#
# 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.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import LLMRunFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.worker import PipelineParams, PipelineWorker, ProcessorUnusablePolicy
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import (
    LLMContextAggregatorPair,
    LLMUserAggregatorParams,
)
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.google.llm import GoogleLLMService
from pipecat.transports.tavus.transport import TavusParams, TavusTransport
from pipecat.workers.runner import WorkerRunner

load_dotenv(override=True)

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


async def main():
    async with aiohttp.ClientSession() as session:
        transport = TavusTransport(
            bot_name="Pipecat bot",
            api_key=os.environ["TAVUS_API_KEY"],
            replica_id=os.environ["TAVUS_REPLICA_ID"],
            session=session,
            params=TavusParams(
                audio_in_enabled=True,
                audio_out_enabled=True,
                microphone_out_enabled=False,
                audio_out_faster_than_realtime=True,
            ),
        )

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

        tts = CartesiaTTSService(
            api_key=os.environ["CARTESIA_API_KEY"],
            settings=CartesiaTTSService.Settings(
                voice="a167e0f3-df7e-4d52-a9c3-f949145efdab",
            ),
        )

        llm = GoogleLLMService(
            api_key=os.environ["GOOGLE_API_KEY"],
            settings=GoogleLLMService.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.",
            ),
        )

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

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

        worker = PipelineWorker(
            pipeline,
            params=PipelineParams(
                audio_in_sample_rate=16000,
                audio_out_sample_rate=24000,
                enable_metrics=True,
                enable_usage_metrics=True,
            ),
            processor_unusable_policy=ProcessorUnusablePolicy.END,
        )

        runner = WorkerRunner()

        await runner.add_workers(worker)

        @transport.event_handler("on_connected")
        async def on_connected(transport, data):
            # Extract the room name to build the conversation URL. Share this
            # URL with a frontend client so it can join the same Daily room.
            room_name = data.get("callConfig", {}).get("roomName")
            conversation_url = f"https://tavus.daily.co/{room_name}"
            logger.info(f"Conversation URL: {conversation_url}")

        @transport.event_handler("on_client_connected")
        async def on_client_connected(transport, participant):
            logger.info("Client connected")
            # Kick off the conversation.
            context.add_message(
                {
                    "role": "developer",
                    "content": "Start by greeting the user and ask how you can help.",
                }
            )
            await worker.queue_frames([LLMRunFrame()])

        @transport.event_handler("on_client_disconnected")
        async def on_client_disconnected(transport, participant):
            logger.info("Client disconnected")
            await runner.cancel()

        await runner.run()


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