253 lines
7.6 KiB
Python
253 lines
7.6 KiB
Python
import logging
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import sys
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from contextlib import asynccontextmanager
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from pathlib import Path
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import torch
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from fastapi import FastAPI, Request
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from fastapi.middleware.cors import CORSMiddleware
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from slowapi import Limiter, _rate_limit_exceeded_handler
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from slowapi.util import get_remote_address
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from slowapi.errors import RateLimitExceeded
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from sqlalchemy import text
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from core.config import settings
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from core.database import init_db
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from core.model_manager import ModelManager
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from core.cleanup import run_scheduled_cleanup
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from api import auth, jobs, tts, cache, metrics, users, voice_designs
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from apscheduler.schedulers.asyncio import AsyncIOScheduler
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logging.basicConfig(
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level=getattr(logging, settings.LOG_LEVEL.upper()),
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.StreamHandler(sys.stdout),
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logging.FileHandler(settings.LOG_FILE)
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]
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)
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logger = logging.getLogger(__name__)
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def get_user_identifier(request: Request) -> str:
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from jose import jwt
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from core.config import settings
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auth_header = request.headers.get("Authorization", "")
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if auth_header.startswith("Bearer "):
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token = auth_header.split(" ")[1]
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try:
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payload = jwt.decode(token, settings.SECRET_KEY, algorithms=[settings.ALGORITHM])
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user_id = payload.get("sub")
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if user_id:
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return f"user:{user_id}"
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except Exception:
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pass
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return get_remote_address(request)
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limiter = Limiter(key_func=get_user_identifier)
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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logger.info("Starting Qwen3-TTS Backend Service...")
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logger.info(f"Model base path: {settings.MODEL_BASE_PATH}")
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logger.info(f"Cache directory: {settings.CACHE_DIR}")
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logger.info(f"Output directory: {settings.OUTPUT_DIR}")
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logger.info(f"Device: {settings.MODEL_DEVICE}")
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try:
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settings.validate()
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logger.info("Configuration validated successfully")
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except Exception as e:
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logger.error(f"Configuration validation failed: {e}")
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raise
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try:
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init_db()
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logger.info("Database initialized successfully")
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except Exception as e:
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logger.error(f"Database initialization failed: {e}")
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raise
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try:
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from core.init_admin import init_superuser
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init_superuser()
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except Exception as e:
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logger.error(f"Superuser initialization failed: {e}")
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raise
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try:
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model_manager = await ModelManager.get_instance()
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await model_manager.load_model("custom-voice")
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logger.info("Preloaded custom-voice model")
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except Exception as e:
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logger.warning(f"Model preload failed: {e}")
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scheduler = AsyncIOScheduler()
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scheduler.add_job(
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run_scheduled_cleanup,
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'interval',
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hours=6,
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args=[str(settings.DATABASE_URL)],
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id='cleanup_task'
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)
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scheduler.start()
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logger.info("Background cleanup scheduler started (runs every 6 hours)")
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yield
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logger.info("Shutting down Qwen3-TTS Backend Service...")
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scheduler.shutdown()
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logger.info("Scheduler shutdown completed")
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try:
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model_manager = await ModelManager.get_instance()
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await model_manager.unload_model()
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logger.info("Model cleanup completed")
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except Exception as e:
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logger.error(f"Model cleanup failed: {e}")
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app = FastAPI(
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title="Qwen3-TTS-WebUI Backend API",
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description="Backend service for Qwen3-TTS-WebUI text-to-speech system",
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version="0.1.0",
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lifespan=lifespan
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)
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app.state.limiter = limiter
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app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)
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if settings.LOG_LEVEL == "debug":
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["http://localhost:5173", "http://127.0.0.1:5173"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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app.include_router(auth.router)
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app.include_router(jobs.router)
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app.include_router(tts.router)
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app.include_router(cache.router)
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app.include_router(metrics.router)
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app.include_router(users.router)
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app.include_router(voice_designs.router)
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@app.get("/health")
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async def health_check():
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from core.batch_processor import BatchProcessor
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from core.database import SessionLocal
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gpu_available = torch.cuda.is_available()
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gpu_memory_used_mb = 0
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gpu_memory_total_mb = 0
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if gpu_available:
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gpu_memory_used_mb = torch.cuda.memory_allocated(0) / 1024**2
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gpu_memory_total_mb = torch.cuda.get_device_properties(0).total_memory / 1024**2
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model_manager = await ModelManager.get_instance()
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current_model, _ = await model_manager.get_current_model()
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batch_processor = await BatchProcessor.get_instance()
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queue_length = await batch_processor.get_queue_length()
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database_connected = True
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try:
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db = SessionLocal()
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db.execute(text("SELECT 1"))
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db.close()
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except Exception as e:
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logger.error(f"Database health check failed: {e}")
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database_connected = False
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cache_dir_writable = True
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try:
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test_file = Path(settings.CACHE_DIR) / ".health_check"
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test_file.write_text("test")
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test_file.unlink()
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except Exception as e:
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logger.error(f"Cache directory health check failed: {e}")
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cache_dir_writable = False
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output_dir_writable = True
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try:
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test_file = Path(settings.OUTPUT_DIR) / ".health_check"
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test_file.write_text("test")
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test_file.unlink()
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except Exception as e:
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logger.error(f"Output directory health check failed: {e}")
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output_dir_writable = False
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critical_issues = []
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if not database_connected:
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critical_issues.append("database_disconnected")
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if not cache_dir_writable:
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critical_issues.append("cache_dir_not_writable")
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if not output_dir_writable:
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critical_issues.append("output_dir_not_writable")
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minor_issues = []
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if not gpu_available:
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minor_issues.append("gpu_not_available")
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if queue_length > 50:
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minor_issues.append("queue_congested")
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backends_status = {}
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try:
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from core.tts_service import TTSServiceFactory
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try:
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local_backend = await TTSServiceFactory.get_backend("local")
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local_health = await local_backend.health_check()
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backends_status["local"] = local_health
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except Exception as e:
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backends_status["local"] = {"available": False, "error": str(e)}
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backends_status["aliyun"] = {
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"available": True,
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"region": settings.ALIYUN_REGION,
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"note": "Requires user API key configuration"
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}
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except Exception as e:
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logger.error(f"Backend health check failed: {e}")
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backends_status = {"error": str(e)}
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if critical_issues:
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status = "unhealthy"
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elif minor_issues:
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status = "degraded"
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else:
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status = "healthy"
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return {
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"status": status,
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"gpu_available": gpu_available,
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"gpu_memory_used_mb": gpu_memory_used_mb,
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"gpu_memory_total_mb": gpu_memory_total_mb,
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"queue_length": queue_length,
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"active_model": current_model,
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"database_connected": database_connected,
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"cache_dir_writable": cache_dir_writable,
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"output_dir_writable": output_dir_writable,
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"backends": backends_status,
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"issues": {
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"critical": critical_issues,
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"minor": minor_issues
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}
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}
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(
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"main:app",
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host=settings.HOST,
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port=settings.PORT,
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workers=settings.WORKERS,
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log_level=settings.LOG_LEVEL.lower()
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)
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