clinic_overview , youtube analysis 정리
parent
d1293f9188
commit
eed57729d9
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@ -1,7 +1,7 @@
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import os
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from pydantic import BaseModel
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from common.utils import get_env
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from integrations.llm.schemas.report import ReportInput, ReportOutput
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from integrations.llm.schemas.report import ReportInput, ReportOutput, YouTubeDiagnosisInput, YouTubeDiagnosisOutput
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from integrations.llm.schemas.plan import PlanInput, PlanOutput
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from integrations.llm.schemas.market import (
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MarketCompetitorsInput, MarketCompetitorsOutput,
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@ -80,3 +80,10 @@ market_target_audience_prompt = Prompt(
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input_class=MarketTargetAudienceInput,
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output_class=MarketTargetAudienceOutput,
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)
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youtube_diagnosis_prompt = Prompt(
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file_name="youtube_diagnosis_prompt.txt",
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prompt_model="REPORT_MODEL",
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input_class=YouTubeDiagnosisInput,
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output_class=YouTubeDiagnosisOutput,
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)
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@ -70,18 +70,12 @@ class RegistryData(BaseModel):
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class ClinicSnapshot(BaseModel):
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name: str
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name_en: str
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established: str
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years_in_business: int
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staff_count: int
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lead_doctor: LeadDoctor
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overall_rating: float
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total_reviews: int
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price_range: PriceRange
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certifications: list[str]
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media_appearances: list[str]
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medical_tourism: list[str]
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location: str
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nearest_station: str
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phone: str
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domain: str
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logo_images: LogoImages | None = None
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@ -137,7 +131,6 @@ class YouTubeAudit(BaseModel):
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avg_video_length: str
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upload_frequency: str
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channel_created_date: str
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subscriber_rank: str
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channel_description: str
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linked_urls: list[LinkedUrl]
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playlists: list[str]
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@ -345,3 +338,20 @@ class MarketingReport(BaseModel):
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ReportOutput = MarketingReport
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# --- YouTubeDiagnosis ---
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class YouTubeDiagnosisInput(BaseModel):
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channel_name: str | None = None
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subscribers: int | None = None
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total_videos: int | None = None
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total_views: int | None = None
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avg_video_length: str | None = None
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upload_frequency: str | None = None
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top_videos: str | None = None
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playlists: str | None = None
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class YouTubeDiagnosisOutput(BaseModel):
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diagnosis: list[DiagnosisItem]
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@ -0,0 +1,24 @@
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다음은 성형외과/피부과 유튜브 채널 데이터입니다.
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채널명: {channel_name}
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구독자 수: {subscribers}
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총 영상 수: {total_videos}
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총 조회수: {total_views}
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평균 영상 길이: {avg_video_length}
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업로드 주기: {upload_frequency}
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인기 영상 목록: {top_videos}
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플레이리스트: {playlists}
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위 데이터를 바탕으로 이 채널의 마케팅 문제점과 개선사항을 진단해줘.
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각 항목은 category(진단 카테고리), detail(상세 설명), severity(critical/warning/info) 형식의 JSON 배열로 출력해줘.
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진단 카테고리들은 다음과 같아. :
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구독자 대비 조회수 비율,
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최근 롱폼 조회수,
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Shorts 조회수,
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업로드 빈도,
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콘텐츠 톤앤매너,
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썸네일 디자인,
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최고 성과 Shorts
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출처 번호([1], [2] 등)는 굳이 포함하지 마.
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@ -79,7 +79,17 @@ class YouTubeClient:
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if resp and resp.is_success:
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videos = resp.json().get("items", [])[:10]
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return {"channelId": channel_id, "channel": channel, "videos": videos}
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playlists: list[dict] = []
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resp = await http_request(
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HTTPMethod.GET,
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url=f"{YT}/playlists",
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params={"part": "snippet", "channelId": channel_id, "maxResults": 50, "key": self.api_key},
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label="yt-playlists",
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)
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if resp and resp.is_success:
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playlists = resp.json().get("items", [])
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return {"channelId": channel_id, "channel": channel, "videos": videos, "playlists": playlists}
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async def get_channel(self, url: str) -> dict | None:
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raw = await self.fetch_channel(url)
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@ -109,6 +119,11 @@ class YouTubeClient:
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}
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for v in raw["videos"]
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],
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"playlists": [
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p.get("snippet", {}).get("title")
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for p in raw["playlists"]
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if p.get("snippet", {}).get("title")
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],
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}
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async def search_channels(self, query: str, max_results: int = 3) -> list[str]:
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@ -68,18 +68,12 @@ class RegistryData(CamelModel):
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class ClinicSnapshot(CamelModel):
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name: str
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name_en: str
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established: str
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years_in_business: int
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staff_count: int
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lead_doctor: LeadDoctor
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overall_rating: float
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total_reviews: int
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price_range: PriceRange
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certifications: list[str]
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media_appearances: list[str]
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medical_tourism: list[str]
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location: str
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nearest_station: str
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phone: str
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domain: str
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logo_images: LogoImages | None = None
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@ -131,7 +125,6 @@ class YouTubeAudit(CamelModel):
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avg_video_length: str
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upload_frequency: str
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channel_created_date: str
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subscriber_rank: str
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channel_description: str
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linked_urls: list[LinkedUrl]
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playlists: list[str]
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@ -1,10 +1,12 @@
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import json
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import logging
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import os
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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
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from integrations.llm.schemas.report import ReportOutput
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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 integrations.llm.schemas.plan import PlanOutput
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from models.status import AnalysisStatus
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@ -84,33 +86,14 @@ async def generate_plan(analysis_run_id: str) -> PlanOutput:
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return await LLMService(provider="perplexity").generate(plan_prompt, input_data)
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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 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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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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def _build_clinic_snapshot(gangnam_unni: dict, hospital: dict) -> dict:
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snapshot: dict = {}
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# ── gangnam_unni ──────────────────────────────────────────────────────────
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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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@ -123,6 +106,144 @@ async def _build_overrides(analysis_run_id: str) -> dict:
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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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return ClinicSnapshot.model_validate(snapshot).model_dump()
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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 ─────────────────────────────────────────────────────────────
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ig_patch: dict = {}
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@ -143,26 +264,6 @@ async def _build_overrides(analysis_run_id: str) -> dict:
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if facebook.get("categories"): fb_patch["category"] = ", ".join(facebook["categories"])
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if facebook.get("website"): fb_patch["linked_domain"] = facebook["website"]
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# ── youtube ───────────────────────────────────────────────────────────────
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yt_patch: dict = {}
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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("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": v.get("duration"),
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"type": "Short" if "M" not in v.get("duration", "") else "Long",
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"uploaded_ago": v.get("date", "")[:10],
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}
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for v in youtube["videos"]
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]
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overrides: dict = {}
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if snapshot:
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@ -225,6 +326,7 @@ async def run_report_task(analysis_run_id: str) -> None:
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if await _is_mock(analysis_run_id):
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logger.info("[report] mock mode run=%s", analysis_run_id)
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result = _load_mock_report()
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result.youtube_audit.linked_urls = []
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else:
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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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