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

import asyncio
import os
import sys

import aiohttp
from dotenv import load_dotenv
from loguru import logger

from pipecat.audio.vad.silero import SileroVADAnalyzer
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.services.deepgram.stt import DeepgramSTTService
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
from pipecat.services.groq.llm import GroqLLMService
from pipecat.transports.lemonslice.transport import (
    LemonSliceNewSessionRequest,
    LemonSliceParams,
    LemonSliceTransport,
)

load_dotenv(override=True)

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


async def main():
    async with aiohttp.ClientSession() as session:
        transport = LemonSliceTransport(
            bot_name="Pipecat",
            api_key=os.getenv("LEMONSLICE_API_KEY"),
            session=session,
            session_request=LemonSliceNewSessionRequest(
                agent_id=os.getenv("LEMONSLICE_AGENT_ID"),
            ),
            params=LemonSliceParams(
                audio_in_enabled=True,
                audio_out_enabled=True,
                microphone_out_enabled=False,
            ),
        )

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

        llm = GroqLLMService(api_key=os.getenv("GROQ_API_KEY"))

        tts = ElevenLabsTTSService(
            api_key=os.getenv("ELEVENLABS_API_KEY", ""),
            voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
        )

        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 spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
            },
        ]

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

        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
            ]
        )

        task = PipelineTask(
            pipeline,
            params=PipelineParams(
                audio_in_sample_rate=16000,
                audio_out_sample_rate=16000,
                enable_metrics=True,
                enable_usage_metrics=True,
            ),
        )

        @transport.event_handler("on_client_connected")
        async def on_client_connected(transport, participant):
            logger.info("Client connected")
            # Kick off the conversation.
            messages.append(
                {
                    "role": "system",
                    "content": "Start by greeting the user and ask how you can help.",
                }
            )
            await task.queue_frames([LLMRunFrame()])

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

        runner = PipelineRunner()

        await runner.run(task)


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