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 response_state_manager import ResponseStateManager
from ffmpeg_converter import FFMpegConverter
from agent_response import AgentResponse
import json

class RespondToPromptAsync:
    def __init__(
            self, 
            response_state_manager:ResponseStateManager, 
            audio_output_queue):
        voice_id="2OviOUQc1JsQRQgNkVBj"
        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.response_state_manager = response_state_manager
        self.sentence_queues = []
        self.sentence_tasks = []    
        # self.ffmpeg_converter = FFMpegConverter.remote(audio_output_queue)

    async def prompt_to_llm(self, prompt:str, messages:[str]):
        chat_service = ChatService()

        async with TaskGroup() as tg:
            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
                    self.response_state_manager.set_llm_preview(text)
                    continue
                agent_response['llm_preview'] = ''
                agent_response['llm_sentence'] = text
                agent_response['llm_sentences'].append(text)
                self.response_state_manager.add_llm_response_and_clear_llm_preview(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)
            self.response_state_manager.add_tts_raw_chunk_id(chunk_id_json, sentence_response['llm_sentence_id'])
            chunk_count += 1

    async def speech_to_converter(self):
        self.ffmpeg_converter = FFMpegConverter(self.audio_output_queue)
        await self.ffmpeg_converter.start_process()
        self.ffmpeg_converter_task = asyncio.create_task(self.ffmpeg_converter.run())
        
        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.push_chunk(audio_chunk)
                break

            await asyncio.sleep(0.01) 

    async def run(self, prompt:str, messages:[str]):
        self.task_group_tasks = []
        async with TaskGroup() as tg:  # Use asyncio's built-in TaskGroup
            t1 = tg.create_task(self.prompt_to_llm(prompt, messages))
            t2 = tg.create_task(self.speech_to_converter())
            self.task_group_tasks.extend([t1, t2])

    async def terminate(self):
        # Cancel tasks
        if self.task_group_tasks:
            for task in self.task_group_tasks:
                task.cancel()
        for task in self.sentence_tasks:
            task.cancel()

        # Close FFmpeg converter actor
        if self.ffmpeg_converter_task:
            self.ffmpeg_converter_task.cancel()
            await self.ffmpeg_converter.close()
        # ray.kill(self.ffmpeg_converter)

        # Flush all queues
        # TODO re-enable to interupt when user speaks
        # while not self.audio_output_queue.empty():
        #     await self.audio_output_queue.get_async()
        #     # await self.audio_output_queue.get_async(block=False)
        while not self.llm_sentence_queue.empty():
            self.llm_sentence_queue.get_nowait()
        while not self.speech_chunk_queue.empty():
            self.speech_chunk_queue.get_nowait()
        for sentence_queue in self.sentence_queues:
            while not sentence_queue.empty():
                sentence_queue.get_nowait()