150 lines
4.8 KiB
Python
150 lines
4.8 KiB
Python
#
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# Copyright (c) 2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import argparse
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import asyncio
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import os
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import sys
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from processors import StoryImageProcessor, StoryProcessor
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from prompts import CUE_USER_TURN, LLM_BASE_PROMPT
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from utils.helpers import load_images, load_sounds
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import EndFrame
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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.openai_llm_context import OpenAILLMContext
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from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
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from pipecat.services.google.image import GoogleImageGenService
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from pipecat.services.google.llm import GoogleLLMService
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from pipecat.transports.services.daily import (
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DailyParams,
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DailyTransport,
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DailyTransportMessageFrame,
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)
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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sounds = load_sounds(["listening.wav"])
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images = load_images(["book1.png", "book2.png"])
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async def main(room_url, token=None):
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async with aiohttp.ClientSession() as session:
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# -------------- Transport --------------- #
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transport = DailyTransport(
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room_url,
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token,
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"Storytelling Bot",
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DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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video_out_enabled=True,
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video_out_width=1024,
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video_out_height=1024,
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transcription_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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)
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logger.debug("Transport created for room:" + room_url)
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# -------------- Services --------------- #
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llm_service = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
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tts_service = ElevenLabsTTSService(
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api_key=os.getenv("ELEVENLABS_API_KEY"), voice_id=os.getenv("ELEVENLABS_VOICE_ID")
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)
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image_gen = GoogleImageGenService(api_key=os.getenv("GOOGLE_API_KEY"))
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# --------------- Setup ----------------- #
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message_history = [LLM_BASE_PROMPT]
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story_pages = []
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# We need aggregators to keep track of user and LLM responses
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context = OpenAILLMContext(message_history)
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context_aggregator = llm_service.create_context_aggregator(context)
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# -------------- Processors ------------- #
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story_processor = StoryProcessor(message_history, story_pages)
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image_processor = StoryImageProcessor(image_gen)
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# -------------- Story Loop ------------- #
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runner = PipelineRunner()
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logger.debug("Waiting for participant...")
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main_pipeline = Pipeline(
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[
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transport.input(),
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context_aggregator.user(),
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llm_service,
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story_processor,
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image_processor,
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tts_service,
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transport.output(),
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context_aggregator.assistant(),
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]
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)
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main_task = PipelineTask(
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main_pipeline,
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params=PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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enable_usage_metrics=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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logger.debug("Participant joined, storytime commence!")
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await transport.capture_participant_transcription(participant["id"])
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await main_task.queue_frames(
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[
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images["book1"],
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context_aggregator.user().get_context_frame(),
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DailyTransportMessageFrame(CUE_USER_TURN),
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# sounds["listening"],
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images["book2"],
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]
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)
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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await main_task.cancel()
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@transport.event_handler("on_call_state_updated")
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async def on_call_state_updated(transport, state):
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if state == "left":
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# Here we don't want to cancel, we just want to finish sending
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# whatever is queued, so we use an EndFrame().
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await main_task.queue_frame(EndFrame())
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await runner.run(main_task)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Daily Storyteller Bot")
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parser.add_argument("-u", type=str, help="Room URL")
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parser.add_argument("-t", type=str, help="Token")
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config = parser.parse_args()
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asyncio.run(main(config.u, config.t))
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