Files
pipecat/src/examples/foundational/08-bots-arguing.py
2024-03-06 14:09:06 -05:00

116 lines
4.5 KiB
Python

import aiohttp
import asyncio
import os
from dailyai.services.daily_transport_service import DailyTransportService
from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
from dailyai.services.fal_ai_services import FalImageGenService
from dailyai.pipeline.frames import AudioFrame, ImageFrame
from examples.foundational.support.runner import configure
async def main(room_url: str):
async with aiohttp.ClientSession() as session:
transport = DailyTransportService(
room_url,
None,
"Respond bot",
duration_minutes=10,
mic_enabled=True,
mic_sample_rate=16000,
camera_enabled=True,
camera_width=1024,
camera_height=1024
)
llm = AzureLLMService(
api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
model=os.getenv("AZURE_CHATGPT_MODEL"))
tts1 = AzureTTSService(
api_key=os.getenv("AZURE_SPEECH_API_KEY"),
region=os.getenv("AZURE_SPEECH_REGION"))
tts2 = ElevenLabsTTSService(
aiohttp_session=session,
api_key=os.getenv("ELEVENLABS_API_KEY"),
voice_id="jBpfuIE2acCO8z3wKNLl")
dalle = FalImageGenService(
image_size="1024x1024",
aiohttp_session=session,
key_id=os.getenv("FAL_KEY_ID"),
key_secret=os.getenv("FAL_KEY_SECRET"))
bot1_messages = [
{"role": "system", "content": "You are a stern librarian. You strongly believe that a hot dog is a sandwich. Start by stating this fact in a few sentences, then be prepared to debate this with the user. You shouldn't ever compromise on the fundamental truth that a hot dog is a sandwich. Your responses should only be a few sentences long."},
]
bot2_messages = [
{
"role": "system",
"content": "You are a silly cat, and you strongly believe that a hot dog is not a sandwich. Debate this with the user, only responding with a few sentences. Don't ever accept that a hot dog is a sandwich."},
]
async def get_bot1_statement():
# Run the LLMs synchronously for the back-and-forth
bot1_msg = await llm.run_llm(bot1_messages)
print(f"bot1_msg: {bot1_msg}")
if bot1_msg:
bot1_messages.append({"role": "assistant", "content": bot1_msg})
bot2_messages.append({"role": "user", "content": bot1_msg})
all_audio = bytearray()
async for audio in tts1.run_tts(bot1_msg):
all_audio.extend(audio)
return all_audio
async def get_bot2_statement():
# Run the LLMs synchronously for the back-and-forth
bot2_msg = await llm.run_llm(bot2_messages)
print(f"bot2_msg: {bot2_msg}")
if bot2_msg:
bot2_messages.append({"role": "assistant", "content": bot2_msg})
bot1_messages.append({"role": "user", "content": bot2_msg})
all_audio = bytearray()
async for audio in tts2.run_tts(bot2_msg):
all_audio.extend(audio)
return all_audio
async def argue():
for i in range(100):
print(f"In iteration {i}")
bot1_description = "A woman conservatively dressed as a librarian in a library surrounded by books, cartoon, serious, highly detailed"
(audio1, image_data1) = await asyncio.gather(
get_bot1_statement(), dalle.run_image_gen(bot1_description)
)
await transport.send_queue.put(
[
ImageFrame(None, image_data1[1]),
AudioFrame(audio1),
]
)
bot2_description = "A cat dressed in a hot dog costume, cartoon, bright colors, funny, highly detailed"
(audio2, image_data2) = await asyncio.gather(
get_bot2_statement(), dalle.run_image_gen(bot2_description)
)
await transport.send_queue.put(
[
ImageFrame(None, image_data2[1]),
AudioFrame(audio2),
]
)
await asyncio.gather(transport.run(), argue())
if __name__ == "__main__":
(url, token) = configure()
asyncio.run(main(url))