app.py

import os
import gradio as gr
from transformers import pipeline

# --------------------------------------------------
# Optional: private model support
# --------------------------------------------------
HF_TOKEN = os.getenv("HF_TOKEN", None)

MODEL_ID = "Neurai/Persian_G2P"
# Example private model:
# MODEL_ID = "username/private-model"

# --------------------------------------------------
# Load pipeline
# --------------------------------------------------
pipe = pipeline(
    "summarization",
    model=MODEL_ID,
    token=HF_TOKEN,        # works for private models
    device_map="auto"      # GPU if available
)

# --------------------------------------------------
# Inference function
# --------------------------------------------------
def generate(text):
    output = pipe(text)
    return output[0]['summary_text']

# --------------------------------------------------
# Gradio UI
# --------------------------------------------------
demo = gr.Interface(
    fn=generate,
    inputs=[
        gr.Textbox(lines=4, label="Grapheme",value="نه بابا چی میگی این عدده نه هزار رو پانصد و بیست و نه هست"),
    ],
    outputs=gr.Textbox(lines=4, label="Phoneme"),
    title="Neura Persian G2P",
    description="Neura Persian Grapheme to Phenoneme"
)

demo.launch()