#
# Copyright (c) 2024-2026, 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 pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.frames.frames import LLMRunFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import (
    LLMContextAggregatorPair,
    LLMUserAggregatorParams,
)
from pipecat.runner.daily import configure
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.daily.transport import DailyParams, DailyTransport
from pipecat.turns.user_stop import TurnAnalyzerUserTurnStopStrategy
from pipecat.turns.user_turn_strategies import UserTurnStrategies

load_dotenv(override=True)

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


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

        transport = DailyTransport(
            room_url,
            token,
            "Respond bot",
            DailyParams(
                audio_in_enabled=True,
                audio_out_enabled=True,
                transcription_enabled=True,
                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
            ),
        )

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
        )

        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 spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
            },
        ]

        context = LLMContext(messages)
        user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
            context,
            user_params=LLMUserAggregatorParams(
                user_turn_strategies=UserTurnStrategies(
                    stop=[
                        TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())
                    ]
                ),
            ),
        )

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

        task = PipelineTask(
            pipeline,
            params=PipelineParams(
                enable_metrics=True,
                enable_usage_metrics=True,
            ),
        )

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
            await 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([LLMRunFrame()])

        @transport.event_handler("on_participant_left")
        async def on_participant_left(transport, participant, reason):
            await task.cancel()

        runner = PipelineRunner()

        await runner.run(task)


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