#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

import asyncio
import json
import os
import sys

from dotenv import load_dotenv
from loguru import logger

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
    InterruptionFrame,
    TranscriptionFrame,
    TTSSpeakFrame,
    UserStartedSpeakingFrame,
    UserStoppedSpeakingFrame,
)
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.livekit import configure
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.livekit.transport import LiveKitParams, LiveKitTransport

load_dotenv(override=True)

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


async def main():
    (url, token, room_name) = await configure()

    transport = LiveKitTransport(
        url=url,
        token=token,
        room_name=room_name,
        params=LiveKitParams(
            audio_in_enabled=True,
            audio_out_enabled=True,
        ),
    )

    stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])

    llm = OpenAILLMService(
        api_key=os.environ["OPENAI_API_KEY"],
        settings=OpenAILLMService.Settings(
            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
        ),
    )

    tts = CartesiaTTSService(
        api_key=os.environ["CARTESIA_API_KEY"],
        settings=CartesiaTTSService.Settings(
            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
        ),
    )

    context = LLMContext()
    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
        context,
        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
    )

    pipeline = Pipeline(
        [
            transport.input(),  # Transport user input
            stt,
            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,
        ),
    )

    # Register an event handler so we can play the audio when the
    # participant joins.
    @transport.event_handler("on_first_participant_joined")
    async def on_first_participant_joined(transport, participant_id):
        await asyncio.sleep(1)
        await task.queue_frame(
            TTSSpeakFrame(
                "Hello there! How are you doing today? Would you like to talk about the weather?"
            )
        )

    # Register an event handler to receive data from the participant via text chat
    # in the LiveKit room. This will be used to as transcription frames and
    # interrupt the bot and pass it to llm for processing and
    # then pass back to the participant as audio output.
    @transport.event_handler("on_data_received")
    async def on_data_received(transport, data, participant_id):
        logger.info(f"Received data from participant {participant_id}: {data}")
        # convert data from bytes to string
        json_data = json.loads(data)

        await task.queue_frames(
            [
                InterruptionFrame(),
                UserStartedSpeakingFrame(),
                TranscriptionFrame(
                    user_id=participant_id,
                    timestamp=json_data["timestamp"],
                    text=json_data["message"],
                ),
                UserStoppedSpeakingFrame(),
            ],
        )

    runner = PipelineRunner()

    await runner.run(task)


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