import streamlit as st
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import spacy

nlp = spacy.load("en_core_web_sm")
model = SentenceTransformer("rufimelo/Legal-BERTimbau-sts-base")

def compute_similarity(left_text: str, right_text: str) -> np.ndarray:
    embeddings = model.encode([left_text, right_text])
    similarity = cosine_similarity(embeddings[0], embeddings[1])

    return similarity

first = st.text_input('First', 'This is a test')
second = st.text_input('Second', 'This is another test')

s = compute_similarity(left_text=first, right_texts=second)
st.datafeame(s)