164 lines
5.2 KiB
Python
164 lines
5.2 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, LLM_INTRO_PROMPT
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from utils.helpers import load_images, load_sounds
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from pipecat.frames.frames import EndFrame, LLMMessagesFrame, StopTaskFrame
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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 PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.fal import FalImageGenService
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from pipecat.services.openai import OpenAILLMService
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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_out_enabled=True,
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camera_out_enabled=True,
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camera_out_width=768,
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camera_out_height=768,
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transcription_enabled=True,
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vad_enabled=True,
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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 = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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tts_service = ElevenLabsTTSService(
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
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)
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fal_service_params = FalImageGenService.InputParams(
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image_size={"width": 768, "height": 768}
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)
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fal_service = FalImageGenService(
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aiohttp_session=session,
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model="fal-ai/fast-lightning-sdxl",
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params=fal_service_params,
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key=os.getenv("FAL_KEY"),
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)
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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(fal_service)
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# -------------- Story Loop ------------- #
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runner = PipelineRunner()
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# The intro pipeline is used to start
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# the story (as per LLM_INTRO_PROMPT)
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intro_pipeline = Pipeline([llm_service, tts_service, transport.output()])
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intro_task = PipelineTask(intro_pipeline)
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logger.debug("Waiting for participant...")
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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 intro_task.queue_frames(
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[
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images["book1"],
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LLMMessagesFrame([LLM_INTRO_PROMPT]),
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DailyTransportMessageFrame(CUE_USER_TURN),
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sounds["listening"],
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images["book2"],
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StopTaskFrame(),
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]
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)
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# We run the intro pipeline. This will start the transport. The intro
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# task will exit after StopTaskFrame is processed.
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await runner.run(intro_task)
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# The main story pipeline is used to continue the story based on user
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# input.
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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(main_pipeline)
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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 intro_task.queue_frame(EndFrame())
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await main_task.queue_frame(EndFrame())
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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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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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