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import gradio as gr | |
from transformers import AutoModelForSeq2SeqLM, NllbTokenizerFast | |
model_repo = "wtarit/nllb-600M-th-en" | |
model = AutoModelForSeq2SeqLM.from_pretrained(model_repo) | |
tokenizer = NllbTokenizerFast.from_pretrained(model_repo, src_lang="tha_Thai", tgt_lang="eng_Latn") | |
def translate(Text): | |
inputs = tokenizer(Text, return_tensors="pt") | |
translated_tokens = model.generate( | |
**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["eng_Latn"], max_length=64 | |
) | |
return tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0] | |
demo = gr.Interface( | |
fn=translate, | |
inputs=[ | |
gr.components.Textbox(placeholder="Enter Thai text here...") | |
], | |
outputs=["text"], | |
title="NLLB TH-EN Translation", | |
allow_flagging="never", | |
) | |
demo.launch() |