dynamic model loading

.gitignore

@@ -1,2 +1,3 @@
 
 /__pycache__
+models

app.py

@@ -10,6 +10,9 @@ import json
 import logging
 import threading
 import queue
+import os
+import requests
+from urllib.parse import urlparse  # Added import
 
 app = FastAPI()
 
@@ -41,6 +44,9 @@ logging.basicConfig(
     ]
 )
 
+# Ensure the models directory exists
+os.makedirs("models", exist_ok=True)
+
 @app.websocket("/")
 async def detect(websocket: WebSocket):
     import asyncio
@@ -136,6 +142,9 @@ async def detect(websocket: WebSocket):
     await websocket.accept()
     task = asyncio.create_task(process_streams())
 
+    model = None
+    model_path = None
+
     try:
         while True:
             try:
@@ -144,7 +153,27 @@ async def detect(websocket: WebSocket):
                 data = json.loads(msg)
                 camera_id = data.get("cameraIdentifier")
                 rtsp_url = data.get("rtspUrl")
+                model_url = data.get("modelUrl")
     
+                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)
@@ -191,4 +220,7 @@ async def detect(websocket: WebSocket):
             stream['buffer'].queue.clear()
             logging.info(f"Released camera {camera_id} and cleaned up resources")
         streams.clear()
+        if model_path and os.path.exists(model_path):
+            os.remove(model_path)
+            logging.info(f"Deleted model file {model_path}")
         logging.info("WebSocket connection closed")