Passing the countinference and secret successfully

Author: bigbitbus

inference/core/managers/base.py

@@ -85,7 +85,7 @@ def add_model(
         logger.debug("ModelManager - model initialisation...")
 
         try:
-            model_class = self.model_registry.get_model(resolved_identifier, api_key)
+            model_class = self.model_registry.get_model(resolved_identifier, api_key, countinference=countinference, service_secret=service_secret)
             model = model_class(
                 model_id=model_id,
                 api_key=api_key,

inference/core/managers/decorators/base.py

@@ -56,6 +56,8 @@ def add_model(
         api_key: str,
         model_id_alias: Optional[str] = None,
         endpoint_type: ModelEndpointType = ModelEndpointType.ORT,
+        countinference: bool = None,
+        service_secret: str = None,
     ):
         """Adds a model to the manager.
 
@@ -71,6 +73,8 @@ def add_model(
             api_key,
             model_id_alias=model_id_alias,
             endpoint_type=endpoint_type,
+            countinference=countinference,
+            service_secret=service_secret,
         )
 
     async def infer_from_request(

inference/core/managers/decorators/fixed_size_cache.py

@@ -38,6 +38,8 @@ def add_model(
         api_key: str,
         model_id_alias: Optional[str] = None,
         endpoint_type: ModelEndpointType = ModelEndpointType.ORT,
+        countinference: bool = None,
+        service_secret: str = None,
     ) -> None:
         """Adds a model to the manager and evicts the least recently used if the cache is full.
 
@@ -95,6 +97,8 @@ def add_model(
                 api_key,
                 model_id_alias=model_id_alias,
                 endpoint_type=endpoint_type,
+                countinference=countinference,
+                service_secret=service_secret,
             )
         except Exception as error:
             logger.debug(

inference/core/registries/base.py

@@ -17,7 +17,7 @@ def __init__(self, registry_dict) -> None:
         """
         self.registry_dict = registry_dict
 
-    def get_model(self, model_type: str, model_id: str) -> Model:
+    def get_model(self, model_type: str, model_id: str, countinference: bool = None, service_secret: str = None) -> Model:
         """Returns the model class based on the given model type.
 
         Args:

inference/core/registries/roboflow.py

@@ -68,7 +68,7 @@ class RoboflowModelRegistry(ModelRegistry):
     then returns a model class based on the model type.
     """
 
-    def get_model(self, model_id: ModelID, api_key: str) -> Model:
+    def get_model(self, model_id: ModelID, api_key: str, countinference: bool = None, service_secret: str = None) -> Model:
         """Returns the model class based on the given model id and API key.
 
         Args:
@@ -81,7 +81,7 @@ def get_model(self, model_id: ModelID, api_key: str) -> Model:
         Raises:
             ModelNotRecognisedError: If the model type is not supported or found.
         """
-        model_type = get_model_type(model_id, api_key)
+        model_type = get_model_type(model_id, api_key, countinference=countinference, service_secret=service_secret)
         logger.debug(f"Model type: {model_type}")
 
         if model_type not in self.registry_dict:
@@ -120,6 +120,8 @@ def _check_if_api_key_has_access_to_model(
 def get_model_type(
     model_id: ModelID,
     api_key: Optional[str] = None,
+    countinference: Optional[bool] = None,
+    service_secret: Optional[str] = None,
 ) -> Tuple[TaskType, ModelType]:
     """Retrieves the model type based on the given model ID and API key.
 
@@ -179,6 +181,8 @@ def get_model_type(
         api_data = get_roboflow_model_data(
             api_key=api_key,
             model_id=model_id,
+            countinference=countinference,
+            service_secret=service_secret,
             endpoint_type=ModelEndpointType.ORT,
             device_id=GLOBAL_DEVICE_ID,
         ).get("ort")
@@ -187,6 +191,8 @@ def get_model_type(
         api_data = get_roboflow_instant_model_data(
             api_key=api_key,
             model_id=model_id,
+            countinference=countinference,
+            service_secret=service_secret,
         )
         project_task_type = api_data.get("taskType", "object-detection")
     if api_data is None:

inference/core/roboflow_api.py

@@ -261,14 +261,15 @@ def get_roboflow_model_data(
         if INTERNAL_WEIGHTS_URL_SUFFIX == "serverless":
             if countinference is not None:
                 params.append(("countinference", str(countinference).lower()))
-            if service_secret is not None:
-                params.append(("service_secret", service_secret))
+                if service_secret is not None:
+                    params.append(("service_secret", service_secret))
 
         api_base_url = urllib.parse.urljoin(API_BASE_URL, INTERNAL_WEIGHTS_URL_SUFFIX)
         api_url = _add_params_to_url(
             url=f"{api_base_url}/{endpoint_type.value}/{model_id}",
             params=params,
         )
+        logger.debug(f"Fetching model data from Roboflow API with URL: {api_url}.")
         api_data = _get_from_url(url=api_url)
         cache.set(
             api_data_cache_key,
@@ -307,14 +308,15 @@ def get_roboflow_instant_model_data(
         if INTERNAL_WEIGHTS_URL_SUFFIX == "serverless":
             if countinference is not None:
                 params.append(("countinference", str(countinference).lower()))
-            if service_secret is not None:
-                params.append(("service_secret", service_secret))
+                if service_secret is not None:
+                    params.append(("service_secret", service_secret))
 
         api_base_url = urllib.parse.urljoin(API_BASE_URL, INTERNAL_WEIGHTS_URL_SUFFIX)
         api_url = _add_params_to_url(
             url=f"{api_base_url}/getWeights",
             params=params,
         )
+        logger.debug(f"Fetching instant model data from Roboflow API with URL: {api_url}.")
         api_data = _get_from_url(url=api_url)
         cache.set(
             api_data_cache_key,