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


import os

import aiohttp
from dotenv import load_dotenv
from loguru import logger

from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.frames.frames import LLMRunFrame
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
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams

from pipecat_typecast import TypecastTTSService

load_dotenv(override=True)


# 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(params=VADParams(stop_secs=0.2)),
        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
    ),
    "twilio": lambda: FastAPIWebsocketParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
    ),
    "webrtc": lambda: TransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
    ),
}


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

    async with aiohttp.ClientSession() as session:
        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))

        typecast_kwargs = {}
        voice_id = os.getenv("TYPECAST_VOICE_ID")
        if voice_id:
            typecast_kwargs["voice_id"] = voice_id

        tts = TypecastTTSService(
            aiohttp_session=session,
            api_key=os.getenv("TYPECAST_API_KEY", ""),
            **typecast_kwargs,
        )

        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))

        messages = [
            {
                "role": "system",
                "content": (
                    "You are a helpful LLM in a WebRTC call. "
                    "Your goal is to demonstrate your capabilities succinctly. "
                    "Your output will be converted to audio, so avoid special characters."
                ),
            },
        ]

        context = LLMContext(messages)
        context_aggregator = LLMContextAggregatorPair(context)

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

        task = PipelineTask(
            pipeline,
            params=PipelineParams(
                enable_metrics=True,
                enable_usage_metrics=True,
            ),
            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
        )

        @transport.event_handler("on_client_connected")
        async def on_client_connected(transport, client):
            logger.info("Client connected")
            # Kick off the conversation.
            messages.append(
                {"role": "system", "content": "Please introduce yourself to the user."}
            )
            await task.queue_frames([LLMRunFrame()])

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

        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)

        await runner.run(task)


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()
