157 lines
4.9 KiB
Python
157 lines
4.9 KiB
Python
#
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# Copyright (c) 2024, 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 asyncio
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import aiohttp
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import os
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import random
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import sys
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from PIL import Image
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from pipecat.frames.frames import Frame, ImageRawFrame, SpriteFrame
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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.llm_context import (
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LLMUserContextAggregator,
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LLMAssistantContextAggregator,
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)
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from pipecat.processors.filters.wake_check_filter import WakeCheckFilter
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.services.openai import OpenAILLMService
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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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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logger.add(sys.stderr, level="DEBUG")
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sprites = {}
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image_files = [
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"sc-default.png",
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"sc-talk.png",
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"sc-listen-1.png",
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"sc-think-1.png",
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"sc-think-2.png",
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"sc-think-3.png",
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"sc-think-4.png",
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]
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script_dir = os.path.dirname(__file__)
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for file in image_files:
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# Build the full path to the image file
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full_path = os.path.join(script_dir, "assets", file)
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# Get the filename without the extension to use as the dictionary key
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filename = os.path.splitext(os.path.basename(full_path))[0]
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# Open the image and convert it to bytes
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with Image.open(full_path) as img:
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sprites[file] = ImageRawFrame(image=img.tobytes(), size=img.size, format=img.format)
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# When the bot isn't talking, show a static image of the cat listening
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quiet_frame = sprites["sc-listen-1.png"]
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# When the bot is talking, build an animation from two sprites
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talking_list = [sprites["sc-default.png"], sprites["sc-talk.png"]]
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talking = [random.choice(talking_list) for x in range(30)]
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talking_frame = SpriteFrame(talking)
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# TODO: Support "thinking" as soon as we get a valid transcript, while LLM
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# is processing
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thinking_list = [
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sprites["sc-think-1.png"],
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sprites["sc-think-2.png"],
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sprites["sc-think-3.png"],
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sprites["sc-think-4.png"],
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]
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thinking_frame = SpriteFrame(thinking_list)
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class ImageSyncAggregator(FrameProcessor):
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await self.push_frame(talking_frame)
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await self.push_frame(frame)
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await self.push_frame(quiet_frame)
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async def main(room_url: str, token):
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async with aiohttp.ClientSession() as session:
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transport = DailyTransport(
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room_url,
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token,
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"Santa Cat",
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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=720,
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camera_out_height=1280,
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camera_out_framerate=10,
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transcription_enabled=True
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)
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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-4-turbo-preview")
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tts = ElevenLabsTTSService(
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aiohttp_session=session,
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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voice_id="jBpfuIE2acCO8z3wKNLl",
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)
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isa = ImageSyncAggregator()
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messages = [
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{
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"role": "system",
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"content": "You are Santa Cat, a cat that lives in Santa's workshop at the North Pole. You should be clever, and a bit sarcastic. You should also tell jokes every once in a while. Your responses should only be a few sentences long.",
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},
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]
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tma_in = LLMUserContextAggregator(messages)
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tma_out = LLMAssistantContextAggregator(messages)
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wcf = WakeCheckFilter(["Santa Cat", "Santa"])
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pipeline = Pipeline([
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transport.input(), # Transport user input
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isa, # Cat talking/quiet images
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wcf, # Filter out speech not directed at Santa Cat
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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 # Santa Cat spoken responses
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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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# Send some greeting at the beginning.
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await tts.say("Hi! If you want to talk to me, just say 'hey Santa Cat'.")
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transport.capture_participant_transcription(participant["id"])
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async def starting_image():
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await transport.send_image(quiet_frame)
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runner = PipelineRunner()
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task = PipelineTask(pipeline)
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await asyncio.gather(runner.run(task), starting_image())
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if __name__ == "__main__":
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(url, token) = configure()
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asyncio.run(main(url, token))
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