#
# Copyright (c) 2024–2025, 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.audio.vad.vad_analyzer import VADParams
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.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.google.gemini_live.llm import (
    GeminiLiveLLMService,
    GeminiModalities,
    InputParams,
)
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams

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


# 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,
        # set stop_secs to something roughly similar to the internal setting
        # of the Multimodal Live api, just to align events. This doesn't really
        # matter because we can only use the Multimodal Live API's phrase
        # endpointing, for now.
        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
    ),
    "twilio": lambda: FastAPIWebsocketParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        # set stop_secs to something roughly similar to the internal setting
        # of the Multimodal Live api, just to align events. This doesn't really
        # matter because we can only use the Multimodal Live API's phrase
        # endpointing, for now.
        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
    ),
    "webrtc": lambda: TransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        # set stop_secs to something roughly similar to the internal setting
        # of the Multimodal Live api, just to align events. This doesn't really
        # matter because we can only use the Multimodal Live API's phrase
        # endpointing, for now.
        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
    ),
}


async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
    logger.info(f"Starting bot")

    # KNOWN ISSUE: If using GeminiLiveVertexLLMService, you cannot specify a
    # modality other than AUDIO (at least not if using the service's default
    # model, which is a native audio model:
    # https://cloud.google.com/vertex-ai/generative-ai/docs/live-api/tools#native-audio).
    llm = GeminiLiveLLMService(
        api_key=os.getenv("GOOGLE_API_KEY"),
        system_instruction=SYSTEM_INSTRUCTION,
        tools=[{"google_search": {}}, {"code_execution": {}}],
        params=InputParams(modalities=GeminiModalities.TEXT),
    )

    # Optionally, you can set the response modalities via a function
    # llm.set_model_modalities(
    #     GeminiMultimodalModalities.TEXT
    # )

    tts = CartesiaTTSService(
        api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
    )

    messages = [
        {
            "role": "user",
            "content": 'Start by saying "Hello, I\'m Gemini".',
        },
    ]

    # Set up conversation context and management
    # The context_aggregator will automatically collect conversation context
    context = LLMContext(messages)
    context_aggregator = LLMContextAggregatorPair(context)

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

    task = PipelineTask(
        pipeline,
        params=PipelineParams(
            enable_metrics=True,
            enable_usage_metrics=True,
        ),
        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
    )

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

    @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=runner_args.handle_sigint)

    await runner.run(task)


async def bot(runner_args: RunnerArguments):
    """Main bot entry point compatible with Pipecat Cloud."""
    transport = await create_transport(runner_args, transport_params)
    await run_bot(transport, runner_args)


if __name__ == "__main__":
    from pipecat.runner.run import main

    main()