v, ensure_ascii=False) if v else None
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input_data = {
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"clinic_name": clinic.get("clinicName"),
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"clinic_name_en": clinic.get("clinicNameEn"),
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"address": clinic.get("address"),
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"phone": clinic.get("phone"),
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"slogan": clinic.get("slogan"),
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"services": json.dumps(clinic.get("services", []), ensure_ascii=False),
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"doctors": json.dumps(clinic.get("doctors", []), ensure_ascii=False),
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"report": _json(report),
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"market_competitors": _json(market.get("competitors")),
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"market_keywords": _json(market.get("keywords")),
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"market_trend": _json(market.get("trend")),
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"market_target_audience": _json(market.get("target_audience")),
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"tiktok": _json(clinic.get("tiktok")),
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"instagram_en": _json(clinic.get("instagramEn")),
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"facebook_en": _json(clinic.get("facebookEn")),
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"naver_blog": _json(_naver_blog_summary(raw.get("naver_blog"))),
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"naver_cafe": _json(clinic.get("naverCafe")),
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"kakao_talk": _json(clinic.get("kakaoTalk")),
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"channel_logos": _json(clinic.get("channelLogos")),
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"brand_assets": _json(clinic.get("brandAssets")),
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}
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return await LLMService(provider="perplexity").generate(plan_prompt, input_data)
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def _build_clinic_snapshot(gangnam_unni: dict, hospital: dict) -> dict:
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snapshot: dict = {}
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doctors = gangnam_unni.get("doctors", [])
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lead = max(doctors, key=lambda d: d.get("reviews", 0)) if doctors else None
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if gangnam_unni.get("name"): snapshot["name"] = gangnam_unni["name"]
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if hospital.get("clinicNameEn"): snapshot["name_en"] = hospital["clinicNameEn"]
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if hospital.get("phone"): snapshot["phone"] = hospital["phone"]
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if hospital.get("domain"): snapshot["domain"] = hospital["domain"]
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if gangnam_unni.get("rating"): snapshot["overall_rating"] = gangnam_unni["rating"]
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if gangnam_unni.get("totalReviews"): snapshot["total_reviews"] = gangnam_unni["totalReviews"]
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if gangnam_unni.get("address"): snapshot["location"] = gangnam_unni["address"]
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if gangnam_unni.get("badges"): snapshot["certifications"] = gangnam_unni["badges"]
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if gangnam_unni.get("totalMajorStaffs"): snapshot["staff_count"] = gangnam_unni["totalMajorStaffs"]
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if lead:
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snapshot["lead_doctor"] = {
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"name": lead.get("name"),
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"credentials": lead.get("specialty"),
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"rating": lead.get("rating"),
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"review_count": lead.get("reviews"),
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}
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# brand_assets에서 logo_images / brand_colors 강제 주입. LLM이 프롬프트 가드 무시하고 null로 두는 케이스 차단.
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ba = hospital.get("brandAssets") or {}
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if ba.get("logo_images"): snapshot["logo_images"] = ba["logo_images"]
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if ba.get("brand_colors"): snapshot["brand_colors"] = ba["brand_colors"]
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return ClinicSnapshot.model_validate(snapshot).model_dump()
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def _naver_blog_summary(blog: dict | None) -> dict | None:
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"""plan 카드 한 장에 들어가는 건 전체 포스트 수와 최근 활동 시점뿐. 그 외(본문·링크·제목)는
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던져봐야 토큰만 늘고 LLM이 무관 정보로 hallucinate 함."""
