| import gradio as gr | |
| from transformers import pipeline | |
| import torch | |
| # pipe = pipeline("image-to-text", | |
| # model="Salesforce/blip-image-captioning-base") | |
| pipe = pipeline(task="translation", | |
| model="facebook/nllb-200-distilled-600M", | |
| torch_dtype=torch.bfloat16) | |
| # text_translated = pipe(text, | |
| # src_lang="eng_Latn", | |
| # tgt_lang="fra_Latn") | |
| def launch(input): | |
| out = pipe(input, src_lang="eng_Latn", | |
| tgt_lang="fra_Latn") | |
| return out[0]['translation_text'] | |
| iface = gr.Interface(launch, | |
| inputs="text", | |
| outputs="text") | |
| iface.launch() |