app.py

import gradio as gr
from transformers import BartForSequenceClassification, BartTokenizer


# model = pipeline("text-generation")

# following https://joeddav.github.io/blog/2020/05/29/ZSL.html
tokenizer_bart = BartTokenizer.from_pretrained('facebook/bart-large-mnli')
model_bart_sq = BartForSequenceClassification.from_pretrained('facebook/bart-large-mnli')


# def predict(prompt):
#     completion = model(prompt)[0]["generated_text"]
#     return completion

def zs(premise,hypothesis):
    input_ids = tokenizer_bart.encode(premise, hypothesis, return_tensors='pt')
    logits = model_bart_sq(input_ids)[0]
    entail_contradiction_logits = logits[:,[0,2]]
    probs = entail_contradiction_logits.softmax(dim=1)
    true_prob = probs[:,1].item() * 100
    return true_prob

gr.Interface(fn=zs, inputs=["text", "text"], outputs="text").launch()