#
# 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.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 BaseTransport, TransportParams
from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
from pipecat.transports.services.daily import DailyParams

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"),
)


# We store functions so objects (e.g. SileroVADAnalyzer) don't get
# instantiated. The function will be called when the desired transport gets
# selected.
transport_params = {
    "daily": lambda: DailyParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        vad_analyzer=SileroVADAnalyzer(),
    ),
    "twilio": lambda: FastAPIWebsocketParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        vad_analyzer=SileroVADAnalyzer(),
    ),
    "webrtc": lambda: TransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        vad_analyzer=SileroVADAnalyzer(),
    ),
}


async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
    logger.info(f"Starting bot")

    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(
            enable_metrics=True,
            enable_usage_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")
        await task.cancel()

    runner = PipelineRunner(handle_sigint=handle_sigint)

    await runner.run(task)


if __name__ == "__main__":
    from pipecat.examples.run import main

    main(run_example, transport_params=transport_params)