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
       
       # Append input sentences to 'sentences' list
       sentences.append(sentence_1)
       sentences.append(sentence_2)
       
       # Create embeddings for both sentences
       sentence_embeddings = model.encode(sentences)
      
              
       st.write('Similarity between {} and {} is {}'.format(sentence_1,
              sentence_2,
              cosine_similarity(sentence_embeddings[0].reshape(1, -1),
              sentence_embeddings[1].reshape(1, -1))[0][0]))