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

import asyncio
import os
import sys
import wave

import aiohttp
from dotenv import load_dotenv
from loguru import logger
from runner import configure

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
    Frame,
    LLMFullResponseEndFrame,
    OutputAudioRawFrame,
)
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.aggregators.openai_llm_context import (
    OpenAILLMContext,
    OpenAILLMContextFrame,
)
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.processors.logger import FrameLogger
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport

load_dotenv(override=True)

logger.remove(0)
logger.add(sys.stderr, level="DEBUG")


sounds = {}
sound_files = ["ding1.wav", "ding2.wav"]

script_dir = os.path.dirname(__file__)

for file in sound_files:
    # Build the full path to the image file
    full_path = os.path.join(script_dir, "assets", file)
    # Get the filename without the extension to use as the dictionary key
    filename = os.path.splitext(os.path.basename(full_path))[0]
    # Open the image and convert it to bytes
    with wave.open(full_path) as audio_file:
        sounds[file] = OutputAudioRawFrame(
            audio_file.readframes(-1), audio_file.getframerate(), audio_file.getnchannels()
        )


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

        if isinstance(frame, LLMFullResponseEndFrame):
            await self.push_frame(sounds["ding1.wav"])
            # In case anything else downstream needs it
            await self.push_frame(frame, direction)
        else:
            await self.push_frame(frame, direction)


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

        if isinstance(frame, OpenAILLMContextFrame):
            await self.push_frame(sounds["ding2.wav"])
            # In case anything else downstream needs it
            await self.push_frame(frame, direction)
        else:
            await self.push_frame(frame, direction)


async def main():
    async with aiohttp.ClientSession() as session:
        (room_url, token) = await configure(session)

        transport = DailyTransport(
            room_url,
            token,
            "Respond bot",
            DailyParams(
                audio_out_enabled=True,
                transcription_enabled=True,
                vad_enabled=True,
                vad_analyzer=SileroVADAnalyzer(),
            ),
        )

        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

        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 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. Respond to what the user said in a creative and helpful way.",
            },
        ]

        context = OpenAILLMContext(messages)
        context_aggregator = llm.create_context_aggregator(context)
        out_sound = OutboundSoundEffectWrapper()
        in_sound = InboundSoundEffectWrapper()
        fl = FrameLogger("LLM Out")
        fl2 = FrameLogger("Transcription In")

        pipeline = Pipeline(
            [
                transport.input(),
                context_aggregator.user(),
                in_sound,
                fl2,
                llm,
                fl,
                tts,
                out_sound,
                transport.output(),
                context_aggregator.assistant(),
            ]
        )

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
            await transport.capture_participant_transcription(participant["id"])
            await tts.say("Hi, I'm listening!")
            await transport.send_audio(sounds["ding1.wav"])

        runner = PipelineRunner()

        task = PipelineTask(pipeline)

        await runner.run(task)


if __name__ == "__main__":
    asyncio.run(main())