import spaces
import torch

import gradio as gr
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read

import tempfile
import os

MODEL_NAME = "alakxender/whisper-large-dv-a40"
BATCH_SIZE = 8
FILE_LIMIT_MB = 1000

device = 0 if torch.cuda.is_available() else "cpu"

pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    device=device,
)


@spaces.GPU
def transcribe(inputs):
    if inputs is None:
        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")

    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": "transcribe"}, return_timestamps=True)["text"]
    return  text


# Custom CSS with modern Gradio styling
custom_css = """
.thaana-textbox textarea {
    font-size: 18px !important;
    font-family: 'MV_Faseyha', 'Faruma', 'A_Faruma', 'Noto Sans Thaana', 'MV Boli' !important;
    line-height: 1.8 !important;
    direction: rtl !important;
}
"""

demo = gr.Blocks(css=custom_css)

file_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(type="filepath", label="Audio file"),
    ],
    outputs= gr.Textbox(
            label="",
            lines=2,
            elem_classes=["thaana-textbox"],
            rtl=True
        ),
    title="Whisper Large V3: Transcribe Audio",
    description=(
        ""
    ),
    allow_flagging="never",
)


with demo:
    gr.TabbedInterface([file_transcribe], ["Audio file"])

demo.queue().launch()