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>
39 lines
941 B
Python
39 lines
941 B
Python
import uuid
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from datetime import date, datetime
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from pydantic import BaseModel
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from app.models.weather import ForecastType, AlertSeverity
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class WeatherDataResponse(BaseModel):
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id: uuid.UUID
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project_id: uuid.UUID
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forecast_date: date
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forecast_type: ForecastType
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temperature_high: float | None
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temperature_low: float | None
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precipitation_mm: float | None
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wind_speed_ms: float | None
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weather_code: str | None
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fetched_at: datetime
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model_config = {"from_attributes": True}
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class WeatherAlertResponse(BaseModel):
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id: uuid.UUID
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project_id: uuid.UUID
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task_id: uuid.UUID | None
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alert_date: date
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alert_type: str
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severity: AlertSeverity
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message: str
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is_acknowledged: bool
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created_at: datetime
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model_config = {"from_attributes": True}
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class WeatherForecastSummary(BaseModel):
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forecast: list[WeatherDataResponse]
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active_alerts: list[WeatherAlertResponse]
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