app.py

import streamlit as st
from transformers import pipeline
from textblob import TextBlob
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

model = SentenceTransformer('paraphrase-xlm-r-multilingual-v1')

sentences = []
     
# Streamlit interface
      
st.title("Sentence Similarity")

# Streamlit webpage elements

sentence_1 = st.text_input("Sentence 1 input")

sentence_2 = st.text_input("Sentence 2 input")

submit_button = st.button("submit")

if submit_button:

       # Perform calculations
       
       sentence_embeddings = model.encode(sentences)

       sentences.append(sentence_1)
       sentences.append(sentence_2)
       
       for sentence, embedding in zip(sentences, sentence_embeddings):
           st.write("Sentence:", sentence)
           st.write("Embedding:", embedding)
           st.write("")