import gradio as gr
from transformers import pipeline

pipe = pipeline("zero-shot-classification",model='MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7')

with gr.Blocks() as demo:
    txt = gr.Textbox('Введите текст', label='Текст для классификации', interactive=True)
    with gr.Row():
        labels = gr.DataFrame(
            headers=['Labels'], 
            row_count=(2, 'static'), 
            col_count=(1, 'fixed'),
            datatype='str', 
            interactive=True, 
            scale=4,
        )
        submit = gr.Button('Обработать', scale=1)
    with gr.Group():
        with gr.Row():
            checkbox = gr.Checkbox(
                label='Множественная положительная классификация', 
                interactive=True, 
                info='',
            )
            dropdown = gr.Dropdown(
                label='Number of Labels to predict', 
                multiselect=False, 
                value=1, 
                choices=list(range(1,6),),
                interactive=False,
            )
    result = gr.Label(
        label='Результат классификации', 
        visible=False,
    )

    def activate_dropdown(ob):
        if not ob:
            return gr.Dropdown(
                interactive=ob, 
                value=1,
            )
        return gr.Dropdown(interactive=ob)

    def submit_btn(text, df, label_no):
        output = pipe(
            text, 
            list(df['Labels']), 
            multi_label=True,
        )
        return gr.Label(
            visible=True, 
            num_top_classes=int(label_no),
            value={i: j for i, j in zip(output['labels'], output['scores'])}
        )

    checkbox.change(activate_dropdown, inputs=[checkbox], outputs=[dropdown])
    submit.click(submit_btn, inputs=[txt, labels, dropdown], outputs=[result])
demo.launch()