app.py

import gradio as gr
import os

os.system("pip install transformers sentencepiece torch")

from transformers import M2M100ForConditionalGeneration
from tokenization_small100 import SMALL100Tokenizer

langs = """Afrikaans (af), Amharic (am), Arabic (ar), Asturian (ast), Azerbaijani (az), Bashkir (ba), Belarusian (be), Bulgarian (bg), Bengali (bn), Breton (br), Bosnian (bs), Catalan; Valencian (ca), Cebuano (ceb), Czech (cs), Welsh (cy), Danish (da), German (de), Greeek (el), English (en), Spanish (es), Estonian (et), Persian (fa), Fulah (ff), Finnish (fi), French (fr), Western Frisian (fy), Irish (ga), Gaelic; Scottish Gaelic (gd), Galician (gl), Gujarati (gu), Hausa (ha), Hebrew (he), Hindi (hi), Croatian (hr), Haitian; Haitian Creole (ht), Hungarian (hu), Armenian (hy), Indonesian (id), Igbo (ig), Iloko (ilo), Icelandic (is), Italian (it), Japanese (ja), Javanese (jv), Georgian (ka), Kazakh (kk), Central Khmer (km), Kannada (kn), 
Korean (ko), Luxembourgish; Letzeburgesch (lb), Ganda (lg), Lingala (ln), Lao (lo), Lithuanian (lt), Latvian (lv), Malagasy (mg), Macedonian (mk), Malayalam (ml), Mongolian (mn), Marathi (mr), Malay (ms), Burmese (my), Nepali (ne), Dutch; Flemish (nl), Norwegian (no), Northern Sotho (ns), Occitan (post 1500) (oc), Oriya (or), Panjabi; Punjabi (pa), Polish (pl), Pushto; Pashto (ps), Portuguese (pt), Romanian; Moldavian; Moldovan (ro), Russian (ru), Sindhi (sd), Sinhala; Sinhalese (si), Slovak (sk), 
Slovenian (sl), Somali (so), Albanian (sq), Serbian (sr), Swati (ss), Sundanese (su), Swedish (sv), Swahili (sw), Tamil (ta), Thai (th), Tagalog (tl), Tswana (tn), 
Turkish (tr), Ukrainian (uk), Urdu (ur), Uzbek (uz), Vietnamese (vi), Wolof (wo), Xhosa (xh), Yiddish (yi), Yoruba (yo), Chinese (zh), Zulu (zu)"""
lang_list = [lang.strip() for lang in langs.split(',')]

model = M2M100ForConditionalGeneration.from_pretrained("alirezamsh/small100")
tokenizer = SMALL100Tokenizer.from_pretrained("alirezamsh/small100")

description = """This is an official demo for the paper [*SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages*](https://arxiv.org/abs/2210.11621) by Alireza Mohammadshahi, Vassilina Nikoulina, Alexandre Berard, Caroline Brun, James Henderson, Laurent Besacier

In this paper, they propose a compact and shallow massively multilingual MT model, and achieve competitive results with M2M-100, while being super smaller and faster. More details are provided [here](https://huggingface.co/alirezamsh/small100). Currently running on 2 vCPU - 16GB RAM."""

def small100_tr(lang, text):

    lang = lang.split(" ")[-1][1:-1]
    
    tokenizer.tgt_lang = lang
    encoded_text = tokenizer(text, return_tensors="pt")
    generated_tokens = model.generate(**encoded_text)
    return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]

examples = [["French (fr)", "Life is like a box of chocolates."]]

output_text = gr.outputs.Textbox()
gr.Interface(small100_tr, inputs=[gr.inputs.Dropdown(lang_list, label=" Target Language"), 'text'], outputs=output_text, title="SMaLL100: Translate much faster between 100 languages",
            description=description,
            examples=examples
            ).launch()