memryx: fix model download bug when using multiple detectors (#20030)

* Add locking for model download files

* ruff format

---------

Co-authored-by: Abinila Siva <abinila.siva@memryx.com>

frigate/detectors/plugins/memryx.py

@@ -177,6 +177,29 @@ class MemryXDetector(DetectionApi):
             logger.error(f"Failed to initialize MemryX model: {e}")
             raise
 
+    def _acquire_file_lock(self, lock_path: str, timeout: int = 60, poll: float = 0.2):
+        """
+        Create an exclusive lock file. Blocks (with polling) until it can acquire,
+        or raises TimeoutError. Uses only stdlib (os.O_EXCL).
+        """
+        start = time.time()
+        while True:
+            try:
+                fd = os.open(lock_path, os.O_CREAT | os.O_EXCL | os.O_RDWR)
+                os.close(fd)
+                return
+            except FileExistsError:
+                if time.time() - start > timeout:
+                    raise TimeoutError(f"Timeout waiting for lock: {lock_path}")
+                time.sleep(poll)
+
+    def _release_file_lock(self, lock_path: str):
+        """Best-effort removal of the lock file."""
+        try:
+            os.remove(lock_path)
+        except FileNotFoundError:
+            pass
+
     def load_yolo_constants(self):
         base = f"{self.cache_dir}/{self.model_folder}"
         # constants for yolov9 post-processing
@@ -188,6 +211,10 @@ class MemryXDetector(DetectionApi):
         if not os.path.exists(self.cache_dir):
             os.makedirs(self.cache_dir, exist_ok=True)
 
+        lock_path = os.path.join(self.cache_dir, f".{self.model_folder}.lock")
+        self._acquire_file_lock(lock_path)
+
+        try:
             # ---------- CASE 1: user provided a custom model path ----------
             if self.memx_model_path:
                 if not self.memx_model_path.endswith(".zip"):
@@ -245,7 +272,9 @@ class MemryXDetector(DetectionApi):
                     self.memx_post_model = None
                 else:
                     # Future model types can optionally use post if present
-                self.memx_post_model = post_candidates[0] if post_candidates else None
+                    self.memx_post_model = (
+                        post_candidates[0] if post_candidates else None
+                    )
 
                 logger.info(f"Using custom model: {self.memx_model_path}")
                 return
@@ -287,7 +316,9 @@ class MemryXDetector(DetectionApi):
                     logger.info(f"Model extracted to {self.cache_dir}.")
 
                 # Re-assign model paths after extraction
-            self.memx_model_path = os.path.join(model_subdir, self.expected_dfp_model)
+                self.memx_model_path = os.path.join(
+                    model_subdir, self.expected_dfp_model
+                )
                 self.memx_post_model = (
                     os.path.join(model_subdir, self.expected_post_model)
                     if self.expected_post_model
@@ -303,7 +334,12 @@ class MemryXDetector(DetectionApi):
                         os.remove(zip_path)
                         logger.info("Cleaned up ZIP file after extraction.")
                     except Exception as e:
-                    logger.warning(f"Failed to remove downloaded zip {zip_path}: {e}")
+                        logger.warning(
+                            f"Failed to remove downloaded zip {zip_path}: {e}"
+                        )
+
+        finally:
+            self._release_file_lock(lock_path)
 
     def send_input(self, connection_id, tensor_input: np.ndarray):
         """Pre-process (if needed) and send frame to MemryX input queue"""