[PROJ] Format code with Black

Author: actions-user

tests/test_asynchronous.py

@@ -26,7 +26,7 @@ async def assert_query_response(query_response):
 async def text_evaluator():
     fn_name, version_number, fn_desc = await abuild(
         task_description="Evaluate the sentiment of the given text. Provide a json object with 'sentiment' and 'explanation' keys.",
-        multimodal=False
+        multimodal=False,
     )
     return fn_name, version_number, fn_desc
 
@@ -44,7 +44,7 @@ async def test_text_aquery(text_evaluator):
 async def image_evaluator():
     fn_name, version_number, fn_desc = await abuild(
         task_description="Describe the contents of the given images. Provide a json object with 'description' and 'objects' keys.",
-        multimodal=True
+        multimodal=True,
     )
     return fn_name, version_number, fn_desc
 
@@ -69,7 +69,7 @@ async def test_image_aquery(image_evaluator):
 async def text_and_image_evaluator():
     fn_name, version_number, fn_desc = await abuild(
         task_description="Evaluate, solve and arrive at a numerical answer for the image provided. Provide a json object with 'answer' and 'explanation' keys.",
-        multimodal=True
+        multimodal=True,
     )
     return fn_name, version_number, fn_desc
 

tests/test_batching.py

@@ -9,7 +9,7 @@
 def ml_task_evaluator():
     fn_name, version_number, _ = build(
         task_description="I want to evaluate the feasibility of a machine learning task. Give me a json object with three keys - 'feasibility', 'justification', and 'suggestions'.",
-        multimodal=False
+        multimodal=False,
     )
     return fn_name, version_number
 
@@ -18,23 +18,29 @@ def ml_task_evaluator():
 def ml_task_inputs():
     return [
         {"text_input": "I want to train a model to predict house prices using the Boston Housing dataset hosted on Kaggle."},
-        {"text_input": "I want to train a model to classify digits using the MNIST dataset hosted on Kaggle using a Google Colab notebook."},
+        {
+            "text_input": "I want to train a model to classify digits using the MNIST dataset hosted on Kaggle using a Google Colab notebook."
+        },
     ]
 
 
 @pytest.fixture
 def image_evaluator():
     fn_name, version_number, _ = build(
         task_description="Describe the contents of the given images. Provide a json object with 'description' and 'objects' keys.",
-        multimodal=True
+        multimodal=True,
     )
     return fn_name, version_number
 
 
 @pytest.fixture
 def image_inputs():
     return [
-        {"images_input": ["https://www.integratedtreatmentservices.co.uk/wp-content/uploads/2013/12/Objects-of-Reference.jpg"]},
+        {
+            "images_input": [
+                "https://www.integratedtreatmentservices.co.uk/wp-content/uploads/2013/12/Objects-of-Reference.jpg"
+            ]
+        },
         {"images_input": ["https://t4.ftcdn.net/jpg/05/70/90/23/360_F_570902339_kNj1reH40GFXakTy98EmfiZHci2xvUCS.jpg"]},
     ]
 
@@ -72,4 +78,4 @@ def test_batch_query_image(image_evaluator, image_inputs):
         assert isinstance(query_response["latency_ms"], float)
 
         output = query_response["output"]
-        assert set(output.keys()) == {"description", "objects"}
+        assert set(output.keys()) == {"description", "objects"}

tests/test_synchronous.py

@@ -25,7 +25,7 @@ def assert_query_response(query_response):
 def text_evaluator():
     fn_name, version_number, fn_desc = build(
         task_description="Evaluate the sentiment of the given text. Provide a json object with 'sentiment' and 'explanation' keys.",
-        multimodal=False
+        multimodal=False,
     )
     return fn_name, version_number, fn_desc
 
@@ -42,7 +42,7 @@ def test_text_query(text_evaluator):
 def image_evaluator():
     fn_name, version_number, fn_desc = build(
         task_description="Describe the contents of the given images. Provide a json object with 'description' and 'objects' keys.",
-        multimodal=True
+        multimodal=True,
     )
     return fn_name, version_number, fn_desc
 
