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| import gradio as gr | |
| title = "DPR" | |
| description = "Gradio Demo for DPR. 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/2004.04906' target='_blank'>Dense Passage Retrieval for Open-Domain Question Answering</a></p>" | |
| examples = [ | |
| ["Hello, is my dog cute ?","dpr-question_encoder-bert-base-multilingual"] | |
| ] | |
| io1 = gr.Interface.load("huggingface/voidful/dpr-question_encoder-bert-base-multilingual") | |
| io2 = gr.Interface.load("huggingface/sivasankalpp/dpr-multidoc2dial-structure-question-encoder") | |
| def inference(inputtext, model): | |
| if model == "dpr-question_encoder-bert-base-multilingual": | |
| outlabel = io1(inputtext) | |
| else: | |
| outlabel = io2(inputtext) | |
| return outlabel | |
| gr.Interface( | |
| inference, | |
| [gr.inputs.Textbox(label="Context",lines=10),gr.inputs.Dropdown(choices=["dpr-question_encoder-bert-base-multilingual","dpr-multidoc2dial-structure-question-encoder"], type="value", default="dpr-question_encoder-bert-base-multilingual", label="model")], | |
| [gr.outputs.Dataframe(type="pandas",label="Output")], | |
| examples=examples, | |
| article=article, | |
| title=title, | |
| description=description).launch(enable_queue=True) |