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')

title="Stance Detection using Zero Shot"

description="Welcome to the side where the grass is greener. This is a simple tool which was created with an aim to stance towards a given entity in a sentence. However, this is not the only use case of it!"


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[:,[0,1,2]]
    entail_contradiction_logits = logits[:,[0,2]]
    probs = entail_contradiction_logits.softmax(dim=1)
    contra_prob = round(probs[:,0].item(),4)
    # neut_prob = round(probs[:,1].item(),4)
    entail_prob = round(probs[:,1].item(),4)
    # return contra_prob, neut_prob, entail_prob
    return contra_prob, entail_prob


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


with gr.Blocks() as demo:
    with gr.Row():
        premise = gr.Textbox(label="Premise")
        hypothesis = gr.Textbox(label="Hypothesis")
    with gr.Row():
        greet_btn = gr.Button("Compute")
    with gr.Row():
        entailment = gr.Textbox(label="Entailment Probability")
        contradiction = gr.Textbox(label="Contradiction Probability")
        # neutral = gr.Textbox(label="Neutral Probability")
        # greet_btn.click(fn=zs, inputs=[premise,hypothesis], outputs=[contradiction,neutral,entailment])
        greet_btn.click(fn=zs, inputs=[premise,hypothesis], outputs=[contradiction,entailment])


demo.launch()