update: formatting for perplexity.py

src/pipecat/services/perplexity.py

@@ -12,13 +12,14 @@
     OpenAILLMService,
 )
 
+
 class PerplexityLLMService(OpenAILLMService):
     """
     A service for interacting with Perplexity's API using an OpenAI-compatible interface.
-    
+
     This service connects to Perplexity's chat completions endpoint and processes the
     aggregated conversation context similarly to GrokLLMService.
-    
+
     Args:
         api_key (str): The API key for accessing Perplexity's API.
         base_url (str, optional): The base URL for the Perplexity API. Defaults to "https://api.perplexity.ai".
@@ -31,6 +32,7 @@ class PerplexityLLMService(OpenAILLMService):
         top_p (float, optional): Nucleus sampling parameter (default: 0.9).
         **kwargs: Additional keyword arguments passed to OpenAILLMService.
     """
+
     def __init__(
         self,
         *,
@@ -68,7 +70,7 @@ async def _process_context(self, context: OpenAILLMContext):
         """
         Process the conversation context by sending it to Perplexity's API and
         incorporating the assistant's reply into the context.
-        
+
         This method also accumulates token usage metrics from the Perplexity response.
         """
         # Reset counters and mark processing start
@@ -95,12 +97,14 @@ async def _process_context(self, context: OpenAILLMContext):
                 "Content-Type": "application/json",
             }
             url = f"{self.base_url.rstrip('/')}/chat/completions"
-            
+
             async with aiohttp.ClientSession() as session:
                 async with session.post(url, headers=headers, json=payload) as response:
                     if response.status != 200:
                         error_text = await response.text()
-                        raise Exception(f"Perplexity API call failed (status {response.status}): {error_text}")
+                        raise Exception(
+                            f"Perplexity API call failed (status {response.status}): {error_text}"
+                        )
                     data = await response.json()
 
             # Extract the assistant's reply from the response
@@ -111,7 +115,9 @@ async def _process_context(self, context: OpenAILLMContext):
             usage = data.get("usage", {})
             self._prompt_tokens = usage.get("prompt_tokens", 0)
             self._completion_tokens = usage.get("completion_tokens", 0)
-            self._total_tokens = usage.get("total_tokens", self._prompt_tokens + self._completion_tokens)
+            self._total_tokens = usage.get(
+                "total_tokens", self._prompt_tokens + self._completion_tokens
+            )
 
             # Push the updated context as a frame
             frame = OpenAILLMContextFrame(context)