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import gradio as gr |
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from translation import Translator, LANGUAGES, MODEL_URL |
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LANGUAGES_LIST = list(LANGUAGES.keys()) |
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def translate_wrapper(text, src, trg, by_sentence=True, preprocess=True, random=False, num_beams=4): |
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src_lang = LANGUAGES.get(src) |
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tgt_lang = LANGUAGES.get(trg) |
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result = translator.translate( |
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text=text, |
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src_lang=src_lang, |
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tgt_lang=tgt_lang, |
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do_sample=random, |
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num_beams=int(num_beams), |
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by_sentence=by_sentence, |
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preprocess=preprocess, |
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) |
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return result |
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article = f""" |
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This is the demo for a NLLB-200-600M model fine-tuned for a few (mostly new) languages. |
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The model itself is available at https://huggingface.co/{MODEL_URL} |
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If you want to host in on your own backend, consider running this dockerized app: https://github.com/slone-nlp/nllb-docker-demo. |
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""" |
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interface = gr.Interface( |
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translate_wrapper, |
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[ |
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gr.Textbox(label="Text", lines=2, placeholder='text to translate '), |
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gr.Dropdown(LANGUAGES_LIST, type="value", label='source language', value=LANGUAGES_LIST[0]), |
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gr.Dropdown(LANGUAGES_LIST, type="value", label='target language', value=LANGUAGES_LIST[1]), |
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gr.Checkbox(label="by sentence", value=True), |
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gr.Checkbox(label="text preprocesing", value=True), |
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gr.Checkbox(label="randomize", value=False), |
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gr.Dropdown([1, 2, 3, 4, 5], label="number of beams", value=4), |
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], |
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"text", |
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title='Erzya-Russian translation', |
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article=article, |
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) |
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if __name__ == '__main__': |
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translator = Translator() |
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interface.launch() |
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