feat: add deployment monitor page

- Add /deploy_status API endpoint for system status check
- Create Monitor.tsx component with real-time status display
- Support CUDA, FFmpeg, Whisper model status monitoring
- Auto-refresh every 30 seconds with manual refresh option

backend/app/routers/config.py

@@ -42,4 +42,37 @@ async def sys_health():
 
 @router.get("/sys_check")
 async def sys_check():
-    return R.success()
+    return R.success()
+
+
+@router.get("/deploy_status")
+async def deploy_status():
+    """返回部署监控所需的所有状态信息"""
+    import torch
+    import os
+    
+    # CUDA 状态
+    cuda_available = torch.cuda.is_available()
+    cuda_info = {
+        "available": cuda_available,
+        "version": torch.version.cuda if cuda_available else None,
+        "gpu_name": torch.cuda.get_device_name(0) if cuda_available else None,
+    }
+    
+    # Whisper 模型状态
+    model_size = os.getenv("WHISPER_MODEL_SIZE", "base")
+    transcriber_type = os.getenv("TRANSCRIBER_TYPE", "fast-whisper")
+    
+    # FFmpeg 状态
+    try:
+        ensure_ffmpeg_or_raise()
+        ffmpeg_ok = True
+    except:
+        ffmpeg_ok = False
+    
+    return R.success(data={
+        "backend": {"status": "running", "port": int(os.getenv("BACKEND_PORT", 8483))},
+        "cuda": cuda_info,
+        "whisper": {"model_size": model_size, "transcriber_type": transcriber_type},
+        "ffmpeg": {"available": ffmpeg_ok},
+    })