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if not blog:
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return None
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posts = blog.get("posts") or []
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return {
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"totalPosts": blog.get("totalResults"),
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"latestPostDate": posts[0].get("postDate") if posts else None,
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}
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def _parse_iso_duration_seconds(iso: str) -> int:
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m = re.match(r"PT(?:(\d+)H)?(?:(\d+)M)?(?:(\d+)S)?", iso or "")
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if not m:
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return 0
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h, mins, s = (int(x or 0) for x in m.groups())
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return h * 3600 + mins * 60 + s
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def _format_seconds(seconds: int) -> str:
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m, s = divmod(seconds, 60)
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h, m = divmod(m, 60)
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return f"{h}시간 {m}분" if h else f"{m}분 {s}초"
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def _format_clock(seconds: int) -> str:
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m, s = divmod(seconds, 60)
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h, m = divmod(m, 60)
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return f"{h}:{m:02d}:{s:02d}" if h else f"{m}:{s:02d}"
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def _calc_avg_video_length(videos: list[dict]) -> str:
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durations = [_parse_iso_duration_seconds(v.get("duration", "")) for v in videos]
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durations = [d for d in durations if d > 0]
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if not durations:
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return ""
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return _format_seconds(sum(durations) // len(durations))
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def _relative_date(date_str: str) -> str:
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if not date_str:
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return ""
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try:
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past = datetime.fromisoformat(date_str[:10])
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except ValueError:
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return ""
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days = (datetime.now() - past).days
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if days < 1:
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return "오늘"
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if days < 30:
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return f"{days}일 전"
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if days < 365:
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return f"{days // 30}개월 전"
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return f"{days // 365}년 전"
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def _calc_upload_frequency(videos: list[dict]) -> str:
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dates = sorted(
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[v["date"][:10] for v in videos if v.get("date")],
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reverse=True,
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)
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if len(dates) < 2:
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return ""
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gaps = [
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(datetime.fromisoformat(dates[i]) - datetime.fromisoformat(dates[i + 1])).days
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for i in range(len(dates) - 1)
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]
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avg_days = sum(gaps) // len(gaps)
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if avg_days <= 7:
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return f"주 {7 // max(avg_days, 1)}회"
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if avg_days <= 30:
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return f"월 {30 // avg_days}회"
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return f"{avg_days}일에 1회"
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async def _build_youtube_audit(youtube: dict) -> dict:
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videos = youtube.get("videos", [])
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yt_patch: dict = {
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"weekly_view_growth": {"absolute": 0, "percentage": 0.0},
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"estimated_monthly_revenue": {"min": 0, "max": 0},
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"linked_urls": [],
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"avg_video_length": _calc_avg_video_length(videos),
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"upload_frequency": _calc_upload_frequency(videos),
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}
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if youtube.get("channelName"): yt_patch["channel_name"] = youtube["channelName"]
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if youtube.get("handle"): yt_patch["handle"] = youtube["handle"]
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if youtube.get("subscribers"): yt_patch["subscribers"] = youtube["subscribers"]
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if youtube.get("totalVideos"): yt_patch["total_videos"] = youtube["totalVideos"]
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if youtube.get("totalViews"): yt_patch["total_views"] = youtube["totalViews"]
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if youtube.get("publishedAt"): yt_patch["channel_created_date"] = youtube["publishedAt"][:10]
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if youtube.get("description"): yt_patch["channel_description"] = youtube["description"]
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if youtube.get("playlists"): yt_patch["playlists"] = youtube["playlists"]
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if videos:
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yt_patch["top_videos"] = [
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{
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"title": v["title"],
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"views": v["views"],
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"duration": _format_clock(_parse_iso_duration_seconds(v.get("duration", ""))),
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"type": "Short" if "M" not in v.get("duration", "") else "Long",
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"uploaded_ago": _relative_date(v.get("date", "")),
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}
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for v in videos
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]
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diagnosis_result = await LLMService(provider="perplexity").generate(
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youtube_diagnosis_prompt,
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{
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"channel_name": yt_patch.get("channel_name"),
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"subscribers": yt_patch.get("subscribers"),
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"total_videos": yt_patch.get("total_videos"),
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"total_views": yt_patch.get("total_views"),
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"avg_video_length": yt_patch.get("avg_video_length"),
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"upload_frequency": yt_patch.get("upload_frequency"),
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"top_videos": json.dumps(yt_patch.get("top_videos", []), ensure_ascii=False),
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"playlists": json.dumps(yt_patch.get("playlists", []), ensure_ascii=False),
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},
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)
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yt_patch["diagnosis"] = [item.model_dump() for item in diagnosis_result.diagnosis]
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return YouTubeAudit.model_validate(yt_patch).model_dump()
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async def _build_overrides(analysis_run_id: str) -> dict:
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run = await fetchone(
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"SELECT hospital_id, instagram_data_id, facebook_data_id,"
