97 lines
3.4 KiB
Python
97 lines
3.4 KiB
Python
import asyncio
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import os
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import logging
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from typing import AsyncGenerator
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import aiohttp
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from PIL import Image
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from dailyai.pipeline.frames import ImageFrame, Frame, TextFrame
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from dailyai.pipeline.pipeline import Pipeline
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from dailyai.transports.daily_transport import DailyTransport
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from dailyai.services.ai_services import AIService
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from dailyai.pipeline.aggregators import (
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LLMAssistantContextAggregator,
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LLMUserContextAggregator,
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)
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from dailyai.services.open_ai_services import OpenAILLMService
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from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
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from runner import configure
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from dotenv import load_dotenv
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load_dotenv(override=True)
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logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
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logger = logging.getLogger("dailyai")
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logger.setLevel(logging.DEBUG)
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class ImageSyncAggregator(AIService):
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def __init__(self, speaking_path: str, waiting_path: str):
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self._speaking_image = Image.open(speaking_path)
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self._speaking_image_bytes = self._speaking_image.tobytes()
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self._waiting_image = Image.open(waiting_path)
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self._waiting_image_bytes = self._waiting_image.tobytes()
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async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
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yield ImageFrame(self._speaking_image_bytes, (1024, 1024))
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yield frame
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yield ImageFrame(self._waiting_image_bytes, (1024, 1024))
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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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"Respond bot",
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5,
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)
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transport._camera_enabled = True
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transport._camera_width = 1024
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transport._camera_height = 1024
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transport._mic_enabled = True
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transport._mic_sample_rate = 16000
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transport.transcription_settings["extra"]["punctuate"] = True
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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=os.getenv("ELEVENLABS_VOICE_ID"),
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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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messages = [
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{
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"role": "system",
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"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so it should not include any special characters. Respond to what the user said in a creative and helpful way.",
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},
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]
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tma_in = LLMUserContextAggregator(
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messages, transport._my_participant_id)
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tma_out = LLMAssistantContextAggregator(
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messages, transport._my_participant_id
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)
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image_sync_aggregator = ImageSyncAggregator(
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os.path.join(os.path.dirname(__file__), "assets", "speaking.png"),
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os.path.join(os.path.dirname(__file__), "assets", "waiting.png"),
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)
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pipeline = Pipeline([image_sync_aggregator, tma_in, llm, tma_out, tts])
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@transport.event_handler("on_first_other_participant_joined")
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async def on_first_other_participant_joined(transport, participant):
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await pipeline.queue_frames([TextFrame("Hi, I'm listening!")])
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await transport.run(pipeline)
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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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