Files
Hyungi Ahn 2f7e083db0 feat: AI 서비스 MLX 듀얼 백엔드 및 모델 최적화
- MLX(맥미니 27B) 우선 → Ollama(조립컴 9B) fallback 구조
- pydantic-settings 기반 config 전환
- health check에 MLX 상태 추가
- 텍스트 모델 qwen3:8b → qwen3.5:9b-q8_0 변경

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-06 23:17:50 +09:00

34 lines
1.3 KiB
Python

from fastapi import APIRouter, HTTPException, Request
from pydantic import BaseModel
from services.report_service import generate_daily_report
from datetime import date
router = APIRouter(tags=["daily_report"])
class DailyReportRequest(BaseModel):
date: str | None = None
project_id: int | None = None
@router.post("/report/daily")
async def daily_report(req: DailyReportRequest, request: Request):
report_date = req.date or date.today().isoformat()
token = request.headers.get("authorization", "").replace("Bearer ", "")
try:
result = await generate_daily_report(report_date, req.project_id, token)
return {"available": True, **result}
except Exception as e:
raise HTTPException(status_code=500, detail="AI 서비스 처리 중 오류가 발생했습니다")
@router.post("/report/preview")
async def report_preview(req: DailyReportRequest, request: Request):
report_date = req.date or date.today().isoformat()
token = request.headers.get("authorization", "").replace("Bearer ", "")
try:
result = await generate_daily_report(report_date, req.project_id, token)
return {"available": True, "preview": True, **result}
except Exception as e:
raise HTTPException(status_code=500, detail="AI 서비스 처리 중 오류가 발생했습니다")