#
# Copyright (c) 2025, 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.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
from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.smallwebrtc.transport import SmallWebRTCTransport

load_dotenv(override=True)

SYSTEM_INSTRUCTION = f"""
"You are Gemini Chatbot, a friendly, helpful robot.

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. Keep your responses brief. One or two sentences at most.
"""


async def run_bot(webrtc_connection):
    pipecat_transport = SmallWebRTCTransport(
        webrtc_connection=webrtc_connection,
        params=TransportParams(
            audio_in_enabled=True,
            audio_out_enabled=True,
            vad_analyzer=SileroVADAnalyzer(),
            audio_out_10ms_chunks=2,
        ),
    )

    llm = GeminiLiveLLMService(
        api_key=os.getenv("GOOGLE_API_KEY"),
        voice_id="Puck",  # Aoede, Charon, Fenrir, Kore, Puck
        system_instruction=SYSTEM_INSTRUCTION,
    )

    context = LLMContext(
        [
            {
                "role": "user",
                "content": "Start by greeting the user warmly and introducing yourself.",
            }
        ],
    )
    context_aggregator = LLMContextAggregatorPair(context)

    pipeline = Pipeline(
        [
            pipecat_transport.input(),
            context_aggregator.user(),
            llm,  # LLM
            pipecat_transport.output(),
            context_aggregator.assistant(),
        ]
    )

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

    @pipecat_transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info("Pipecat Client connected")
        # Kick off the conversation.
        await task.queue_frames([LLMRunFrame()])

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

    runner = PipelineRunner(handle_sigint=False)

    await runner.run(task)