autopep8 formatting

src/dailyai/pipeline/aggregators.py

@@ -57,7 +57,8 @@ class ResponseAggregator(FrameProcessor):
             # Sometimes VAD triggers quickly on and off. If we don't get any transcription,
             # it creates empty LLM message queue frames
             if len(self.aggregation) > 0:
-                self.messages.append({"role": self._role, "content": self.aggregation})
+                self.messages.append(
+                    {"role": self._role, "content": self.aggregation})
                 self.aggregation = ""
                 yield self._end_frame()
                 yield LLMMessagesQueueFrame(self.messages)
@@ -110,7 +111,8 @@ class LLMContextAggregator(AIService):
         self.pass_through = pass_through
 
     async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        # We don't do anything with non-text frames, pass it along to next in the pipeline.
+        # We don't do anything with non-text frames, pass it along to next in
+        # the pipeline.
         if not isinstance(frame, TextFrame):
             yield frame
             return
@@ -132,7 +134,8 @@ class LLMContextAggregator(AIService):
             # though we check it above
             self.sentence += frame.text
             if self.sentence.endswith((".", "?", "!")):
-                self.messages.append({"role": self.role, "content": self.sentence})
+                self.messages.append(
+                    {"role": self.role, "content": self.sentence})
                 self.sentence = ""
                 yield LLMMessagesQueueFrame(self.messages)
         else:
@@ -144,17 +147,24 @@ class LLMContextAggregator(AIService):
 
 class LLMUserContextAggregator(LLMContextAggregator):
     def __init__(
-        self, messages: list[dict], bot_participant_id=None, complete_sentences=True
-    ):
+            self,
+            messages: list[dict],
+            bot_participant_id=None,
+            complete_sentences=True):
         super().__init__(
-            messages, "user", bot_participant_id, complete_sentences, pass_through=False
-        )
+            messages,
+            "user",
+            bot_participant_id,
+            complete_sentences,
+            pass_through=False)
 
 
 class LLMAssistantContextAggregator(LLMContextAggregator):
     def __init__(
-        self, messages: list[dict], bot_participant_id=None, complete_sentences=True
-    ):
+            self,
+            messages: list[dict],
+            bot_participant_id=None,
+            complete_sentences=True):
         super().__init__(
             messages,
             "assistant",
@@ -328,7 +338,8 @@ class ParallelPipeline(FrameProcessor):
                 continue
             seen_ids.add(id(frame))
 
-            # Skip passing along EndParallelPipeQueueFrame, because we use them for our own flow control.
+            # Skip passing along EndParallelPipeQueueFrame, because we use them
+            # for our own flow control.
             if not isinstance(frame, EndPipeFrame):
                 yield frame
 

src/dailyai/pipeline/frames.py

@@ -13,7 +13,7 @@ class ControlFrame(Frame):
     # Control frames should contain no instance data, so
     # equality is based solely on the class.
     def __eq__(self, other):
-        return type(other) == self.__class__
+        return isinstance(other, self.__class__)
 
 
 class StartFrame(ControlFrame):

src/dailyai/pipeline/merge_pipeline.py

@@ -6,7 +6,8 @@ from dailyai.pipeline.pipeline import Pipeline
 class SequentialMergePipeline(Pipeline):
     """This class merges the sink queues from a list of pipelines. Frames from
     each pipeline's sink are merged in the order of pipelines in the list."""
-    def __init__(self, pipelines:List[Pipeline]):
+
+    def __init__(self, pipelines: List[Pipeline]):
         super().__init__([])
         self.pipelines = pipelines
 
@@ -14,7 +15,9 @@ class SequentialMergePipeline(Pipeline):
         for pipeline in self.pipelines:
             while True:
                 frame = await pipeline.sink.get()
-                if isinstance(frame, EndFrame) or isinstance(frame, EndPipeFrame):
+                if isinstance(
+                        frame, EndFrame) or isinstance(
+                        frame, EndPipeFrame):
                     break
                 await self.sink.put(frame)
 

src/dailyai/pipeline/opeanai_llm_aggregator.py

@@ -69,7 +69,10 @@ class OpenAIContextAggregator(FrameProcessor):
         else:
             yield frame
 
-    def string_aggregator(self, frame: Frame, aggregation: str | None) -> str | None:
+    def string_aggregator(
+            self,
+            frame: Frame,
+            aggregation: str | None) -> str | None:
         if not isinstance(frame, TextFrame):
             raise TypeError(
                 "Frame must be a TextFrame instance to be aggregated by a string aggregator."
@@ -94,7 +97,7 @@ class OpenAIUserContextAggregator(OpenAIContextAggregator):
 
 class OpenAIAssistantContextAggregator(OpenAIContextAggregator):
 
-    def __init__(self, context:OpenAILLMContext):
+    def __init__(self, context: OpenAILLMContext):
         super().__init__(
             context,
             aggregator=self.string_aggregator,

src/dailyai/pipeline/pipeline.py

@@ -89,7 +89,8 @@ class Pipeline:
                 ):
                     break
         except asyncio.CancelledError:
-            # this means there's been an interruption, do any cleanup necessary here.
+            # this means there's been an interruption, do any cleanup necessary
+            # here.
             for processor in self.processors:
                 await processor.interrupted()
             pass
@@ -107,4 +108,3 @@ class Pipeline:
                     yield final_frame
         else:
             yield initial_frame
-