app.py

#!pip install gradio
import os
import torch
import gradio as gr
import time
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

def translation(text):

    model_checkpoint = "bigscience/mt0-base"
    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
    #inference
    inputs = tokenizer("translate to darija : " + text, return_tensors="pt")
    output = model.generate(**inputs)
    output = tokenizer.decode(output.cpu().numpy()[0], skip_special_tokens=True)

    return output


if __name__ == '__main__':
    print('\tinit models')

    #inputs = [gr.inputs.Radio(['nllb-distilled-600M', 'nllb-1.3B', 'nllb-distilled-1.3B'], label='NLLB Model'),
    inputs = [gr.inputs.Textbox(lines=5, label="Input text")]

    outputs = gr.outputs.Textbox(label="Input text")

    title = "NLP Translation model from english to darija"

    demo_status = "Demo is running on CPU"
    description = f"Details: https://github.com/facebookresearch/fairseq/tree/nllb. {demo_status}"
    examples = [
    ['English', 'Darija', 'Hi nice to meet you']
    ]

    gr.Interface(translation,
                 inputs,
                 outputs,
                 title=title,
                 description=description,
                 ).launch()