diff --git a/app.py b/app.py
index da4a073..666730f 100644
--- a/app.py
+++ b/app.py
@@ -13,6 +13,8 @@ import queue
 import os
 import requests
 from urllib.parse import urlparse  # Added import
+import asyncio  # Ensure asyncio is imported
+import psutil  # Added import
 
 app = FastAPI()
 
@@ -47,6 +49,10 @@ logging.basicConfig(
 # Ensure the models directory exists
 os.makedirs("models", exist_ok=True)
 
+# Add constants for heartbeat
+HEARTBEAT_INTERVAL = 2  # seconds
+WORKER_TIMEOUT_MS = 10000
+
 @app.websocket("/")
 async def detect(websocket: WebSocket):
     import asyncio
@@ -102,6 +108,7 @@ async def detect(websocket: WebSocket):
                 break
 
     async def process_streams():
+        global model, class_names  # Added line
         logging.info("Started processing streams")
         try:
             while True:
@@ -141,8 +148,149 @@ async def detect(websocket: WebSocket):
         except Exception as e:
             logging.error(f"Error in process_streams: {e}")
 
+    async def send_heartbeat():
+        while True:
+            try:
+                cpu_usage = psutil.cpu_percent()
+                memory_usage = psutil.virtual_memory().percent
+                if torch.cuda.is_available():
+                    gpu_usage = torch.cuda.memory_allocated() / (1024 ** 2)  # Convert to MB
+                    gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2)  # Convert to MB
+                else:
+                    gpu_usage = None
+                    gpu_memory_usage = None
+            
+                camera_connections = [
+                    {
+                        "cameraIdentifier": camera_id,
+                        "modelId": stream['modelId'],
+                        "modelName": stream['modelName'],
+                        "online": True
+                    }
+                    for camera_id, stream in streams.items()
+                ]
+            
+                state_report = {
+                    "type": "stateReport",
+                    "cpuUsage": cpu_usage,
+                    "memoryUsage": memory_usage,
+                    "gpuUsage": gpu_usage,
+                    "gpuMemoryUsage": gpu_memory_usage,
+                    "cameraConnections": camera_connections
+                }
+                await websocket.send_text(json.dumps(state_report))
+                logging.debug("Sent stateReport as heartbeat")
+                await asyncio.sleep(HEARTBEAT_INTERVAL)
+            except Exception as e:
+                logging.error(f"Error sending stateReport heartbeat: {e}")
+                break
+
+    async def on_message():
+        global model, class_names  # Changed from nonlocal to global
+        while True:
+            msg = await websocket.receive_text()
+            logging.debug(f"Received message: {msg}")
+            data = json.loads(msg)
+            msg_type = data.get("type")
+
+            if msg_type == "subscribe":
+                payload = data.get("payload", {})
+                camera_id = payload.get("cameraIdentifier")
+                rtsp_url = payload.get("rtspUrl")
+                model_url = payload.get("modelUrl")
+                modelId = payload.get("modelId")
+                modelName = payload.get("modelName")
+    
+                if model_url:
+                    print(f"Downloading model from {model_url}")
+                    parsed_url = urlparse(model_url)
+                    filename = os.path.basename(parsed_url.path)    
+                    model_filename = os.path.join("models", filename)
+                    # Download the model
+                    response = requests.get(model_url, stream=True)
+                    if response.status_code == 200:
+                        with open(model_filename, 'wb') as f:
+                            for chunk in response.iter_content(chunk_size=8192):
+                                f.write(chunk)
+                        logging.info(f"Downloaded model from {model_url} to {model_filename}")
+                        model = YOLO(model_filename)
+                        if torch.cuda.is_available():
+                            model.to('cuda')
+                        class_names = model.names
+                    else:
+                        logging.error(f"Failed to download model from {model_url}")
+                        continue
+                if camera_id and rtsp_url:
+                    if camera_id not in streams and len(streams) < max_streams:
+                        cap = cv2.VideoCapture(rtsp_url)
+                        if not cap.isOpened():
+                            logging.error(f"Failed to open RTSP stream for camera {camera_id}")
+                            continue
+                        buffer = queue.Queue(maxsize=1)
+                        stop_event = threading.Event()
+                        thread = threading.Thread(target=frame_reader, args=(camera_id, cap, buffer, stop_event))
+                        thread.daemon = True
+                        thread.start()
+                        streams[camera_id] = {
+                            'cap': cap,
+                            'buffer': buffer,
+                            'thread': thread,
+                            'rtsp_url': rtsp_url,
+                            'stop_event': stop_event,
+                            'modelId': modelId,
+                            'modelName': modelName
+                        }
+                        logging.info(f"Subscribed to camera {camera_id} with modelId {modelId}, modelName {modelName} and URL {rtsp_url}")
+                    elif camera_id and camera_id in streams:
+                        stream = streams.pop(camera_id)
+                        stream['cap'].release()
+                        logging.info(f"Unsubscribed from camera {camera_id}")
+            elif msg_type == "unsubscribe":
+                payload = data.get("payload", {})
+                camera_id = payload.get("cameraIdentifier")
+                if camera_id and camera_id in streams:
+                    stream = streams.pop(camera_id)
+                    stream['cap'].release()
+                    logging.info(f"Unsubscribed from camera {camera_id}")
+            elif msg_type == "requestState":
+                # Handle state request
+                cpu_usage = psutil.cpu_percent()
+                memory_usage = psutil.virtual_memory().percent
+                if torch.cuda.is_available():
+                    gpu_usage = torch.cuda.memory_allocated() / (1024 ** 2)  # Convert to MB
+                    gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2)  # Convert to MB
+                else:
+                    gpu_usage = None
+                    gpu_memory_usage = None
+                
+                camera_connections = [
+                    {
+                        "cameraIdentifier": camera_id,
+                        "modelId": stream['modelId'],
+                        "modelName": stream['modelName'],
+                        "online": True
+                    }
+                    for camera_id, stream in streams.items()
+                ]
+                
+                state_report = {
+                    "type": "stateReport",
+                    "cpuUsage": cpu_usage,
+                    "memoryUsage": memory_usage,
+                    "gpuUsage": gpu_usage,
+                    "gpuMemoryUsage": gpu_memory_usage,
+                    "cameraConnections": camera_connections
+                }
+                await websocket.send_text(json.dumps(state_report))
+            else:
+                logging.error(f"Unknown message type: {msg_type}")
+
     await websocket.accept()
     task = asyncio.create_task(process_streams())
+    heartbeat_task = asyncio.create_task(send_heartbeat())
+    message_task = asyncio.create_task(on_message())
+
+    await asyncio.gather(heartbeat_task, message_task)
 
     model = None
     model_path = None