103 lines
3.3 KiB
Python
103 lines
3.3 KiB
Python
import asyncio
|
|
import aiohttp
|
|
import logging
|
|
import os
|
|
from typing import AsyncGenerator
|
|
|
|
from dailyai.pipeline.frames import Frame, LLMMessagesQueueFrame
|
|
from dailyai.pipeline.pipeline import Pipeline
|
|
from dailyai.pipeline.frame_processor import FrameProcessor
|
|
from dailyai.services.daily_transport_service import DailyTransportService
|
|
from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
|
|
from dailyai.services.open_ai_services import OpenAILLMService, OpenAIVisionService
|
|
from dailyai.services.ai_services import FrameLogger
|
|
from dailyai.pipeline.aggregators import (
|
|
LLMAssistantContextAggregator,
|
|
LLMUserContextAggregator,
|
|
)
|
|
from dailyai.pipeline.frames import VideoImageFrame, VisionFrame
|
|
from examples.support.runner import configure
|
|
|
|
logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
|
|
logger = logging.getLogger("dailyai")
|
|
logger.setLevel(logging.DEBUG)
|
|
|
|
|
|
class VideoImageFrameProcessor(FrameProcessor):
|
|
def __init__(self):
|
|
pass
|
|
|
|
async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
|
|
if isinstance(frame, VideoImageFrame):
|
|
yield VisionFrame("What is in this image?", frame.image)
|
|
else:
|
|
yield frame
|
|
|
|
|
|
async def main(room_url: str, token):
|
|
async with aiohttp.ClientSession() as session:
|
|
transport = DailyTransportService(
|
|
room_url,
|
|
token,
|
|
"Respond bot",
|
|
duration_minutes=5,
|
|
start_transcription=True,
|
|
mic_enabled=True,
|
|
mic_sample_rate=16000,
|
|
camera_enabled=False,
|
|
vad_enabled=True,
|
|
receive_video=True,
|
|
receive_video_fps=1/10.0
|
|
)
|
|
|
|
tts = ElevenLabsTTSService(
|
|
aiohttp_session=session,
|
|
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
|
voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
|
|
)
|
|
|
|
llm = OpenAILLMService(
|
|
api_key=os.getenv("OPENAI_CHATGPT_API_KEY"),
|
|
model="gpt-4-turbo-preview")
|
|
fl = FrameLogger("!!! before VIFP")
|
|
fl2 = FrameLogger("Outer")
|
|
fl3 = FrameLogger("### Before VS")
|
|
fl4 = FrameLogger("$$$ After VS")
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"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. Respond to what the user said in a creative and helpful way.",
|
|
},
|
|
]
|
|
|
|
tma_in = LLMUserContextAggregator(
|
|
messages, transport._my_participant_id)
|
|
tma_out = LLMAssistantContextAggregator(
|
|
messages, transport._my_participant_id
|
|
)
|
|
vs = OpenAIVisionService(api_key=os.getenv("OPENAI_CHATGPT_API_KEY"))
|
|
|
|
vifp = VideoImageFrameProcessor()
|
|
pipeline = Pipeline(
|
|
processors=[
|
|
fl,
|
|
vifp,
|
|
fl3,
|
|
vs,
|
|
fl4,
|
|
llm,
|
|
fl2,
|
|
tts,
|
|
tma_out,
|
|
],
|
|
)
|
|
|
|
transport.transcription_settings["extra"]["endpointing"] = True
|
|
transport.transcription_settings["extra"]["punctuate"] = True
|
|
await transport.run(pipeline)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
(url, token) = configure()
|
|
asyncio.run(main(url, token))
|