2a4950d8a0
소형 건설업체(100억 미만)를 위한 AI 기반 토목공사 통합관리 플랫폼 Backend (FastAPI): - SQLAlchemy 모델 13개 (users, projects, wbs, tasks, daily_reports, reports, inspections, quality, weather, permits, rag, settings) - API 라우터 11개 (auth, projects, tasks, daily_reports, reports, inspections, weather, rag, kakao, permits, settings) - Services: Claude AI 래퍼, CPM Gantt 계산, 기상청 API, RAG(pgvector), 카카오 Skill API - Alembic 마이그레이션 (pgvector 포함) - pytest 테스트 (CPM, 날씨 경보) Frontend (Next.js 15): - 11개 페이지 (대시보드, 프로젝트, Gantt, 일보, 검측, 품질, 날씨, 인허가, RAG, 설정) - TanStack Query + Zustand + Tailwind CSS 인프라: - Docker Compose (PostgreSQL pgvector + backend + frontend) - 한국어 README 및 설치 가이드 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
100 lines
2.7 KiB
Python
100 lines
2.7 KiB
Python
"""Weekly and monthly report generation."""
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from app.services.ai_engine import complete
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from app.services.prompts.report import (
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WEEKLY_SYSTEM_PROMPT, MONTHLY_SYSTEM_PROMPT,
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build_weekly_prompt, build_monthly_prompt,
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)
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async def generate_weekly_report(
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project_name: str,
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period_start: str,
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period_end: str,
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daily_reports: list,
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overall_progress_pct: float,
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weather_alerts: list,
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) -> tuple[str, dict]:
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"""
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Generate weekly report text and structured data.
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Returns (ai_text, content_json).
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"""
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daily_summaries = [
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{
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"date": str(r.report_date),
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"work_content": r.work_content or "",
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}
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for r in daily_reports
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]
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weather_issues = [f"{a.alert_date}: {a.message}" for a in weather_alerts]
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# Calculate stats
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total_workers = sum(
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sum(r.workers_count.values()) if r.workers_count else 0
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for r in daily_reports
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)
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prompt = build_weekly_prompt(
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project_name=project_name,
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period_start=period_start,
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period_end=period_end,
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daily_summaries=daily_summaries,
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overall_progress_pct=overall_progress_pct,
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weather_issues=weather_issues,
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)
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ai_text = await complete(
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messages=[{"role": "user", "content": prompt}],
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system=WEEKLY_SYSTEM_PROMPT,
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temperature=0.3,
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)
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content_json = {
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"period_start": period_start,
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"period_end": period_end,
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"overall_progress_pct": overall_progress_pct,
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"daily_count": len(daily_reports),
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"total_workers": total_workers,
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"weather_alert_count": len(weather_alerts),
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}
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return ai_text, content_json
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async def generate_monthly_report(
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project_name: str,
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period_start: str,
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period_end: str,
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daily_reports: list,
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overall_progress_pct: float,
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) -> tuple[str, dict]:
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"""Generate monthly report text and structured data."""
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# Group dailies by week for summary
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weekly_summaries = []
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for r in daily_reports[::7]: # Sample weekly
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if r.work_content:
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weekly_summaries.append(f"- {r.report_date}: {r.work_content[:80]}...")
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prompt = build_monthly_prompt(
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project_name=project_name,
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period_start=period_start,
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period_end=period_end,
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weekly_summaries=weekly_summaries,
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overall_progress_pct=overall_progress_pct,
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)
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ai_text = await complete(
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messages=[{"role": "user", "content": prompt}],
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system=MONTHLY_SYSTEM_PROMPT,
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temperature=0.3,
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)
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content_json = {
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"period_start": period_start,
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"period_end": period_end,
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"overall_progress_pct": overall_progress_pct,
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"daily_count": len(daily_reports),
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}
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return ai_text, content_json
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