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import gradio as gr | |
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline | |
import torch | |
from langs import LANGS | |
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M") | |
tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M") | |
source = 'som_Latn' | |
target = "eng_Latn" | |
def translate(source, target, text): | |
translation_pipeline = pipeline("translation", | |
model=model, | |
tokenizer=tokenizer, | |
src_lang=source, | |
tgt_lang=target) | |
result = translation_pipeline(text) | |
return result[0]['translation_text'] | |
gr.Interface( | |
translate, | |
[ | |
gr.components.Dropdown(label="Source Language", choices=LANGS), | |
gr.components.Dropdown(label="Target Language", choices=LANGS), | |
gr.components.Textbox(label="Text") | |
], | |
["text"], | |
).launch() |