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| import gradio as gr | |
| title = "MBart" | |
| description = "Gradio Demo for MBart. To use it, simply add your text, or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2001.08210' target='_blank'>Multilingual Denoising Pre-training for Neural Machine Translation</a></p>" | |
| examples = [ | |
| ["Paris is the capital of France","mbart-large-50-one-to-many-mmt"] | |
| ] | |
| io1 = gr.Interface.load("huggingface/facebook/mbart-large-50-one-to-many-mmt") | |
| io2 = gr.Interface.load("huggingface/facebook/mbart-large-50") | |
| def inference(text, model): | |
| if model == "mbart-large-50-one-to-many-mmt": | |
| outtext = io1(text) | |
| else: | |
| outtext = io2(text) | |
| return outtext | |
| gr.Interface( | |
| inference, | |
| [gr.inputs.Textbox(label="Input"),gr.inputs.Dropdown(choices=["mbart-large-50-one-to-many-mmt","mbart-large-50"], type="value", default="mbart-large-50-one-to-many-mmt", label="model") | |
| ], | |
| gr.outputs.Textbox(label="Output"), | |
| examples=examples, | |
| article=article, | |
| title=title, | |
| description=description).launch(enable_queue=True) |