introduce Ruff formatting
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@@ -14,7 +14,9 @@ from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.llm_response import (
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LLMAssistantResponseAggregator, LLMUserResponseAggregator)
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LLMAssistantResponseAggregator,
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LLMUserResponseAggregator,
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)
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from pipecat.processors.user_idle_processor import UserIdleProcessor
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from pipecat.services.cartesia import CartesiaTTSService
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from pipecat.services.openai import OpenAILLMService
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@@ -26,6 +28,7 @@ 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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@@ -44,8 +47,8 @@ async def main():
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audio_out_enabled=True,
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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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vad_analyzer=SileroVADAnalyzer(),
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),
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)
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tts = CartesiaTTSService(
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@@ -53,9 +56,7 @@ 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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@@ -69,33 +70,41 @@ async def main():
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async def user_idle_callback(user_idle: UserIdleProcessor):
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messages.append(
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{"role": "system", "content": "Ask the user if they are still there and try to prompt for some input, but be short."})
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{
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"role": "system",
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"content": "Ask the user if they are still there and try to prompt for some input, but be short.",
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}
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)
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await user_idle.push_frame(LLMMessagesFrame(messages))
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user_idle = UserIdleProcessor(callback=user_idle_callback, timeout=5.0)
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pipeline = Pipeline([
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transport.input(), # Transport user input
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user_idle, # Idle user check-in
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tma_in, # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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tma_out # Assistant spoken responses
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])
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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user_idle, # Idle user check-in
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tma_in, # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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tma_out, # Assistant spoken responses
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]
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)
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task = PipelineTask(pipeline, PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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report_only_initial_ttfb=True,
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))
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task = PipelineTask(
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pipeline,
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PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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report_only_initial_ttfb=True,
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),
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)
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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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transport.capture_participant_transcription(participant["id"])
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# Kick off the conversation.
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messages.append(
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{"role": "system", "content": "Please introduce yourself to the user."})
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messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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await task.queue_frames([LLMMessagesFrame(messages)])
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runner = PipelineRunner()
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