import gradio as gr

title = "XLM-RoBERTa"

description = "Gradio Demo for XLM-RoBERTa. 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/1911.02116' target='_blank'>Unsupervised Cross-lingual Representation Learning at Scale</a></p>"

examples = [
    ["Hello I'm a <mask> model.","xlm-roberta-base"]
]


io1 = gr.Interface.load("huggingface/xlm-roberta-base")

io2 = gr.Interface.load("huggingface/xlm-roberta-large")

def inference(inputtext, model):
    if model == "xlm-roberta-base":
        outlabel = io1(inputtext)
    else:
        outlabel = io2(inputtext)
    return outlabel
     

gr.Interface(
    inference, 
    [gr.inputs.Textbox(label="Context",lines=10),gr.inputs.Dropdown(choices=["xlm-roberta-base","xlm-roberta-large"], type="value", default="xlm-roberta-base", label="model")], 
    [gr.outputs.Textbox(label="Output")],
    examples=examples,
    article=article,
    title=title,
    description=description).launch(enable_queue=True, cache_examples=True)