import aiohttp
import asyncio
import logging
import os
import wave

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.queue_aggregators import LLMContextAggregator, LLMUserContextAggregator, LLMAssistantContextAggregator
from dailyai.services.ai_services import AIService, FrameLogger
from dailyai.queue_frame import QueueFrame, AudioQueueFrame, LLMResponseEndQueueFrame, LLMMessagesQueueFrame
from typing import AsyncGenerator

from examples.foundational.support.runner import configure

logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")  # or whatever
logger = logging.getLogger("dailyai")
logger.setLevel(logging.DEBUG)

sounds = {}
sound_files = [
    'ding1.wav',
    'ding2.wav'
]

script_dir = os.path.dirname(__file__)

for file in sound_files:
    # Build the full path to the image file
    full_path = os.path.join(script_dir, "assets", file)
    # Get the filename without the extension to use as the dictionary key
    filename = os.path.splitext(os.path.basename(full_path))[0]
    # Open the image and convert it to bytes
    with wave.open(full_path) as audio_file:
        sounds[file] = audio_file.readframes(-1)


class OutboundSoundEffectWrapper(AIService):
    def __init__(self):
        pass

    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
        if isinstance(frame, LLMResponseEndQueueFrame):
            yield AudioQueueFrame(sounds["ding1.wav"])
            # In case anything else up the stack needs it
            yield frame
        else:
            yield frame


class InboundSoundEffectWrapper(AIService):
    def __init__(self):
        pass

    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
        if isinstance(frame, LLMMessagesQueueFrame):
            yield AudioQueueFrame(sounds["ding2.wav"])
            # In case anything else up the stack needs it
            yield frame
        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,
            mic_enabled=True,
            mic_sample_rate=16000,
            camera_enabled=False
        )

        llm = AzureLLMService(
            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
            model=os.getenv("AZURE_CHATGPT_MODEL"))
        tts = ElevenLabsTTSService(
            aiohttp_session=session,
            api_key=os.getenv("ELEVENLABS_API_KEY"),
            voice_id="ErXwobaYiN019PkySvjV")

        @transport.event_handler("on_first_other_participant_joined")
        async def on_first_other_participant_joined(transport):
            await tts.say("Hi, I'm listening!", transport.send_queue)
            await transport.send_queue.put(AudioQueueFrame(sounds["ding1.wav"]))

        async def handle_transcriptions():
            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
            )
            out_sound = OutboundSoundEffectWrapper()
            in_sound = InboundSoundEffectWrapper()
            fl = FrameLogger("LLM Out")
            fl2 = FrameLogger("Transcription In")
            await out_sound.run_to_queue(
                transport.send_queue,
                tts.run(
                    fl.run(
                        tma_out.run(
                            llm.run(
                                fl2.run(
                                    in_sound.run(
                                        tma_in.run(
                                            transport.get_receive_frames()
                                        )
                                    )
                                )
                            )
                        )
                    )
                )
            )

        transport.transcription_settings["extra"]["punctuate"] = True
        await asyncio.gather(transport.run(), handle_transcriptions())


if __name__ == "__main__":
    (url, token) = configure()
    asyncio.run(main(url, token))