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from asyncio import Queue, TaskGroup
import asyncio
from contextlib import asynccontextmanager
import ray
from chat_service import ChatService
# from local_speaker_service import LocalSpeakerService
from text_to_speech_service import TextToSpeechService
from environment_state_actor import EnvironmentStateActor
from ffmpeg_converter_actor import FFMpegConverterActor
from agent_response import AgentResponse
import json
from asyncio import Semaphore
class RespondToPromptAsync:
def __init__(
self,
environment_state_actor:EnvironmentStateActor,
audio_output_queue):
voice_id="2OviOUQc1JsQRQgNkVBj"
self.prompt_queue = Queue(maxsize=100)
self.llm_sentence_queue = Queue(maxsize=100)
self.speech_chunk_queue = Queue(maxsize=100)
self.voice_id = voice_id
self.audio_output_queue = audio_output_queue
self.environment_state_actor = environment_state_actor
self.processing_semaphore = Semaphore(1)
self.sentence_queues = []
self.sentence_tasks = []
# self.ffmpeg_converter_actor = FFMpegConverterActor.remote(audio_output_queue)
async def enqueue_prompt(self, prompt:str, messages:[str]):
if len(prompt) > 0: # handles case where we just want to flush
await self.prompt_queue.put((prompt, messages))
print("Enqueued prompt")
async def prompt_to_llm(self):
chat_service = ChatService()
async with TaskGroup() as tg:
while True:
prompt, messages = await self.prompt_queue.get()
agent_response = AgentResponse(prompt)
async for text, is_complete_sentance in chat_service.get_responses_as_sentances_async(messages):
if chat_service.ignore_sentence(text):
is_complete_sentance = False
if not is_complete_sentance:
agent_response['llm_preview'] = text
await self.environment_state_actor.set_llm_preview.remote(text)
continue
agent_response['llm_preview'] = ''
agent_response['llm_sentence'] = text
agent_response['llm_sentences'].append(text)
await self.environment_state_actor.add_llm_response_and_clear_llm_preview.remote(text)
print(f"{agent_response['llm_sentence']} id: {agent_response['llm_sentence_id']} from prompt: {agent_response['prompt']}")
sentence_response = agent_response.make_copy()
new_queue = Queue()
self.sentence_queues.append(new_queue)
task = tg.create_task(self.llm_sentence_to_speech(sentence_response, new_queue))
self.sentence_tasks.append(task)
agent_response['llm_sentence_id'] += 1
async def llm_sentence_to_speech(self, sentence_response, output_queue):
tts_service = TextToSpeechService(self.voice_id)
chunk_count = 0
async for chunk_response in tts_service.get_speech_chunks_async(sentence_response):
chunk_response = chunk_response.make_copy()
# await self.output_queue.put_async(chunk_response)
await output_queue.put(chunk_response)
chunk_response = {
'prompt': sentence_response['prompt'],
'llm_sentence_id': sentence_response['llm_sentence_id'],
'chunk_count': chunk_count,
}
chunk_id_json = json.dumps(chunk_response)
await self.environment_state_actor.add_tts_raw_chunk_id.remote(chunk_id_json)
chunk_count += 1
async def speech_to_converter(self):
self.ffmpeg_converter_actor = FFMpegConverterActor.remote(self.audio_output_queue)
await self.ffmpeg_converter_actor.start_process.remote()
self.ffmpeg_converter_actor.run.remote()
while True:
for i, task in enumerate(self.sentence_tasks):
# Skip this task/queue pair if task completed
queue = self.sentence_queues[i]
if task.done() and queue.empty():
continue
while not queue.empty():
chunk_response = await queue.get()
audio_chunk_ref = chunk_response['tts_raw_chunk_ref']
audio_chunk = ray.get(audio_chunk_ref)
await self.ffmpeg_converter_actor.push_chunk.remote(audio_chunk)
break
await asyncio.sleep(0.01)
async def run(self):
async with TaskGroup() as tg: # Use asyncio's built-in TaskGroup
tg.create_task(self.prompt_to_llm())
tg.create_task(self.speech_to_converter())
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