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

import argparse
import os

from dotenv import load_dotenv
from loguru import logger

from pipecat.audio.vad.silero import SileroVADAnalyzer
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.services.deepgram.stt import DeepgramSTTService
from pipecat.services.deepgram.tts import DeepgramTTSService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
from pipecat.transports.services.daily import DailyTransportMessageFrame

load_dotenv(override=True)


async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
    logger.info(f"Starting bot")

    transport = SmallWebRTCTransport(
        webrtc_connection=webrtc_connection,
        params=TransportParams(
            audio_in_enabled=True,
            audio_out_enabled=True,
            vad_analyzer=SileroVADAnalyzer(),
        ),
    )

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

    tts = DeepgramTTSService(
        api_key=os.getenv("DEEPGRAM_API_KEY"),
        voice="aura-asteria-en",
        base_url="http://0.0.0.0:8080",
    )

    llm = OpenAILLMService(
        # To use OpenAI
        # api_key=os.getenv("OPENAI_API_KEY"),
        # Or, to use a local vLLM (or similar) api server
        model="meta-llama/Meta-Llama-3-8B-Instruct",
        base_url="http://0.0.0.0:8000/v1",
    )

    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 and helpful way.",
        },
    ]

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

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

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

    # When the first participant joins, the bot should introduce itself.
    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info(f"Client connected")
        # Kick off the conversation.
        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
        await task.queue_frames([context_aggregator.user().get_context_frame()])

    # Handle "latency-ping" messages. The client will send app messages that look like
    # this:
    #   { "latency-ping": { ts: <client-side timestamp> }}
    #
    # We want to send an immediate pong back to the client from this handler function.
    # Also, we will push a frame into the top of the pipeline and send it after the
    #
    @transport.event_handler("on_app_message")
    async def on_app_message(transport, message, sender):
        try:
            if "latency-ping" in message:
                logger.debug(f"Received latency ping app message: {message}")
                ts = message["latency-ping"]["ts"]
                # Send immediately
                transport.output().send_message(
                    DailyTransportMessageFrame(
                        message={"latency-pong-msg-handler": {"ts": ts}}, participant_id=sender
                    )
                )
                # And push to the pipeline for the Daily transport.output to send
                await task.queue_frame(
                    DailyTransportMessageFrame(
                        message={"latency-pong-pipeline-delivery": {"ts": ts}},
                        participant_id=sender,
                    )
                )
        except Exception as e:
            logger.debug(f"message handling error: {e} - {message}")

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

    @transport.event_handler("on_client_closed")
    async def on_client_closed(transport, client):
        logger.info(f"Client closed connection")
        await task.cancel()

    runner = PipelineRunner(handle_sigint=False)

    await runner.run(task)


if __name__ == "__main__":
    from run import main

    main()