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

import asyncio
import aiohttp
import os
import sys

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.llm_response import (
    LLMAssistantResponseAggregator,
    LLMUserResponseAggregator,
)
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.services.openpipe import OpenPipeLLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
from pipecat.vad.silero import SileroVADAnalyzer

from runner import configure

from loguru import logger
import time

from dotenv import load_dotenv
load_dotenv(override=True)

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


async def main(room_url: str, token):
    async with aiohttp.ClientSession() as session:
        transport = DailyTransport(
            room_url,
            token,
            "Respond bot",
            DailyParams(
                audio_out_enabled=True,
                transcription_enabled=True,
                vad_enabled=True,
                vad_analyzer=SileroVADAnalyzer()
            )
        )

        tts = ElevenLabsTTSService(
            aiohttp_session=session,
            api_key=os.getenv("ELEVENLABS_API_KEY"),
            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
        )

        timestamp = int(time.time())
        llm = OpenPipeLLMService(
            api_key=os.getenv("OPENAI_API_KEY"),
            openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
            model="gpt-4o",
            tags={
                "conversation_id": f"pipecat-{timestamp}"
            }
        )

        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.",
            },
        ]
        tma_in = LLMUserResponseAggregator(messages)
        tma_out = LLMAssistantResponseAggregator(messages)

        pipeline = Pipeline([
            transport.input(),   # Transport user input
            tma_in,              # User responses
            llm,                 # LLM
            tts,                 # TTS
            transport.output(),  # Transport bot output
            tma_out              # Assistant spoken responses
        ])

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

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
            transport.capture_participant_transcription(participant["id"])
            # 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__":
    (url, token) = configure()
    asyncio.run(main(url, token))