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import gradio as gr
from transformers import M2M100ForConditionalGeneration
from tokenization_small100 import SMALL100Tokenizer
langs = """af,am,ar,ast,az,ba,be,bg,bn,br,bs,ca,ceb,cs,cy,da,de,el,en,es,et,fa,ff,fi,fr,fy,ga,gd,gl,gu,ha,he,hi,hr,ht,hu,hy,id,ig,ilo,is,it,ja,jv,ka,kk,km,kn,ko,lb,lg,ln,lo,lt,lv,mg,mk,ml,mn,mr,ms,my,ne,nl,no,ns,oc,or,pa,pl,ps,pt,ro,ru,sd,si,sk,sl,so,sq,sr,ss,su,sv,sw,ta,th,tl,tn,tr,uk,ur,uz,vi,wo,xh,yi,yo,zh,zu"""
lang_list = langs.split(',')
model = M2M100ForConditionalGeneration.from_pretrained("alirezamsh/small100")
tokenizer = SMALL100Tokenizer.from_pretrained("alirezamsh/small100")
def translate(lang, text):
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]
with gr.Blocks(analytics_enabled=False) as app:
Source = gr.Textbox( label="Source" )
Language = gr.Dropdown( lang_list, label="Language" )
Translate = gr.Button( "Translate" )
Result = gr.Textbox( label="Result" )
Info = gr.Markdown( "# [$hyoo_lingua](https://lingua.hyoo.ru/)" )
Translate.click(
translate,
inputs=[ Language, Source ],
outputs=[Result],
api_name="translate",
)
app.launch( inline=True )
block.queue( concurrency_count=2 )
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