import logging
import tempfile

import gradio as gr
import numpy as np
from dotenv import load_dotenv
from gradio_webrtc import AdditionalOutputs, ReplyOnPause, WebRTC
from openai import OpenAI
from pydub import AudioSegment

load_dotenv()


# Configure the root logger to WARNING to suppress debug messages from other libraries
logging.basicConfig(level=logging.WARNING)

# Create a console handler
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.DEBUG)

# Create a formatter
formatter = logging.Formatter("%(name)s - %(levelname)s - %(message)s")
console_handler.setFormatter(formatter)

# Configure the logger for your specific library
logger = logging.getLogger("gradio_webrtc")
logger.setLevel(logging.DEBUG)
logger.addHandler(console_handler)


client = OpenAI()


def transcribe(audio: tuple[int, np.ndarray], transcript: list[dict]):
    segment = AudioSegment(
        audio[1].tobytes(),
        frame_rate=audio[0],
        sample_width=audio[1].dtype.itemsize,
        channels=1,
    )

    with tempfile.NamedTemporaryFile(suffix=".mp3") as temp_audio:
        segment.export(temp_audio.name, format="mp3")
        next_chunk = client.audio.transcriptions.create(
            model="whisper-1", file=open(temp_audio.name, "rb")
        ).text
        transcript.append({"role": "user", "content": next_chunk})
        yield AdditionalOutputs(transcript)


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            audio = WebRTC(
                label="Stream",
                mode="send",
                modality="audio",
            )
        with gr.Column():
            transcript = gr.Chatbot(label="transcript", type="messages")

    audio.stream(ReplyOnPause(transcribe), inputs=[audio, transcript], outputs=[audio],
                 time_limit=30)
    audio.on_additional_outputs(lambda s: s, outputs=transcript)

if __name__ == "__main__":
    demo.launch()