tabs/sentence_similarity_tab.py

import streamlit as st # type: ignore
from PIL import Image
import os
import ast
import contextlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
from translate_app import tr

title = "Sentence Similarity"
sidebar_name = "Sentence Similarity"
dataPath = st.session_state.DataPath


def run():
    st.write("")
    st.write("")
    st.title(tr(title))
    sentences = ["This is an example sentence", "Each sentence is converted"]
    sentences[0] = st.text_area(label=tr("Saisir un élément issu de la proposition de valeur (quelque soit la langue):"), value="This is an example sentence")
    sentences[1] = st.text_area(label=tr("Saisir une phrase issue de l'acte de vente (quelque soit la langue):"), value="Each sentence is converted", height=200)
    st.button(label=tr("Validez"), type="primary")

    model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
    embeddings = model.encode(sentences)
    st.write(tr("Transformation de chaque phrase en vecteur (dimension = 384 ):"))
    st.write(embeddings)
    st.write("")
    # Calculate cosine similarity between the two sentences
    similarity = cosine_similarity([embeddings[0]], [embeddings[1]])
    st.write(tr("**Cosine similarity** comprise entre 0 et 1 :"), similarity[0][0])
    st.write("")
    st.write("")
    st.write("")