def get_predictions(input_text: str) -> dict:
    label2id = model.config.label2id
    inputs = tokenizer(input_text, return_tensors='pt', truncation=True)
    inputs = inputs.to(device)
    outputs = model(**inputs)
    logits = outputs.logits
    sigmoid = torch.nn.Sigmoid()
    probs = sigmoid(logits.squeeze().cpu())
    probs = probs.detach().numpy()
    for i, k in enumerate(label2id.keys()):
        label2id[k] = probs[i]
    label2id = {k: float(v) for k, v in sorted(label2id.items(), key=lambda item: item[1].item(), reverse=True)}
    print(label2id)
    return label2id

import gradio as gr
gr.Interface(
    fn=get_predictions,
    inputs=gr.components.Textbox(label='Input'),
    theme = "darkdefault",
    outputs=gr.components.Label(label='Predictions', num_top_classes=3),
    allow_flagging='never'
).launch(debug='True')