app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, T5ForConditionalGeneration

MODEL_NAME = "google/byt5-small"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME)
model.eval()

def text_to_ipa(text):
    prompt = f"Text: {text}\nIPA:"
    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        outputs = model.generate(**inputs, max_new_tokens=64)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

with gr.Blocks() as demo:
    input_text = gr.Textbox()
    output_text = gr.Textbox()

    btn = gr.Button("Generate")

    btn.click(
        text_to_ipa,
        inputs=input_text,
        outputs=output_text,
        api_name="predict"
    )

demo.launch()