introduce Ruff formatting
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@@ -31,6 +31,7 @@ from runner import configure
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from loguru import logger
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from dotenv import load_dotenv
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load_dotenv(override=True)
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logger.remove(0)
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@@ -52,16 +53,21 @@ class ImageSyncAggregator(FrameProcessor):
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await super().process_frame(frame, direction)
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if not isinstance(frame, SystemFrame) and direction == FrameDirection.DOWNSTREAM:
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await self.push_frame(OutputImageRawFrame(
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image=self._speaking_image_bytes,
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size=(1024, 1024),
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format=self._speaking_image_format)
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await self.push_frame(
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OutputImageRawFrame(
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image=self._speaking_image_bytes,
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size=(1024, 1024),
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format=self._speaking_image_format,
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)
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)
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await self.push_frame(frame)
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await self.push_frame(OutputImageRawFrame(
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image=self._waiting_image_bytes,
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size=(1024, 1024),
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format=self._waiting_image_format))
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await self.push_frame(
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OutputImageRawFrame(
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image=self._waiting_image_bytes,
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size=(1024, 1024),
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format=self._waiting_image_format,
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)
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)
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else:
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await self.push_frame(frame)
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@@ -82,7 +88,7 @@ async def main():
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transcription_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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)
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),
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)
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tts = CartesiaHttpTTSService(
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@@ -90,9 +96,7 @@ async def main():
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voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
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)
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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model="gpt-4o")
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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messages = [
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{
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@@ -109,21 +113,23 @@ async def main():
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os.path.join(os.path.dirname(__file__), "assets", "waiting.png"),
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)
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pipeline = Pipeline([
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transport.input(),
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image_sync_aggregator,
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tma_in,
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llm,
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tts,
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transport.output(),
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tma_out
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])
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pipeline = Pipeline(
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[
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transport.input(),
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image_sync_aggregator,
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tma_in,
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llm,
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tts,
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transport.output(),
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tma_out,
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]
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)
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task = PipelineTask(pipeline)
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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participant_name = participant["info"]["userName"] or ''
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participant_name = participant["info"]["userName"] or ""
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transport.capture_participant_transcription(participant["id"])
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await task.queue_frames([TextFrame(f"Hi there {participant_name}!")])
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