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

import os

from dotenv import load_dotenv
from loguru import logger

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.evals.transport import EvalTransportParams
from pipecat.frames.frames import LLMRunFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.worker import PipelineParams, PipelineWorker, ProcessorUnusablePolicy
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import (
    AssistantTurnStoppedMessage,
    LLMContextAggregatorPair,
    LLMUserAggregatorParams,
    UserTurnStoppedMessage,
)
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
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, 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)


class TranscriptHandler:
    """Handles real-time transcript processing and output.

    Maintains a list of conversation messages and outputs them either to a log
    or to a file as they are received. Each message includes its timestamp and role.

    Parameters:
        messages: List of all processed transcript messages
        output_file: Optional path to file where transcript is saved. If None, outputs to log only.
    """

    def __init__(self, output_file: str | None = None):
        """Initialize handler with optional file output.

        Args:
            output_file: Path to output file. If None, outputs to log only.
        """
        self.output_file: str | None = output_file
        logger.debug(
            f"TranscriptHandler initialized {'with output_file=' + output_file if output_file else 'with log output only'}"
        )

    async def save_message(self, role: str, content: str, timestamp: str):
        """Save a single transcript message.

        Outputs the message to the log and optionally to a file.

        Args:
            role: Who generated this transcript
            content: The transcript to save
        """
        line = f"[{timestamp}] {role}: {content}"

        # Always log the message
        logger.info(f"Transcript: {line}")

        # Optionally write to file
        if self.output_file:
            try:
                with open(self.output_file, "a", encoding="utf-8") as f:
                    f.write(line + "\n\n")
            except Exception as e:
                logger.error(f"Error saving transcript message to file: {e}")

    async def on_user_transcript(self, message: UserTurnStoppedMessage):
        """Handle new user transcript message.

        Args:
            message: The new user message
        """
        logger.debug("Received user transcript update")
        await self.save_message("user", message.content, message.timestamp)

    async def on_assistant_transcript(self, message: AssistantTurnStoppedMessage):
        """Handle new assistant transcript message.

        Args:
            message: The new assistant message
        """
        logger.debug("Received assistant transcript update")
        await self.save_message("assistant", message.content, message.timestamp)


# 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,
        audio_out_enabled=True,
    ),
    "twilio": lambda: FastAPIWebsocketParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
    ),
    "webrtc": lambda: TransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
    ),
}


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

    stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])

    tts = CartesiaTTSService(
        api_key=os.environ["CARTESIA_API_KEY"],
        settings=CartesiaTTSService.Settings(
            voice="86e30c1d-714b-4074-a1f2-1cb6b552fb49",
        ),
    )

    llm = OpenAILLMService(
        api_key=os.environ["OPENAI_API_KEY"],
        settings=OpenAILLMService.Settings(
            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
        ),
    )

    context = LLMContext()
    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
        context,
        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
    )

    # Create transcript processor and handler
    transcript_handler = TranscriptHandler()  # Output to log only
    # transcript_handler = TranscriptHandler(output_file="transcript.txt")  # Output to file and log

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

    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_connected")
    async def on_client_connected(transport, client):
        logger.info("Client connected")
        # Start conversation - empty prompt to let LLM follow system instructions
        context.add_message(
            {"role": "developer", "content": "Please introduce yourself to the user."}
        )
        await worker.queue_frames([LLMRunFrame()])

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

    @user_aggregator.event_handler("on_user_turn_stopped")
    async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMessage):
        await transcript_handler.on_user_transcript(message)

    @assistant_aggregator.event_handler("on_assistant_turn_stopped")
    async def on_assistant_turn_stopped(aggregator, message: AssistantTurnStoppedMessage):
        await transcript_handler.on_assistant_transcript(message)

    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()