import asyncio
import base64
import json
from pathlib import Path

import gradio as gr
import numpy as np
import openai
from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.responses import HTMLResponse, StreamingResponse
from fastrtc import (
    AdditionalOutputs,
    AsyncStreamHandler,
    Stream,
    get_twilio_turn_credentials,
    wait_for_item,
)
from gradio.utils import get_space

load_dotenv()

cur_dir = Path(__file__).parent

SAMPLE_RATE = 24000


class OpenAIHandler(AsyncStreamHandler):
    def __init__(
        self,
    ) -> None:
        super().__init__(
            expected_layout="mono",
            output_sample_rate=SAMPLE_RATE,
            input_sample_rate=SAMPLE_RATE,
        )
        self.connection = None
        self.output_queue = asyncio.Queue()

    def copy(self):
        return OpenAIHandler()

    async def start_up(
        self,
    ):
        """Connect to realtime API. Run forever in separate thread to keep connection open."""
        self.client = openai.AsyncOpenAI()
        async with self.client.beta.realtime.connect(
            model="gpt-4o-mini-realtime-preview-2024-12-17"
        ) as conn:
            await conn.session.update(
                session={
                    "turn_detection": {"type": "server_vad"},
                    "input_audio_transcription": {
                        "model": "whisper-1",
                        "language": "en",
                    },
                }
            )
            self.connection = conn
            async for event in self.connection:
                # Handle interruptions
                if event.type == "input_audio_buffer.speech_started":
                    self.clear_queue()
                if (
                    event.type
                    == "conversation.item.input_audio_transcription.completed"
                ):
                    await self.output_queue.put(
                        AdditionalOutputs({"role": "user", "content": event.transcript})
                    )
                if event.type == "response.audio_transcript.done":
                    await self.output_queue.put(
                        AdditionalOutputs(
                            {"role": "assistant", "content": event.transcript}
                        )
                    )
                if event.type == "response.audio.delta":
                    await self.output_queue.put(
                        (
                            self.output_sample_rate,
                            np.frombuffer(
                                base64.b64decode(event.delta), dtype=np.int16
                            ).reshape(1, -1),
                        ),
                    )

    async def receive(self, frame: tuple[int, np.ndarray]) -> None:
        if not self.connection:
            return
        _, array = frame
        array = array.squeeze()
        audio_message = base64.b64encode(array.tobytes()).decode("utf-8")
        await self.connection.input_audio_buffer.append(audio=audio_message)  # type: ignore

    async def emit(self) -> tuple[int, np.ndarray] | AdditionalOutputs | None:
        return await wait_for_item(self.output_queue)

    async def shutdown(self) -> None:
        if self.connection:
            await self.connection.close()
            self.connection = None


def update_chatbot(chatbot: list[dict], response: dict):
    chatbot.append(response)
    return chatbot


chatbot = gr.Chatbot(type="messages")
latest_message = gr.Textbox(type="text", visible=False)
stream = Stream(
    OpenAIHandler(),
    mode="send-receive",
    modality="audio",
    additional_inputs=[chatbot],
    additional_outputs=[chatbot],
    additional_outputs_handler=update_chatbot,
    rtc_configuration=get_twilio_turn_credentials() if get_space() else None,
    concurrency_limit=5 if get_space() else None,
    time_limit=90 if get_space() else None,
)

app = FastAPI()

stream.mount(app)


@app.get("/")
async def _():
    rtc_config = get_twilio_turn_credentials() if get_space() else None
    html_content = (cur_dir / "index.html").read_text()
    html_content = html_content.replace("__RTC_CONFIGURATION__", json.dumps(rtc_config))
    return HTMLResponse(content=html_content)


@app.get("/outputs")
def _(webrtc_id: str):
    async def output_stream():
        import json

        async for output in stream.output_stream(webrtc_id):
            s = json.dumps(output.args[0])
            yield f"event: output\ndata: {s}\n\n"

    return StreamingResponse(output_stream(), media_type="text/event-stream")


if __name__ == "__main__":
    import os

    if (mode := os.getenv("MODE")) == "UI":
        stream.ui.launch(server_port=7860)
    elif mode == "PHONE":
        stream.fastphone(host="0.0.0.0", port=7860)
    else:
        import uvicorn

        uvicorn.run(app, host="0.0.0.0", port=7860)
