Run black formatter.

examples/simple_poem.py

@@ -2,6 +2,7 @@
 
 Please see the poem.py for more complex use cases.
 """
+
 from bespokelabs import curator
 
 # Use GPT-4o-mini for this example.
@@ -15,6 +16,10 @@
 print(poem)
 
 # Note that we can also pass a list of prompts to generate multiple responses.
-poems = llm(["Write a sonnet about the importance of data in AI.",
-              "Write a haiku about the importance of data in AI."])
-print(poems)
+poems = llm(
+    [
+        "Write a sonnet about the importance of data in AI.",
+        "Write a haiku about the importance of data in AI.",
+    ]
+)
+print(poems)

src/bespokelabs/curator/__init__.py

@@ -1,3 +1,3 @@
 from .dataset import Dataset
 from .llm.llm import LLM
-from .llm.simple_llm import SimpleLLM
+from .llm.simple_llm import SimpleLLM

src/bespokelabs/curator/llm/simple_llm.py

@@ -4,29 +4,30 @@
 
 
 class SimpleLLM:
-  """A simpler interface for the LLM class.
-  
-  Usage:
-    llm = SimpleLLM(model_name="gpt-4o-mini")
-    llm("Do you know about the bitter lesson?")
-    llm(["What is the capital of France?", "What is the capital of Germany?"])
-  For more complex use cases (e.g. structured outputs and custom prompt functions), see the LLM class.
-  """
-  def __init__(self, model_name: str, backend: str = "openai"):
-    self._model_name = model_name
-    self._backend = backend
+    """A simpler interface for the LLM class.
 
-  def __call__(self, prompt: Union[str, List[str]]) -> Union[str, List[str]]:
-    prompt_list = [prompt] if isinstance(prompt, str) else prompt
-    dataset: Dataset = Dataset.from_dict({"prompt": prompt_list})
+    Usage:
+      llm = SimpleLLM(model_name="gpt-4o-mini")
+      llm("Do you know about the bitter lesson?")
+      llm(["What is the capital of France?", "What is the capital of Germany?"])
+    For more complex use cases (e.g. structured outputs and custom prompt functions), see the LLM class.
+    """
 
-    llm = LLM(
-      prompt_func=lambda row: row['prompt'],
-      model_name=self._model_name,
-      response_format=None,
-      backend=self._backend
-    )
-    response = llm(dataset)
-    if isinstance(prompt, str):
-      return response["response"][0]
-    return response["response"]
+    def __init__(self, model_name: str, backend: str = "openai"):
+        self._model_name = model_name
+        self._backend = backend
+
+    def __call__(self, prompt: Union[str, List[str]]) -> Union[str, List[str]]:
+        prompt_list = [prompt] if isinstance(prompt, str) else prompt
+        dataset: Dataset = Dataset.from_dict({"prompt": prompt_list})
+
+        llm = LLM(
+            prompt_func=lambda row: row["prompt"],
+            model_name=self._model_name,
+            response_format=None,
+            backend=self._backend,
+        )
+        response = llm(dataset)
+        if isinstance(prompt, str):
+            return response["response"][0]
+        return response["response"]