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" naver_blog_data_id, youtube_data_id, gangnam_unni_data_id"
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" FROM analysis_runs WHERE analysis_run_id = %s",
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(analysis_run_id,),
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)
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if not run:
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return {}
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hospital_row = await fetchone(
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"SELECT raw_data, url FROM hospital_baseinfo WHERE hospital_id = %s",
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(run["hospital_id"],),
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)
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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 {}
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hospital["domain"] = (hospital_row or {}).get("url") or ""
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instagram = await fetch_raw("instagram_data", run["instagram_data_id"]) or {}
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facebook = await fetch_raw("facebook_data", run["facebook_data_id"]) or {}
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naver_blog = await fetch_raw("naver_blog_data", run["naver_blog_data_id"]) or {}
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youtube = await fetch_raw("youtube_data", run["youtube_data_id"]) or {}
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gangnam_unni = await fetch_raw("gangnam_unni_data", run["gangnam_unni_data_id"]) or {}
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snapshot: dict = _build_clinic_snapshot(gangnam_unni, hospital)
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yt_patch: dict = await _build_youtube_audit(youtube)
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# ── instagram (KR·EN 계정을 코드에서 구성 → LLM 출력 무시하고 교체) ──────────────
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ig_patch = build_instagram_accounts(
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instagram, hospital.get("instagramEn") or {}, hospital.get("channelLogos") or {},
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)
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# ── facebook (KR=facebook_data, EN=hospital.facebookEn 둘 다 코드 산출, [KR, EN] 순서) ──
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fb_pages = build_facebook_pages(facebook, hospital.get("facebookEn") or {})
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overrides: dict = {}
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if snapshot:
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overrides["clinic_snapshot"] = snapshot
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if ig_patch:
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overrides["instagram_audit"] = {"accounts": ig_patch}
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if fb_pages:
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overrides["facebook_audit"] = {"pages": fb_pages}
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if yt_patch:
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overrides["youtube_audit"] = yt_patch
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return overrides
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def _deep_merge(base: dict, overrides: dict) -> dict:
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for k, v in overrides.items():
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if isinstance(v, dict) and isinstance(base.get(k), dict):
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_deep_merge(base[k], v)
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elif isinstance(v, list) and isinstance(base.get(k), list):
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for i, item in enumerate(v):
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if i < len(base[k]) and isinstance(item, dict) and isinstance(base[k][i], dict):
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_deep_merge(base[k][i], item)
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else:
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base[k] = v
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return base
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def _patch_report(result: ReportOutput, overrides: dict) -> ReportOutput:
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merged = _deep_merge(result.model_dump(), overrides)
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# 인스타 계정은 프롬프트에서 LLM이 []로 두게 했고, 코드가 수집 데이터로 채운다 (데이터 없으면 빈 리스트)
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merged.setdefault("instagram_audit", {})["accounts"] = (overrides.get("instagram_audit") or {}).get("accounts") or []
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# 페북 페이지(KR+EN): _page_patch가 부분 필드만 만들어 그대로 박으면 검증 실패(label/logo 등 누락).
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# LLM이 만든 첫 페이지(보통 KR)를 템플릿으로 복사한 뒤 코드 patch로 인덱스별 덮어쓰기 →
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# 필수 필드는 LLM 디폴트 받고, 수집 수치는 코드 값. EN 누락 버그 회피.
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fb_pages = (overrides.get("facebook_audit") or {}).get("pages") or []
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if fb_pages:
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base_pages = merged.setdefault("facebook_audit", {}).setdefault("pages", [])
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template = base_pages[0] if base_pages else None
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while len(base_pages) < len(fb_pages) and template:
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base_pages.append({**template})
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for i, patch in enumerate(fb_pages):
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if i < len(base_pages):
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base_pages[i].update(patch)
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return ReportOutput(**merged)
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async def run_report_task(analysis_run_id: str) -> None:
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logger.info("[report] start run=%s", analysis_run_id)
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result = await generate_report(analysis_run_id)
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result = _patch_report(result, await _build_overrides(analysis_run_id))
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await save_analysis_report(analysis_run_id, result.model_dump())
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logger.info("[report] done run=%s", analysis_run_id)
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def _patch_plan(result: PlanOutput, logo_desc: str) -> PlanOutput:
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"""brand_guide.channel_branding[].profile_photo 는 LLM 안 맡기고 코드가 박는다
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(모든 채널 동일값 = brand_assets.logo_description). LLM이 fallback 문구 hallucinate 방지."""
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p = result.model_dump()
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for ch in (p.get("brand_guide") or {}).get("channel_branding") or []:
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ch["profile_photo"] = logo_desc
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return PlanOutput(**p)
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async def run_plan_task(analysis_run_id: str) -> None:
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logger.info("[plan] start run=%s", analysis_run_id)
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result = await generate_plan(analysis_run_id)
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# profile_photo 는 brand_assets.logo_description 으로 코드가 박음 (LLM "(가이드 미보유)" 같은 hallucination 차단)
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run = await fetchone("SELECT hospital_id FROM analysis_runs WHERE analysis_run_id = %s", (analysis_run_id,))
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if run:
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hr = await fetchone("SELECT raw_data FROM hospital_baseinfo WHERE hospital_id = %s", (run["hospital_id"],))
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h = json.loads(hr["raw_data"]) if hr and isinstance(hr.get("raw_data"), str) else (hr or {}).get("raw_data") or {}
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logo_desc = ((h.get("brandAssets") or {}).get("logo_description")) or ""
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result = _patch_plan(result, logo_desc)
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await execute(
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"UPDATE analysis_runs SET plan_data = %s WHERE analysis_run_id = %s",
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(json.dumps(result.model_dump(), ensure_ascii=False), analysis_run_id),
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)
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logger.info("[plan] done run=%s", analysis_run_id)
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