diff --git a/supervisely_integration/serve/src/main.py b/supervisely_integration/serve/src/main.py
index d31fff8..d54c23b 100644
--- a/supervisely_integration/serve/src/main.py
+++ b/supervisely_integration/serve/src/main.py
@@ -114,7 +114,7 @@ def load_on_device(
         # variable for storing image ids from previous inference iterations
         self.previous_image_id = None
         # dict for storing model variables to avoid unnecessary calculations
-        self.cache = Cache(maxsize=100, ttl=5 * 60)
+        self.model_cache = Cache(maxsize=100, ttl=5 * 60)
         # set variables for smart tool mode
         self._inference_image_lock = threading.Lock()
         self._inference_image_cache = Cache(maxsize=100, ttl=60)
@@ -139,9 +139,9 @@ def model_meta(self):
 
     def set_image_data(self, input_image, settings):
         if settings["input_image_id"] != self.previous_image_id:
-            if settings["input_image_id"] not in self.cache:
+            if settings["input_image_id"] not in self.model_cache:
                 self.predictor.set_image(input_image)
-                self.cache.set(
+                self.model_cache.set(
                     settings["input_image_id"],
                     {
                         "features": self.predictor.features,
@@ -150,7 +150,7 @@ def set_image_data(self, input_image, settings):
                     },
                 )
             else:
-                cached_data = self.cache.get(settings["input_image_id"])
+                cached_data = self.model_cache.get(settings["input_image_id"])
                 self.predictor.features = cached_data["features"]
                 self.predictor.input_size = cached_data["input_size"]
                 self.predictor.original_size = cached_data["original_size"]
@@ -287,15 +287,15 @@ def predict(self, image_path: str, settings: Dict[str, Any]) -> List[sly.nn.Pred
             self.set_image_data(input_image, settings)
             # get predicted masks
             if (
-                settings["input_image_id"] in self.cache
+                settings["input_image_id"] in self.model_cache
                 and (
-                    self.cache.get(settings["input_image_id"]).get("previous_bbox")
+                    self.model_cache.get(settings["input_image_id"]).get("previous_bbox")
                     == bbox_coordinates
                 ).all()
                 and self.previous_image_id == settings["input_image_id"]
             ):
                 # get mask from previous predicton and use at as an input for new prediction
-                mask_input = self.cache.get(settings["input_image_id"])["mask_input"]
+                mask_input = self.model_cache.get(settings["input_image_id"])["mask_input"]
                 masks, scores, logits = self.predictor.predict(
                     point_coords=point_coordinates,
                     point_labels=point_labels,
@@ -311,12 +311,12 @@ def predict(self, image_path: str, settings: Dict[str, Any]) -> List[sly.nn.Pred
                     multimask_output=False,
                 )
             # save bbox ccordinates and mask to cache
-            if settings["input_image_id"] in self.cache:
+            if settings["input_image_id"] in self.model_cache:
                 input_image_id = settings["input_image_id"]
-                cached_data = self.cache.get(input_image_id)
+                cached_data = self.model_cache.get(input_image_id)
                 cached_data["previous_bbox"] = bbox_coordinates
                 cached_data["mask_input"] = logits[0]
-                self.cache.set(input_image_id, cached_data)
+                self.model_cache.set(input_image_id, cached_data)
             # update previous_image_id variable
             self.previous_image_id = settings["input_image_id"]
             mask = masks[0]
@@ -326,7 +326,6 @@ def predict(self, image_path: str, settings: Dict[str, Any]) -> List[sly.nn.Pred
     def serve(self):
         super().serve()
         server = self._app.get_server()
-        self.add_cache_endpoint(server)
 
         @server.post("/smart_segmentation")
         def smart_segmentation(response: Response, request: Request):
@@ -387,9 +386,9 @@ def smart_segmentation(response: Response, request: Request):
                     smtool_state,
                     api,
                     app_dir,
-                    cache_load_img=self.download_image,
-                    cache_load_frame=self.download_frame,
-                    cache_load_img_hash=self.download_image_by_hash,
+                    cache_load_img=self.cache.download_image,
+                    cache_load_frame=self.cache.download_frame,
+                    cache_load_img_hash=self.cache.download_image_by_hash,
                 )
                 self._inference_image_cache.set(hash_str, image_np)
             else:
@@ -470,7 +469,7 @@ def smart_segmentation(response: Response, request: Request):
         def is_online(response: Response, request: Request):
             response = {"is_online": True}
             return response
-        
+
         @server.post("/smart_segmentation_batched")
         def smart_segmentation_batched(response: Response, request: Request):
             response_batch = {}
@@ -487,7 +486,6 @@ def smart_segmentation_batched(response: Response, request: Request):
             return response_batch
 
 
-
 model = SegmentAnythingHQModel(
     use_gui=True,
     custom_inference_settings="./supervisely_integration/serve/custom_settings.yaml",