148 lines
4.8 KiB
Python
148 lines
4.8 KiB
Python
from datetime import datetime
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from typing import List, Optional
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from fastapi import APIRouter, Depends, HTTPException, Query, Body
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from pydantic import BaseModel
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from sqlalchemy.orm import Session
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from db.crud import (
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create_alarm,
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get_alarm_stats,
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get_alarms,
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update_alarm,
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)
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from db.models import get_db
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from inference.pipeline import get_pipeline
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router = APIRouter(prefix="/api/alarms", tags=["告警管理"])
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class AlarmUpdateRequest(BaseModel):
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llm_checked: Optional[bool] = None
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llm_result: Optional[str] = None
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processed: Optional[bool] = None
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@router.get("", response_model=List[dict])
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def list_alarms(
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camera_id: Optional[int] = None,
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event_type: Optional[str] = None,
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limit: int = Query(default=100, le=1000),
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offset: int = Query(default=0, ge=0),
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db: Session = Depends(get_db),
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):
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alarms = get_alarms(db, camera_id=camera_id, event_type=event_type, limit=limit, offset=offset)
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return [
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{
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"id": alarm.id,
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"camera_id": alarm.camera_id,
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"roi_id": alarm.roi_id,
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"event_type": alarm.event_type,
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"confidence": alarm.confidence,
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"snapshot_path": alarm.snapshot_path,
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"llm_checked": alarm.llm_checked,
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"llm_result": alarm.llm_result,
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"processed": alarm.processed,
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"created_at": alarm.created_at.isoformat() if alarm.created_at else None,
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}
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for alarm in alarms
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]
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@router.get("/stats")
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def get_alarm_statistics(db: Session = Depends(get_db)):
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stats = get_alarm_stats(db)
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return stats
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@router.get("/{alarm_id}", response_model=dict)
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def get_alarm(alarm_id: int, db: Session = Depends(get_db)):
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from db.crud import get_alarms
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alarms = get_alarms(db, limit=1)
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alarm = next((a for a in alarms if a.id == alarm_id), None)
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if not alarm:
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raise HTTPException(status_code=404, detail="告警不存在")
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return {
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"id": alarm.id,
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"camera_id": alarm.camera_id,
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"roi_id": alarm.roi_id,
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"event_type": alarm.event_type,
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"confidence": alarm.confidence,
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"snapshot_path": alarm.snapshot_path,
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"llm_checked": alarm.llm_checked,
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"llm_result": alarm.llm_result,
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"processed": alarm.processed,
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"created_at": alarm.created_at.isoformat() if alarm.created_at else None,
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}
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@router.put("/{alarm_id}")
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def update_alarm_status(
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alarm_id: int,
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request: AlarmUpdateRequest = Body(...),
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db: Session = Depends(get_db),
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):
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alarm = update_alarm(db, alarm_id, llm_checked=request.llm_checked, llm_result=request.llm_result, processed=request.processed)
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if not alarm:
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raise HTTPException(status_code=404, detail="告警不存在")
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return {"message": "更新成功"}
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@router.post("/{alarm_id}/llm-check")
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async def trigger_llm_check(alarm_id: int, db: Session = Depends(get_db)):
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from db.crud import get_alarms
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alarms = get_alarms(db, limit=1)
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alarm = next((a for a in alarms if a.id == alarm_id), None)
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if not alarm:
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raise HTTPException(status_code=404, detail="告警不存在")
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if not alarm.snapshot_path or not os.path.exists(alarm.snapshot_path):
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raise HTTPException(status_code=400, detail="截图不存在")
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try:
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from config import get_config
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config = get_config()
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if not config.llm.enabled:
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raise HTTPException(status_code=400, detail="大模型功能未启用")
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import base64
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with open(alarm.snapshot_path, "rb") as f:
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img_base64 = base64.b64encode(f.read()).decode("utf-8")
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from openai import OpenAI
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client = OpenAI(
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api_key=config.llm.api_key,
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base_url=config.llm.base_url,
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)
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prompt = """分析这张监控截图，判断是否存在异常行为。请简要说明：
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1. 画面中是否有人
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2. 人员位置和行为
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3. 是否存在异常"""
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response = client.chat.completions.create(
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model=config.llm.model,
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messages=[
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{
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"role": "user",
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"content": [
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{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img_base64}"}},
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{"type": "text", "text": prompt},
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],
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}
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],
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)
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result = response.choices[0].message.content
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update_alarm(db, alarm_id, llm_checked=True, llm_result=result)
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return {"message": "大模型分析完成", "result": result}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"大模型调用失败: {str(e)}")
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@router.get("/queue/size")
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def get_event_queue_size():
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pipeline = get_pipeline()
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return {"size": len(pipeline.event_queue), "max_size": pipeline.config.inference.event_queue_maxlen}
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