Update asset cost parameters

aixplain/factories/model_factory.py

@@ -65,7 +65,7 @@ def _create_model_from_response(cls, response: Dict) -> Model:
             response["name"],
             supplier=response["supplier"],
             api_key=response["api_key"],
-            pricing=response["pricing"],
+            cost=response["pricing"],
             function=Function(response["function"]["id"]),
             parameters=parameters,
             is_subscribed=True if "subscription" in response else False,
@@ -404,9 +404,11 @@ def onboard_model(cls, model_id: Text, image_tag: Text, image_hash: Text, api_ke
         message = "Your onboarding request has been submitted to an aiXplain specialist for finalization. We will notify you when the process is completed."
         logging.info(message)
         return response
-    
+
     @classmethod
-    def deploy_huggingface_model(cls, name: Text, hf_repo_id: Text, hf_token: Optional[Text] = "", api_key: Optional[Text] = None) -> Dict:
+    def deploy_huggingface_model(
+        cls, name: Text, hf_repo_id: Text, hf_token: Optional[Text] = "", api_key: Optional[Text] = None
+    ) -> Dict:
         """Onboards and deploys a Hugging Face large language model.
 
         Args:
@@ -433,20 +435,16 @@ def deploy_huggingface_model(cls, name: Text, hf_repo_id: Text, hf_token: Option
                 "sourceLanguage": "en",
             },
             "source": "huggingface",
-            "onboardingParams": {
-                "hf_model_name": model_name,
-                "hf_supplier": supplier,
-                "hf_token": hf_token
-            }
+            "onboardingParams": {"hf_model_name": model_name, "hf_supplier": supplier, "hf_token": hf_token},
         }
         response = _request_with_retry("post", deploy_url, headers=headers, json=body)
         logging.debug(response.text)
         response_dicts = json.loads(response.text)
         return response_dicts
-    
+
     @classmethod
     def get_huggingface_model_status(cls, model_id: Text, api_key: Optional[Text] = None):
-        """Gets the on-boarding status of a Hugging Face model with ID MODEL_ID. 
+        """Gets the on-boarding status of a Hugging Face model with ID MODEL_ID.
 
         Args:
             model_id (Text): The model's ID as returned by DEPLOY_HUGGINGFACE_MODEL
@@ -466,6 +464,6 @@ def get_huggingface_model_status(cls, model_id: Text, api_key: Optional[Text] =
             "status": response_dicts["status"],
             "name": response_dicts["name"],
             "id": response_dicts["id"],
-            "pricing": response_dicts["pricing"]
+            "pricing": response_dicts["pricing"],
         }
-        return ret_dict
+        return ret_dict

aixplain/modules/asset.py

@@ -36,7 +36,7 @@ def __init__(
         version: Text = "1.0",
         license: Optional[License] = None,
         privacy: Privacy = Privacy.PRIVATE,
-        cost: float = 0,
+        cost: Optional[Union[Dict, float]] = None,
     ) -> None:
         """Create an Asset with the necessary information
 
@@ -46,6 +46,7 @@ def __init__(
             description (Text): Description of the Asset
             supplier (Union[Dict, Text, Supplier, int], optional): supplier of the asset. Defaults to "aiXplain".
             version (Optional[Text], optional): asset version. Defaults to "1.0".
+            cost (Optional[Union[Dict, float]], optional): asset price. Defaults to None.
         """
         self.id = id
         self.name = name

aixplain/modules/metric.py

@@ -61,12 +61,10 @@ def __init__(
             supplier (Text): author of the Metric
             is_reference_required (bool): does the metric use reference
             is_source_required (bool): does the metric use source
-            cost (float): cost of the metric
+            cost (float): price of the metric
             normalization_options(list, [])
             **additional_info: Any additional Metric info to be saved
         """
-
-
         super().__init__(id, name, description="", supplier=supplier, version="1.0", cost=cost)
         self.is_source_required = is_source_required
         self.is_reference_required = is_reference_required
@@ -76,7 +74,7 @@ def __init__(
 
     def __repr__(self) -> str:
         return f"<Metric {self.name}>"
-    
+
     def add_normalization_options(self, normalization_options: List[str]):
         """Add a given set of normalization options to be used while benchmarking
 
@@ -85,7 +83,12 @@ def add_normalization_options(self, normalization_options: List[str]):
         """
         self.normalization_options.append(normalization_options)
 
-    def run(self, hypothesis: Optional[Union[str, List[str]]]=None, source: Optional[Union[str, List[str]]]=None, reference: Optional[Union[str, List[str]]]=None):
+    def run(
+        self,
+        hypothesis: Optional[Union[str, List[str]]] = None,
+        source: Optional[Union[str, List[str]]] = None,
+        reference: Optional[Union[str, List[str]]] = None,
+    ):
         """Run the metric to calculate the scores.
 
