#
# Copyright (c) 2024, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

import asyncio
import os
import sys

import aiohttp
from dotenv import load_dotenv
from loguru import logger
from runner import configure

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
    LLMMessagesFrame,
)
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.filters.stt_mute_filter import STTMuteConfig, STTMuteFilter, STTMuteStrategy
from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport

load_dotenv(override=True)

logger.remove(0)
logger.add(sys.stderr, level="DEBUG")


async def main():
    async with aiohttp.ClientSession() as session:
        (room_url, _) = await configure(session)

        transport = DailyTransport(
            room_url,
            None,
            "Respond bot",
            DailyParams(
                audio_out_enabled=True,
                vad_enabled=True,
                vad_analyzer=SileroVADAnalyzer(),
                vad_audio_passthrough=True,
            ),
        )

        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
        # Configure the mute processor to mute only during first speech
        stt_mute_processor = STTMuteFilter(
            stt_service=stt, config=STTMuteConfig(strategy=STTMuteStrategy.FIRST_SPEECH)
        )

        tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")

        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

        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 so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
            },
        ]

        context = OpenAILLMContext(messages)
        context_aggregator = llm.create_context_aggregator(context)

        pipeline = Pipeline(
            [
                transport.input(),  # Transport user input
                stt_mute_processor,  # Add the mute processor before STT
                stt,  # STT
                context_aggregator.user(),  # User responses
                llm,  # LLM
                tts,  # TTS
                transport.output(),  # Transport bot output
                context_aggregator.assistant(),  # Assistant spoken responses
            ]
        )

        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
            # Kick off the conversation.
            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
            await task.queue_frames([LLMMessagesFrame(messages)])

        runner = PipelineRunner()

        await runner.run(task)


if __name__ == "__main__":
    asyncio.run(main())