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

import time

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.evals.transport import EvalTransportParams
from pipecat.frames.frames import (
    Frame,
    InterimTranscriptionFrame,
    TranscriptionFrame,
    UserStoppedSpeakingFrame,
)
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.worker import PipelineParams, PipelineWorker, ProcessorUnusablePolicy
from pipecat.processors.audio.vad_processor import VADProcessor
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.whisper.stt import MLXModel, WhisperSTTServiceMLX
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
from pipecat.workers.runner import WorkerRunner

load_dotenv(override=True)


STOP_SECS = 2.0


class TranscriptionLogger(FrameProcessor):
    """Measures transcription latency.

    Uses the (intentionally) long STOP_SECS parameter to give the transcription time to finish,
    then outputs the timing between when the VAD first classified audio input as not-speech and
    the delivery of the last transcription frame.
    """

    def __init__(self):
        super().__init__()
        self._last_transcription_time = time.time()

    async def process_frame(self, frame: Frame, direction: FrameDirection):
        await super().process_frame(frame, direction)

        if isinstance(frame, UserStoppedSpeakingFrame):
            logger.debug(
                f"Transcription latency: {(STOP_SECS - (time.time() - self._last_transcription_time)):.2f}"
            )
        elif isinstance(frame, TranscriptionFrame):
            self._last_transcription_time = time.time()
        elif isinstance(frame, InterimTranscriptionFrame):
            print(f"Interim transcription: {frame.text}")

        # Push all frames through
        await self.push_frame(frame, direction)


# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
transport_params = {
    "eval": lambda: EvalTransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
    ),
    "daily": lambda: DailyParams(
        audio_in_enabled=True,
    ),
    "twilio": lambda: FastAPIWebsocketParams(
        audio_in_enabled=True,
    ),
    "webrtc": lambda: TransportParams(
        audio_in_enabled=True,
    ),
}


async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
    logger.info("Starting bot")

    stt = WhisperSTTServiceMLX(
        settings=WhisperSTTServiceMLX.Settings(
            model=MLXModel.LARGE_V3_TURBO.value,
        ),
    )

    tl = TranscriptionLogger()

    vad_processor = VADProcessor(
        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=STOP_SECS))
    )

    pipeline = Pipeline([transport.input(), vad_processor, stt, tl, transport.output()])

    worker = PipelineWorker(
        pipeline,
        params=PipelineParams(
            enable_metrics=True,
            enable_usage_metrics=True,
        ),
        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
        processor_unusable_policy=ProcessorUnusablePolicy.END,
    )

    runner = WorkerRunner(handle_sigint=runner_args.handle_sigint)

    await runner.add_workers(worker)

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

    await runner.run()


async def bot(runner_args: RunnerArguments):
    """Main bot entry point compatible with Pipecat Cloud."""
    transport = await create_transport(runner_args, transport_params)
    await run_bot(transport, runner_args)


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

    main()