| import gradio as gr | |
| import numpy as np | |
| from huggingface_hub import hf_hub_download | |
| from transformers import pipeline | |
| REPO_ID_NLLB = "facebook/nllb-200-distilled-600M" | |
| REPO_ID_MARIANNMT_en = "mbarnig/MarianNMT-tatoeba-en-lb" | |
| REPO_ID_MARIANNMT_lb = "mbarnig/MarianNMT-tatoeba-lb-en" | |
| REPO_ID_T5MT5 = "mbarnig/T5-mt5-tatoeba-en-lb" | |
| translator = pipeline("translation", model=REPO_ID_NLLB) | |
| my_title = "🇫🇷 Mir iwwersetzen vun an op Lëtzebuergesch ! 🇬🇧" | |
| my_description = "English-Luxembourgish machine translation (MT) demo based on 3 transformer models: NLLB, MarianNMT & T5/mt5." | |
| my_article = "abc" | |
| TRANSLATION_MODELS = [ | |
| "NLLB", | |
| "MarianNMT", | |
| "T5/mt5" | |
| ] | |
| TRANSLATION_DIRECTION = [ | |
| "en -> lb", | |
| "lb -> en" | |
| ] | |
| EXAMPLE = "..." | |
| my_input = gr.Textbox(label="Input") | |
| my_output = gr.Textbox(label="Translation") | |
| def iwwersetz(source_text): | |
| translation = translator(source_text) | |
| return translation | |
| demo=gr.Interface( | |
| fn=iwwersetz, | |
| inputs=my_input, | |
| outputs=my_output, | |
| title=my_title, | |
| description=my_description, | |
| article=my_article, | |
| examples=EXAMPLE, | |
| allow_flagging=False) | |
| demo.launch() |