app.py

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:
    print("computing embeddings...")
    embeddings = model.encode([left_text, right_text])
    print("computing similarity...")
    similarity = cosine_similarity(embeddings[: 1], 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_text=second)
st.dataframe(s)