app.py

import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Helsinki-NLP/opus-mt-en-th"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prediction function
def translate_text(input_text):
    inputs = tokenizer.encode(input_text, return_tensors="pt")
    outputs = model.generate(
        inputs, 
        max_new_tokens=40, 
        do_sample=True, 
        top_k=30, 
        top_p=0.95
    )
    translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return translated_text

# Gradio interface
interface = gr.Interface(
    fn=translate_text,
    inputs="text",
    outputs="text",
    title="Language Translation",
    description="Translate text using the my_awesome_opus_books_model."
)

# Launch the Gradio app
interface.launch()