app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

# Load model and tokenizer
model_name = "VietAI/envit5-translation"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

def translate(text, source_lang):
    """Translate text based on the source language."""
    input_text = f"{source_lang}: {text}"
    inputs = tokenizer(input_text, return_tensors="pt", padding=True).input_ids.to('cpu')
    outputs = model.generate(inputs, max_length=512)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Create UI
demo = gr.Interface(
    fn=translate,
    inputs=[gr.Textbox(label="Input Text"), gr.Radio(["vi", "en"], label="Source Language")],
    outputs=gr.Textbox(label="Translated Text"),
    title="VietAI Translation",
    description="Translate between Vietnamese and English using envit5-translation model."
)

# Launch app
demo.launch()