introduce Ruff formatting

This commit is contained in:
Aleix Conchillo Flaqué
2024-09-23 09:11:37 -07:00
parent dfa4ac81fd
commit eeb8338dce
149 changed files with 2653 additions and 2461 deletions

View File

@@ -14,7 +14,9 @@ from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator, LLMUserResponseAggregator)
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.services.azure import AzureLLMService, AzureSTTService, AzureTTSService
from pipecat.transports.services.daily import DailyParams, DailyTransport
from pipecat.vad.silero import SileroVADAnalyzer
@@ -25,6 +27,7 @@ from runner import configure
from loguru import logger
from dotenv import load_dotenv
load_dotenv(override=True)
logger.remove(0)
@@ -45,7 +48,7 @@ async def main():
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
vad_audio_passthrough=True,
)
),
)
stt = AzureSTTService(
@@ -74,15 +77,17 @@ async def main():
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
pipeline = Pipeline([
transport.input(), # Transport user input
stt, # STT
tma_in, # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out # Assistant spoken responses
])
pipeline = Pipeline(
[
transport.input(), # Transport user input
stt, # STT
tma_in, # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out, # Assistant spoken responses
]
)
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
@@ -90,8 +95,7 @@ async def main():
async def on_first_participant_joined(transport, participant):
transport.capture_participant_transcription(participant["id"])
# Kick off the conversation.
messages.append(
{"role": "system", "content": "Please introduce yourself to the user."})
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMMessagesFrame(messages)])
runner = PipelineRunner()