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e19b795 84dbb52 e19b795 c303abd 84dbb52 b4627df 22a85f1 e19b795 22a85f1 e19b795 22a85f1 a3a6b45 e19b795 22a85f1 e19b795 b4627df 22a85f1 e19b795 c303abd 84dbb52 c303abd 84dbb52 c303abd e19b795 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | from fastapi import FastAPI
# from prometheus_client import metrics
from ai.depth.depth_anything import DepthAnything
from ai.detectors.yolo_detector import YOLO_Detector
from config.settings import AppConfig
from backend.api.routers.metrics import metrics_asgi_app
from infra.system_metrics import log_system_metrics
from backend.api.routers import camera_stream
from backend.api.routers import dashboard_stream
from backend.api.routers import health
from contextlib import asynccontextmanager
from infra.logger_structlog import StructLogger
import asyncio
import mlflow
from backend.utils.experiment import log_config
import torch
from redis.asyncio import Redis
from huggingface_hub import hf_hub_download
import redis.asyncio as aioredis
@asynccontextmanager
async def lifespan(app: FastAPI):
"""
This is on_event("startup") new alternative, Make sure you load models here.
"""
settings = AppConfig()
logger = StructLogger(settings=settings)
logger.info("Starting Server.... ")
asyncio.create_task(log_system_metrics(logger, logger_interval_sec=settings.intervals.system_metrics_seconds))
# Using this way to can store data. it is acts as a dict which holds instances
app.state.detection_model = YOLO_Detector(settings.yolo.model_path)
app.state.depth_model = DepthAnything(encoder=settings.depth.encoder, depth_model_path=settings.depth.model_path, DEVICE="cuda")
# safety_detection_path = hf_hub_download(repo_id="e1250/safety_detection", filename="yolo_smoke_fire.pt")
app.state.safety_detection_model = YOLO_Detector(settings.security_detector.model_path)
app.state.logger = logger
app.state.settings = settings
# app.state.camera_metadata = {}
# app.state.dashboard_clients = set()
# Redis(host="localhost", port=6379, db=0, decode_responses=True)
app.state.redis = aioredis.from_url("redis://localhost:6379", db=0, decode_responses=True)
# Cnecking connection to redis.
# Thinking of moving this to the health check.
try:
await app.state.redis.ping()
logger.info("Redis connected successfully...")
except Exception as e:
logger.error(f"Failed to connect to Redis: {e}")
raise e
# Each camera should have its tracker to be able to work fine.
# app.state.camera_trackers = {}
yield
logger.warn("Shutting down the server....")
torch.cuda.empty_cache()
await app.state.redis.close()
# You can remove connections and release gpu here .
mlflow.set_tracking_uri("sqlite:///config/logs/mlflow.db")
mlflow.set_experiment("realtime-detection-system")
mlflow.enable_system_metrics_logging()
app = FastAPI(
title="Tracking System Backend",
description="real-time frame processing API",
version="0.1.0",
lifespan=lifespan
)
# Routes
app.mount("/metrics", metrics_asgi_app) # Starting Prometheus server attached to my server.
app.include_router(camera_stream.router, prefix="/detectors")
app.include_router(dashboard_stream.router, prefix="/dashboard")
app.include_router(health.router, prefix="/health")
@app.get("/")
async def root():
return {"status": "Real-Time tracker backend is running..."} |