app.py

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import gradio as gr     

def envit5_translation(text):
    inputs = [f"en: {text}"]
    outputs = model.generate(tokenizer(inputs, return_tensors="pt", padding=True).input_ids, max_length=512)
    results = tokenizer.batch_decode(outputs, skip_special_tokens=True)
    return results[0][4:]

def my_translation(text):
    return "My Translation"

def finetune_BERT(text):
    return "BERT"

def translation(text):
    output1 = my_translation(text)
    output2 = envit5_translation(text)
    output3 = finetune_BERT(text)

    return (output1, output2, output3)

if __name__ == "__main__":
    model_name = "VietAI/envit5-translation"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

    inputs = [
    "textbox"
    ]

    with gr.Blocks() as app:
        gr.Interface(
            fn=translation,
            inputs=inputs,
            outputs=["textbox", "textbox", "textbox"]
            )

    app.launch(shared = True)