@@ -66,7 +66,7 @@ def test_image_query(image_evaluator):
 def text_and_image_evaluator():
     fn_name, version_number, fn_desc = build(
         task_description="Evaluate, solve and arrive at a numerical answer for the image provided. Perform any additional things if instructed. Provide a json object with 'answer' and 'explanation' keys.",
-        multimodal=True
+        multimodal=True,
     )
     return fn_name, version_number, fn_desc
 

weco/client.py

@@ -31,7 +31,7 @@ class WecoAI:
     ----------
     api_key : str
         The API key used for authentication.
-    
+
     timeout : float
         The timeout for the HTTP requests in seconds. Default is 120.0.
 
@@ -160,17 +160,19 @@ def _process_query_response(self, response: Dict[str, Any]) -> Dict[str, Any]:
             "latency_ms": response["latency_ms"],
         }
 
-    def _build(self, task_description: str, multimodal: bool, is_async: bool) -> Union[Tuple[str, int, str], Coroutine[Any, Any, Tuple[str, int, str]]]:
+    def _build(
+        self, task_description: str, multimodal: bool, is_async: bool
+    ) -> Union[Tuple[str, int, str], Coroutine[Any, Any, Tuple[str, int, str]]]:
         """Internal method to handle both synchronous and asynchronous build requests.
 
         Parameters
         ----------
         task_description : str
             A description of the task for which the function is being built.
-        
+
         multimodal : bool
             Whether the function is multimodal or not.
-        
+
         is_async : bool
             Whether to perform an asynchronous request.
 
@@ -212,7 +214,7 @@ async def abuild(self, task_description: str, multimodal: bool = False) -> Tuple
         ----------
         task_description : str
             A description of the task for which the function is being built.
-        
+
         multimodal : bool, optional
             Whether the function is multimodal or not (default is False).
 
@@ -230,7 +232,7 @@ def build(self, task_description: str, multimodal: bool = False) -> Tuple[str, i
         ----------
         task_description : str
             A description of the task for which the function is being built.
-        
+
         multimodal : bool, optional
             Whether the function is multimodal or not (default is False).
 
@@ -385,7 +387,12 @@ def _validate_query(self, text_input: str, images_input: List[str]) -> List[Dict
         return image_info
 
     def _query(
-        self, is_async: bool, fn_name: str, version_number: Optional[int], text_input: Optional[str], images_input: Optional[List[str]]
+        self,
+        is_async: bool,
+        fn_name: str,
+        version_number: Optional[int],
+        text_input: Optional[str],
+        images_input: Optional[List[str]],
     ) -> Union[Dict[str, Any], Coroutine[Any, Any, Dict[str, Any]]]:
         """Internal method to handle both synchronous and asynchronous query requests.
 
@@ -442,7 +449,11 @@ async def _async_query():
             return self._process_query_response(response=response)
 
     async def aquery(
-        self, fn_name: str, version_number: Optional[int] = -1, text_input: Optional[str] = "", images_input: Optional[List[str]] = []
+        self,
+        fn_name: str,
+        version_number: Optional[int] = -1,
+        text_input: Optional[str] = "",
+        images_input: Optional[List[str]] = [],
     ) -> Dict[str, Any]:
         """Asynchronously queries a function with the given function ID and input.
 
@@ -463,9 +474,17 @@ async def aquery(
             A dictionary containing the output of the function, the number of input tokens, the number of output tokens,
             and the latency in milliseconds.
         """
-        return await self._query(fn_name=fn_name, version_number=version_number, text_input=text_input, images_input=images_input, is_async=True)
-
-    def query(self, fn_name: str, version_number: Optional[int] = -1, text_input: Optional[str] = "", images_input: Optional[List[str]] = []) -> Dict[str, Any]:
+        return await self._query(
+            fn_name=fn_name, version_number=version_number, text_input=text_input, images_input=images_input, is_async=True
+        )
+
+    def query(
+        self,
+        fn_name: str,
+        version_number: Optional[int] = -1,
+        text_input: Optional[str] = "",
+        images_input: Optional[List[str]] = [],
+    ) -> Dict[str, Any]:
         """Synchronously queries a function with the given function ID and input.
 
