app.py

import gradio as gr
import os
import torch
import gradio as gr
from transformers import M2M100Tokenizer, M2M100ForConditionalGeneration

if torch.cuda.is_available():
    device = torch.device("cuda:0")
else:
    device = torch.device("cpu")

tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_1.2B")
model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_1.2B").to(device)
model.eval()

l1="Afrikaans"
class Language:
    def __init__(self, name, code):
        self.name = name
        self.code = code






lang_id = [
    "":"",
    Language("Afrikaans", "af"),
    Language("Albanian", "sq"),
    Language("Amharic", "am"),
    Language("Arabic", "ar"),
    Language("Armenian", "hy"),
    Language("Asturian", "ast"),
    Language("Azerbaijani", "az"),
    Language("Bashkir", "ba"),
    Language("Belarusian", "be"),
    Language("Bulgarian", "bg"),
    Language("Bengali", "bn"),
    Language("Breton", "br"),
    Language("Bosnian", "bs"),
    Language("Burmese", "my"),
    Language("Catalan", "ca"),
    Language("Cebuano", "ceb"),
    Language("Chinese","zh"),
    Language("Croatian","hr"),
    Language("Czech","cs"),
    Language("Danish","da"),
    Language("Dutch","nl"),
    Language("English","en"),
    Language("Estonian","et"),
    Language("Fulah","ff"),
    Language("Finnish","fi"),
    Language("French","fr"),
    Language("Western Frisian","fy"),
    Language("Gaelic","gd"),
    Language("Galician","gl"),
    Language("Georgian","ka"),
    Language("German","de"),
    Language("Greek","el"),
    Language("Gujarati","gu"),
    Language("Hausa","ha"),
    Language("Hebrew","he"),
    Language("Hindi","hi"),
    Language("Haitian","ht"),
    Language("Hungarian","hu"),
    Language("Irish","ga"),
    Language("Indonesian","id"),
    Language("Igbo","ig"),
    Language("",""),
    Language("",""),
    Language("",""),
    Language("",""),
    Language("",""),
    Language("",""),
    Language("",""),
    Language("",""),
    Language("",""),
    Language("",""),
    Language("",""),
    Language("",""),
    Language("",""),
"Iloko": "ilo",
"Icelandic": "is",
"Italian": "it",
"Japanese": "ja",
"Javanese": "jv",
"Kazakh": "kk",
"Central Khmer": "km",
"Kannada": "kn",
"Korean": "ko",
"Luxembourgish": "lb",
"Ganda": "lg",
"Lingala": "ln",
"Lao": "lo",
"Lithuanian": "lt",
"Latvian": "lv",
"Malagasy": "mg",
"Macedonian": "mk",
"Malayalam": "ml",
"Mongolian": "mn",
"Marathi": "mr",
"Malay": "ms",
"Nepali": "ne",
"Norwegian": "no",
"Northern Sotho": "ns",
"Occitan": "oc",
"Oriya": "or",
"Panjabi": "pa",
"Persian": "fa",
"Polish": "pl",
"Pushto": "ps",
"Portuguese": "pt",
"Romanian": "ro",
"Russian": "ru",
"Sindhi": "sd",
"Sinhala": "si",
"Slovak": "sk",
"Slovenian": "sl",
"Spanish": "es",
"Somali": "so",
"Serbian": "sr",
"Serbian (cyrillic)": "sr",
"Serbian (latin)": "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",
"Welsh": "cy",
"Wolof": "wo",
"Xhosa": "xh",
"Yiddish": "yi",
"Yoruba": "yo",
"Zulu": "zu",
]

def trans_page(input,input1,trg):
    src_lang = lang_id["English"]
    trg_lang = lang_id[trg]
    if trg_lang != src_lang:
        
        tokenizer.src_lang = src_lang
        with torch.no_grad():
            encoded_input = tokenizer(input, return_tensors="pt").to(device)
            generated_tokens = model.generate(**encoded_input, forced_bos_token_id=tokenizer.get_lang_id(trg_lang))
            translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
    else:
        translated_text=input
        pass
    if trg_lang != src_lang:
        
        tokenizer.src_lang = src_lang
        with torch.no_grad():
            #lang_tr = lang_id
            encoded_input = tokenizer(lang_id, return_tensors="pt").to(device)
            generated_tokens = model.generate(**encoded_input, forced_bos_token_id=tokenizer.get_lang_id(trg_lang))
            translated_text1 = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
    else:
        translated_text1=input1
        pass
    return translated_text,gr.Dropdown.update(choices=list(translated_text1.keys()))


def trans_to(input,src,trg):
    src_lang = lang_id[src]
    trg_lang = lang_id[trg]
    if trg_lang != src_lang:
        tokenizer.src_lang = src_lang
        with torch.no_grad():
            encoded_input = tokenizer(input, return_tensors="pt").to(device)
            generated_tokens = model.generate(**encoded_input, forced_bos_token_id=tokenizer.get_lang_id(trg_lang))
            translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
    else:
        translated_text=input
        pass
    return translated_text

md1 = "Translate - 100 Languages"



with gr.Blocks() as transbot:
    #this=gr.State()
    with gr.Row():
        gr.Column()
        with gr.Column():
            with gr.Row():
                t_space = gr.Dropdown(label="Translate Space", choices=list(lang_id.keys()),value="English")
                t_submit = gr.Button("Translate Space")
        gr.Column()
        
    with gr.Row():
        gr.Column()
        with gr.Column():
            md = gr.Markdown("""<h1><center>Translate - 100 Languages</center></h1><h4><center>Translation may not be accurate</center></h4>""")
            with gr.Row():
                lang_from = gr.Dropdown(label="From:", choices=list(lang_id.keys()),value="English")
                lang_to = gr.Dropdown(label="To:", choices=list(lang_id.keys()),value="Chinese")
            submit = gr.Button("Go")
            with gr.Row():
                with gr.Column():
                    message = gr.Textbox(label="Prompt",placeholder="Enter Prompt",lines=4)
                    translated = gr.Textbox(label="Translated",lines=4,interactive=False)
        gr.Column()
    t_submit.click(trans_page,[md,lang_from,t_space],[md,lang_from])
    
    submit.click(trans_to, inputs=[message,lang_from,lang_to], outputs=[translated])
transbot.queue(concurrency_count=20)
transbot.launch()