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

import os
from asyncio.transports import BaseTransport

from dotenv import load_dotenv
from loguru import logger
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,
    LLMUserAggregatorParams,
)
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import parse_telephony_websocket
from pipecat.serializers.plivo import PlivoFrameSerializer
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import BaseTransport
from pipecat.transports.websocket.fastapi import (
    FastAPIWebsocketParams,
    FastAPIWebsocketTransport,
)
from pipecat.turns.user_stop import TurnAnalyzerUserTurnStopStrategy
from pipecat.turns.user_turn_strategies import UserTurnStrategies

load_dotenv()


async def run_bot(transport: BaseTransport, handle_sigint: bool):
    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))

    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))

    tts = CartesiaTTSService(
        api_key=os.getenv("CARTESIA_API_KEY"),
        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
    )

    messages = [
        {
            "role": "system",
            "content": "You are an elementary teacher in an audio call. Your output will be converted to audio so don't include special characters in your answers. Respond to what the student said in a short short sentence.",
        },
    ]

    context = LLMContext(messages)
    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
        context,
        user_params=LLMUserAggregatorParams(
            user_turn_strategies=UserTurnStrategies(
                stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
            ),
        ),
    )

    pipeline = Pipeline(
        [
            transport.input(),  # Websocket input from client
            stt,  # Speech-To-Text
            user_aggregator,
            llm,  # LLM
            tts,  # Text-To-Speech
            transport.output(),  # Websocket output to client
            assistant_aggregator,
        ]
    )

    task = PipelineTask(
        pipeline,
        params=PipelineParams(
            audio_in_sample_rate=8000,
            audio_out_sample_rate=8000,
            enable_metrics=True,
            enable_usage_metrics=True,
        ),
    )

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        # 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):
        await task.cancel()

    runner = PipelineRunner(handle_sigint=handle_sigint)

    await runner.run(task)


async def bot(runner_args: RunnerArguments):
    """Main bot entry point compatible with Pipecat Cloud."""

    transport_type, call_data = await parse_telephony_websocket(runner_args.websocket)
    logger.info(f"Auto-detected transport: {transport_type}")

    serializer = PlivoFrameSerializer(
        stream_id=call_data["stream_id"],
        call_id=call_data["call_id"],
        auth_id=os.getenv("PLIVO_AUTH_ID", ""),
        auth_token=os.getenv("PLIVO_AUTH_TOKEN", ""),
    )

    transport = FastAPIWebsocketTransport(
        websocket=runner_args.websocket,
        params=FastAPIWebsocketParams(
            audio_in_enabled=True,
            audio_out_enabled=True,
            add_wav_header=False,
            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
            serializer=serializer,
        ),
    )

    handle_sigint = runner_args.handle_sigint

    await run_bot(transport, handle_sigint)