import gradio as gr

title = "BART"

description = "Gradio Demo for BART, 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/1910.13461' target='_blank'>BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension</a></p>"

examples = [
    ["I have a problem with my iphone that needs to be resolved asap!!","bart-large-mnli","urgent, not urgent, phone, tablet, computer",False]
]

io1 = gr.Interface.load("huggingface/facebook/bart-large-mnli")

io2 = gr.Interface.load("huggingface/facebook/bart-large-cnn")

def inference(text, model,class_names,allow_multiple):
    if model == "bart-large-mnli":
        outlabel = io1(text,class_names,allow_multiple)
        outtext = ""
    else:
        outtext = io2(text)
        outlabel = {}
    return outlabel, outtext   
     

gr.Interface(
    inference, 
    [gr.inputs.Textbox(label="Input",lines=10),gr.inputs.Dropdown(choices=["bart-large-mnli","bart-large-cnn"], type="value", default="bart-large-mnli", label="model"),gr.inputs.Textbox(label="Possible class names (comma-separated)"),gr.inputs.Checkbox(default=False, label="Allow multiple true classes")], 
    ["label","textbox"],
    examples=examples,
    article=article,
    title=title,
    description=description).launch(enable_queue=True, cache_examples=True)