176 lines
8.3 KiB
Python
176 lines
8.3 KiB
Python
import json
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import logging
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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.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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**{
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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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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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}
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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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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 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 doctors: snapshot["staff_count"] = len(doctors)
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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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# ── instagram ─────────────────────────────────────────────────────────────
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ig_patch: dict = {}
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if instagram.get("username"): ig_patch["handle"] = instagram["username"]
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if instagram.get("posts"): ig_patch["posts"] = instagram["posts"]
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if instagram.get("followers"): ig_patch["followers"] = instagram["followers"]
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if instagram.get("following"): ig_patch["following"] = instagram["following"]
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if instagram.get("bio"): ig_patch["bio"] = instagram["bio"]
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if instagram.get("username"): ig_patch["profile_link"] = f"https://www.instagram.com/{instagram['username']}/"
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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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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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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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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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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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