         Parameters
@@ -485,9 +504,13 @@ def query(self, fn_name: str, version_number: Optional[int] = -1, text_input: Op
             A dictionary containing the output of the function, the number of input tokens, the number of output tokens,
             and the latency in milliseconds.
         """
-        return self._query(fn_name=fn_name, version_number=version_number, text_input=text_input, images_input=images_input, is_async=False)
+        return self._query(
+            fn_name=fn_name, version_number=version_number, text_input=text_input, images_input=images_input, is_async=False
+        )
 
-    def batch_query(self, fn_name: str, batch_inputs: List[Dict[str, Any]], version_number: Optional[int] = -1) -> List[Dict[str, Any]]:
+    def batch_query(
+        self, fn_name: str, batch_inputs: List[Dict[str, Any]], version_number: Optional[int] = -1
+    ) -> List[Dict[str, Any]]:
         """Batch queries a function version with a list of inputs.
 
         Parameters
@@ -499,7 +522,7 @@ def batch_query(self, fn_name: str, batch_inputs: List[Dict[str, Any]], version_
             A list of inputs for the functions to query. The input must be a dictionary containing the data to be processed. e.g.,
             when providing for a text input, the dictionary should be {"text_input": "input text"}, for an image input, the dictionary should be {"images_input": ["url1", "url2", ...]}
             and for a combination of text and image inputs, the dictionary should be {"text_input": "input text", "images_input": ["url1", "url2", ...]}.
-        
+
         version_number : int, optional
             The version number of the function to query. If not provided, the latest version will be used. Pass -1 to use the latest version.
 
@@ -509,11 +532,11 @@ def batch_query(self, fn_name: str, batch_inputs: List[Dict[str, Any]], version_
             A list of dictionaries, each containing the output of a function query,
             in the same order as the input queries.
         """
+
         async def run_queries():
-            tasks = list(map(
-                lambda fn_input: self.aquery(fn_name=fn_name, version_number=version_number, **fn_input),
-                batch_inputs
-            ))
+            tasks = list(
+                map(lambda fn_input: self.aquery(fn_name=fn_name, version_number=version_number, **fn_input), batch_inputs)
+            )
             return await asyncio.gather(*tasks)
 
         return asyncio.run(run_queries())

weco/functional.py

@@ -48,7 +48,11 @@ async def abuild(task_description: str, multimodal: bool = False, api_key: str =
 
 
 def query(
-    fn_name: str, version_number: Optional[int] = -1, text_input: Optional[str] = "", images_input: Optional[List[str]] = [], api_key: Optional[str] = None
+    fn_name: str,
+    version_number: Optional[int] = -1,
+    text_input: Optional[str] = "",
+    images_input: Optional[List[str]] = [],
+    api_key: Optional[str] = None,
 ) -> Dict[str, Any]:
     """Queries a function synchronously with the given function ID and input.
 
@@ -77,7 +81,11 @@ def query(
 
 
 async def aquery(
-    fn_name: str, version_number: Optional[int] = -1, text_input: Optional[str] = "", images_input: Optional[List[str]] = [], api_key: Optional[str] = None
+    fn_name: str,
+    version_number: Optional[int] = -1,
+    text_input: Optional[str] = "",
+    images_input: Optional[List[str]] = [],
+    api_key: Optional[str] = None,
 ) -> Dict[str, Any]:
     """Queries a function asynchronously with the given function ID and input.
 
@@ -101,7 +109,9 @@ async def aquery(
         and the latency in milliseconds.
     """
     client = WecoAI(api_key=api_key)
-    response = await client.aquery(fn_name=fn_name, version_number=version_number, text_input=text_input, images_input=images_input)
+    response = await client.aquery(
+        fn_name=fn_name, version_number=version_number, text_input=text_input, images_input=images_input
+    )
     return response