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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 form elements
with st.form("submission_form", clear_on_submit=False):
sentence_1 = st.text_input("Sentence 1 input")
sentence_2 = st.text_input("Sentence 2 input")
submit_button = st.form_submit_button("Compare Sentences")
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]) * 100)
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