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

import argparse
import os
from typing import List, Optional

from dotenv import load_dotenv
from loguru import logger

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import TranscriptionMessage, TranscriptionUpdateFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.transcript_processor import TranscriptProcessor
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.network.fastapi_websocket import FastAPIWebsocketParams
from pipecat.transports.services.daily import DailyParams

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.

    Attributes:
        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: Optional[str] = None):
        """Initialize handler with optional file output.

        Args:
            output_file: Path to output file. If None, outputs to log only.
        """
        self.messages: List[TranscriptionMessage] = []
        self.output_file: Optional[str] = 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, message: TranscriptionMessage):
        """Save a single transcript message.

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

        Args:
            message: The message to save
        """
        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
        line = f"{timestamp}{message.role}: {message.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")
            except Exception as e:
                logger.error(f"Error saving transcript message to file: {e}")

    async def on_transcript_update(
        self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame
    ):
        """Handle new transcript messages.

        Args:
            processor: The TranscriptProcessor that emitted the update
            frame: TranscriptionUpdateFrame containing new messages
        """
        logger.debug(f"Received transcript update with {len(frame.messages)} new messages")

        for msg in frame.messages:
            self.messages.append(msg)
            await self.save_message(msg)


# 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")

    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
    )

    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 in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative, helpful, and brief way. Say hello.",
        },
    ]

    context = OpenAILLMContext(messages)
    context_aggregator = llm.create_context_aggregator(context)

    # Create transcript processor and handler
    transcript = TranscriptProcessor()
    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
            transcript.user(),  # User transcripts
            context_aggregator.user(),  # User responses
            llm,  # LLM
            tts,  # TTS
            transport.output(),  # Transport bot output
            transcript.assistant(),  # Assistant transcripts
            context_aggregator.assistant(),  # Assistant spoken responses
        ]
    )

    task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info(f"Client connected")
        # Start conversation - empty prompt to let LLM follow system instructions
        await task.queue_frames([context_aggregator.user().get_context_frame()])

    # Register event handler for transcript updates
    @transcript.event_handler("on_transcript_update")
    async def on_transcript_update(processor, frame):
        await transcript_handler.on_transcript_update(processor, frame)

    @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)