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)