150 lines
4.7 KiB
Python
150 lines
4.7 KiB
Python
import asyncio
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import aiohttp
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import logging
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import os
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from PIL import Image
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from typing import AsyncGenerator
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from dailyai.pipeline.aggregators import (
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LLMAssistantResponseAggregator,
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LLMUserResponseAggregator,
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)
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from dailyai.pipeline.frames import (
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ImageFrame,
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SpriteFrame,
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Frame,
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LLMResponseEndFrame,
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LLMMessagesFrame,
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AudioFrame,
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PipelineStartedFrame,
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)
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from dailyai.services.ai_services import AIService
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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.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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sprites = []
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script_dir = os.path.dirname(__file__)
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for i in range(1, 26):
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# Build the full path to the image file
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full_path = os.path.join(script_dir, f"assets/robot0{i}.png")
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# Get the filename without the extension to use as the dictionary key
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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.append(img.tobytes())
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flipped = sprites[::-1]
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sprites.extend(flipped)
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# When the bot isn't talking, show a static image of the cat listening
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quiet_frame = ImageFrame(sprites[0], (1024, 576))
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talking_frame = SpriteFrame(images=sprites)
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class TalkingAnimation(AIService):
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"""
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This class starts a talking animation when it receives an first AudioFrame,
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and then returns to a "quiet" sprite when it sees a LLMResponseEndFrame.
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"""
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def __init__(self):
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super().__init__()
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self._is_talking = False
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async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
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if isinstance(frame, AudioFrame):
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if not self._is_talking:
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yield talking_frame
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yield frame
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self._is_talking = True
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else:
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yield frame
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elif isinstance(frame, LLMResponseEndFrame):
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yield quiet_frame
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yield frame
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self._is_talking = False
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else:
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yield frame
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class AnimationInitializer(AIService):
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def __init__(self):
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super().__init__()
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async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
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if isinstance(frame, PipelineStartedFrame):
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yield quiet_frame
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yield frame
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else:
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yield 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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"Chatbot",
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duration_minutes=5,
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start_transcription=True,
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mic_enabled=True,
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mic_sample_rate=16000,
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camera_enabled=True,
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camera_width=1024,
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camera_height=576,
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vad_enabled=True,
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)
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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="pNInz6obpgDQGcFmaJgB",
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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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ta = TalkingAnimation()
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ai = AnimationInitializer()
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pipeline = Pipeline([ai, llm, tts, ta])
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messages = [
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{
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"role": "system",
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"content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by introducing yourself.",
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},
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]
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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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print(f"!!! in here, pipeline.source is {pipeline.source}")
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await pipeline.queue_frames([LLMMessagesFrame(messages)])
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async def run_conversation():
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await transport.run_interruptible_pipeline(
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pipeline,
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post_processor=LLMAssistantResponseAggregator(messages),
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pre_processor=LLMUserResponseAggregator(messages),
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)
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transport.transcription_settings["extra"]["endpointing"] = True
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transport.transcription_settings["extra"]["punctuate"] = True
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await asyncio.gather(transport.run(), run_conversation())
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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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