import logging

from dotenv import load_dotenv
from google.genai import types  # noqa: F401

from livekit.agents import Agent, AgentServer, AgentSession, JobContext, cli
from livekit.agents.inference import TurnDetector
from livekit.plugins import deepgram, google, openai  # noqa: F401

logger = logging.getLogger("realtime-turn-detector")
logger.setLevel(logging.INFO)

load_dotenv()

## This example demonstrates how to use LiveKit's turn detection model with a realtime LLM.
## Since the current turn detection model runs in text space, it will need to be combined
## with a STT model, even though the audio is going directly to the Realtime API.
## In this example, speech is being processed in parallel by both the STT and the realtime API

server = AgentServer()


@server.rtc_session()
async def entrypoint(ctx: JobContext):
    session = AgentSession(
        allow_interruptions=True,
        turn_detection=TurnDetector(),
        stt=deepgram.STT(),
        # To use OpenAI Realtime API
        llm=openai.realtime.RealtimeModel(
            voice="alloy",
            # it's necessary to turn off turn detection in the OpenAI Realtime API in order to use
            # LiveKit's turn detection model
            turn_detection=None,
            input_audio_transcription=None,  # we use Deepgram STT instead
        ),
        # To use Gemini Live API
        # llm=google.realtime.RealtimeModel(
        #     realtime_input_config=types.RealtimeInputConfig(
        #         automatic_activity_detection=types.AutomaticActivityDetection(
        #             disabled=True,
        #         ),
        #     ),
        #     input_audio_transcription=None,
        # ),
    )
    await session.start(agent=Agent(instructions="You are a helpful assistant."), room=ctx.room)


if __name__ == "__main__":
    cli.run_app(server)