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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
import gradio as gr
DEBUG_UI = False
LANGS = {
'English': 'eng_Latn',
'Interslavic': 'isv_Latn',
# 'Интерславик': 'isv_Cyrl',
'Russian': 'rus_Cyrl',
'Belarusian': 'bel_Cyrl',
'Ukrainian': 'ukr_Cyrl',
'Polish': 'pol_Latn',
'Silesian': 'szl_Latn',
'Czech': 'ces_Latn',
'Slovak': 'slk_Latn',
'Slovenian': 'slv_Latn',
'Croatian': 'hrv_Latn',
'Bosnian': 'bos_Latn',
'Serbian': 'srp_Cyrl',
'Macedonian': 'mkd_Cyrl',
'Bulgarian': 'bul_Cyrl',
'Esperanto': 'epo_Latn',
'German': 'deu_Latn',
'French': 'fra_Latn',
'Spanish': 'spa_Latn',
}
if DEBUG_UI:
def translate(text, src_lang, tgt_lang):
return text
else:
# model_name = 'Salavat/nllb-200-distilled-600M-finetuned-isv'
model_name = 'Salavat/nllb-200-distilled-600M-finetuned-isv_v2'
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def get_lang(lang):
if lang in LANGS:
return LANGS[lang]
return lang
def translate(text, from_, to_):
"""
Translate the text from source lang to target lang
"""
translation_pipeline = pipeline(
"translation", model=model, tokenizer=tokenizer, max_length=400,
src_lang=LANGS.get(from_, from_), tgt_lang=LANGS.get(to_, to_)
)
result = translation_pipeline(text.split('\n'))
output = '\n'.join(line['translation_text'] for line in result)
return output
with gr.Blocks() as demo:
gr.Markdown("# Interslavic translator via NLLB200")
gr.Markdown("This is a modified version of the original [NLLB-Translator](https://huggingface.co./spaces/Narrativaai/NLLB-Translator) space")
with gr.Row():
lang_input = gr.components.Dropdown(label="From", choices=list(LANGS.keys()), value='English')
lang_output = gr.components.Dropdown(label="To", choices=list(LANGS.keys()), value='Interslavic')
with gr.Row().style(equal_height=True):
text_input = gr.components.Textbox(label="Text", lines=5, placeholder="Your text")
text_output = gr.components.Textbox(label="Result", lines=5, placeholder="Translation...")
button = gr.Button("Translate")
button.click(translate, inputs=[text_input, lang_input, lang_output], outputs=text_output)
demo.launch()
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