import asyncio
import logging
import os

import aiohttp
from dailyai.pipeline.pipeline import Pipeline

from dailyai.services.daily_transport_service import DailyTransportService
from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
from dailyai.services.deepgram_ai_services import DeepgramTTSService
from dailyai.pipeline.frames import EndFrame, LLMMessagesQueueFrame
from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
from examples.support.runner import configure

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


async def main(room_url: str):
    async with aiohttp.ClientSession() as session:
        transport = DailyTransportService(
            room_url,
            None,
            "Static And Dynamic Speech",
            duration_minutes=1,
            mic_enabled=True,
            mic_sample_rate=16000,
        )

        llm = AzureLLMService(
            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
            model=os.getenv("AZURE_CHATGPT_MODEL"),
        )
        azure_tts = AzureTTSService(
            api_key=os.getenv("AZURE_SPEECH_API_KEY"),
            region=os.getenv("AZURE_SPEECH_REGION"),
        )

        deepgram_tts = DeepgramTTSService(
            aiohttp_session=session,
            api_key=os.getenv("DEEPGRAM_API_KEY"),
        )
        elevenlabs_tts = ElevenLabsTTSService(
            aiohttp_session=session,
            api_key=os.getenv("ELEVENLABS_API_KEY"),
            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
        )

        messages = [{"role": "system", "content": "tell the user a joke about llamas"}]

        # Start a task to run the LLM to create a joke, and convert the LLM output to audio frames. This task
        # will run in parallel with generating and speaking the audio for static text, so there's no delay to
        # speak the LLM response.
        buffer_queue = asyncio.Queue()
        source_queue = asyncio.Queue()
        pipeline = Pipeline(
            source=source_queue, sink=buffer_queue, processors=[llm, elevenlabs_tts]
        )
        await source_queue.put(LLMMessagesQueueFrame(messages))
        await source_queue.put(EndFrame())
        pipeline_run_task = pipeline.run_pipeline()

        @transport.event_handler("on_first_other_participant_joined")
        async def on_first_other_participant_joined(transport):
            await azure_tts.say(
                "My friend the LLM is now going to tell a joke about llamas.",
                transport.send_queue,
            )

            # khk: deepgram_tts.say() doesn't seem to put bytes in the transport
            # queue. I get a debug log line that indicates we're set up okay, but
            # no further log lines or audio bytes. debug this later:
            #   20 2024-03-10 13:24:46,235 Running deepgram tts for My friend the LLM is now going to tell a joke about llamas.
            # await deepgram_tts.say(
            #    "My friend the LLM is now going to tell a joke about llamas.",
            #    transport.send_queue,
            # )

            async def buffer_to_send_queue():
                while True:
                    frame = await buffer_queue.get()
                    await transport.send_queue.put(frame)
                    buffer_queue.task_done()
                    if isinstance(frame, EndFrame):
                        break

            await asyncio.gather(pipeline_run_task, buffer_to_send_queue())
            await transport.stop_when_done()

        await transport.run()


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