         Args:
@@ -94,6 +97,7 @@ def run(self, hypothesis: Optional[Union[str, List[str]]]=None, source: Optional
             reference (Optional[Union[str, List[str]]], optional): Can give a single reference or a list of references for metric calculation. Defaults to None.
         """
         from aixplain.factories.model_factory import ModelFactory
+
         model = ModelFactory.get(self.id)
         payload = {
             "function": self.function,
@@ -115,4 +119,3 @@ def run(self, hypothesis: Optional[Union[str, List[str]]]=None, source: Optional
                 reference = [[ref] for ref in reference]
             payload["references"] = reference
         return model.run(payload)
-

aixplain/modules/model.py

@@ -48,6 +48,7 @@ class Model(Asset):
         function (Text, optional): model AI function. Defaults to None.
         url (str): URL to run the model.
         backend_url (str): URL of the backend.
+        pricing (Dict, optional): model price. Defaults to None.
         **additional_info: Any additional Model info to be saved
     """
 
@@ -61,6 +62,7 @@ def __init__(
         version: Optional[Text] = None,
         function: Optional[Text] = None,
         is_subscribed: bool = False,
+        cost: Optional[Dict] = None,
         **additional_info,
     ) -> None:
         """Model Init
@@ -74,9 +76,10 @@ def __init__(
             version (Text, optional): version of the model. Defaults to "1.0".
             function (Text, optional): model AI function. Defaults to None.
             is_subscribed (bool, optional): Is the user subscribed. Defaults to False.
+            cost (Dict, optional): model price. Defaults to None.
             **additional_info: Any additional Model info to be saved
         """
-        super().__init__(id, name, description, supplier, version)
+        super().__init__(id, name, description, supplier, version, cost=cost)
         self.api_key = api_key
         self.additional_info = additional_info
         self.url = config.MODELS_RUN_URL
@@ -264,6 +267,7 @@ def check_finetune_status(self, after_epoch: Optional[int] = None):
         """
         from aixplain.enums.asset_status import AssetStatus
         from aixplain.modules.finetune.status import FinetuneStatus
+
         headers = {"x-api-key": self.api_key, "Content-Type": "application/json"}
         resp = None
         try:
@@ -274,15 +278,15 @@ def check_finetune_status(self, after_epoch: Optional[int] = None):
             finetune_status = AssetStatus(resp["finetuneStatus"])
             model_status = AssetStatus(resp["modelStatus"])
             logs = sorted(resp["logs"], key=lambda x: float(x["epoch"]))
-            
+
             target_epoch = None
             if after_epoch is not None:
                 logs = [log for log in logs if float(log["epoch"]) > after_epoch]
                 if len(logs) > 0:
                     target_epoch = float(logs[0]["epoch"])
             elif len(logs) > 0:
                 target_epoch = float(logs[-1]["epoch"])
-            
+
             if target_epoch is not None:
                 log = None
                 for log_ in logs:
@@ -294,7 +298,7 @@ def check_finetune_status(self, after_epoch: Optional[int] = None):
                                 log["trainLoss"] = log_["trainLoss"]
                             if log_["evalLoss"] is not None:
                                 log["evalLoss"] = log_["evalLoss"]
-                
+
                 status = FinetuneStatus(
                     status=finetune_status,
                     model_status=model_status,

tests/functional/general_assets/asset_functional_test.py

@@ -82,8 +82,8 @@ def test_model_sort():
         prev_model = models[idx - 1]
         model = models[idx]
 
-        prev_model_price = prev_model.additional_info["pricing"]["price"]
-        model_price = model.additional_info["pricing"]["price"]
+        prev_model_price = prev_model.cost["price"]
+        model_price = model.cost["price"]
         assert prev_model_price >= model_price