116 lines
4.5 KiB
Python
116 lines
4.5 KiB
Python
import aiohttp
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import asyncio
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import os
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from dailyai.services.daily_transport_service import DailyTransportService
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from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
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from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
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from dailyai.services.fal_ai_services import FalImageGenService
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from dailyai.pipeline.frames import AudioFrame, ImageFrame
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from examples.foundational.support.runner import configure
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async def main(room_url: str):
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async with aiohttp.ClientSession() as session:
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transport = DailyTransportService(
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room_url,
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None,
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"Respond bot",
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duration_minutes=10,
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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=1024
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)
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llm = AzureLLMService(
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api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
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endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
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model=os.getenv("AZURE_CHATGPT_MODEL"))
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tts1 = AzureTTSService(
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api_key=os.getenv("AZURE_SPEECH_API_KEY"),
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region=os.getenv("AZURE_SPEECH_REGION"))
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tts2 = 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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dalle = FalImageGenService(
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image_size="1024x1024",
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aiohttp_session=session,
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key_id=os.getenv("FAL_KEY_ID"),
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key_secret=os.getenv("FAL_KEY_SECRET"))
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bot1_messages = [
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{"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."},
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]
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bot2_messages = [
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{
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"role": "system",
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"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."},
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]
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async def get_bot1_statement():
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# Run the LLMs synchronously for the back-and-forth
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bot1_msg = await llm.run_llm(bot1_messages)
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print(f"bot1_msg: {bot1_msg}")
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if bot1_msg:
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bot1_messages.append({"role": "assistant", "content": bot1_msg})
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bot2_messages.append({"role": "user", "content": bot1_msg})
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all_audio = bytearray()
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async for audio in tts1.run_tts(bot1_msg):
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all_audio.extend(audio)
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return all_audio
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async def get_bot2_statement():
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# Run the LLMs synchronously for the back-and-forth
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bot2_msg = await llm.run_llm(bot2_messages)
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print(f"bot2_msg: {bot2_msg}")
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if bot2_msg:
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bot2_messages.append({"role": "assistant", "content": bot2_msg})
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bot1_messages.append({"role": "user", "content": bot2_msg})
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all_audio = bytearray()
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async for audio in tts2.run_tts(bot2_msg):
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all_audio.extend(audio)
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return all_audio
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async def argue():
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for i in range(100):
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print(f"In iteration {i}")
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bot1_description = "A woman conservatively dressed as a librarian in a library surrounded by books, cartoon, serious, highly detailed"
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(audio1, image_data1) = await asyncio.gather(
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get_bot1_statement(), dalle.run_image_gen(bot1_description)
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)
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await transport.send_queue.put(
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[
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ImageFrame(None, image_data1[1]),
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AudioFrame(audio1),
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]
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)
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bot2_description = "A cat dressed in a hot dog costume, cartoon, bright colors, funny, highly detailed"
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(audio2, image_data2) = await asyncio.gather(
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get_bot2_statement(), dalle.run_image_gen(bot2_description)
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)
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await transport.send_queue.put(
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[
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ImageFrame(None, image_data2[1]),
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AudioFrame(audio2),
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]
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)
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await asyncio.gather(transport.run(), argue())
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if __name__ == "__main__":
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(url, token) = configure()
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asyncio.run(main(url))
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