#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

import argparse
import os

from dotenv import load_dotenv
from loguru import logger

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.ultravox.stt import UltravoxSTTService
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection

load_dotenv(override=True)

# NOTE: This example requires GPU resources to run efficiently.
# The Ultravox model is compute-intensive and performs best with GPU acceleration.
# This can be deployed on cloud GPU providers like Cerebrium.ai for optimal performance.


# Want to initialize the ultravox processor since it takes time to load the model and dont
# want to load it every time the pipeline is run
ultravox_processor = UltravoxSTTService(
    model_name="fixie-ai/ultravox-v0_5-llama-3_1-8b",
    hf_token=os.getenv("HF_TOKEN"),
)


async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
    logger.info(f"Starting bot")

    transport = SmallWebRTCTransport(
        webrtc_connection=webrtc_connection,
        params=TransportParams(
            audio_in_enabled=True,
            audio_out_enabled=True,
            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
        ),
    )

    tts = CartesiaTTSService(
        api_key=os.environ.get("CARTESIA_API_KEY"),
        voice_id="97f4b8fb-f2fe-444b-bb9a-c109783a857a",
    )

    pipeline = Pipeline(
        [
            transport.input(),  # Transport user input
            ultravox_processor,
            tts,  # TTS
            transport.output(),  # Transport bot output
        ]
    )

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

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info(f"Client connected")

    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(transport, client):
        logger.info(f"Client disconnected")

    @transport.event_handler("on_client_closed")
    async def on_client_closed(transport, client):
        logger.info(f"Client closed connection")
        await task.cancel()

    runner = PipelineRunner(handle_sigint=False)

    await runner.run(task)


if __name__ == "__main__":
    from run import main

    main()