app.py

import streamlit as st
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

# Set page title
st.set_page_config(page_title='Sentence Similarity Demo')

# Create a title for the app
st.title('Sentence Similarity Demo')

# Input sentences
sentence1 = st.text_input('Enter the first sentence:', 'This is an example sentence')
sentence2 = st.text_input('Enter the second sentence:', 'Each sentence is converted')

# Load the Sentence Transformer model
@st.cache_resource
def load_model():
    return SentenceTransformer('sentence-transformers/sentence-t5-base')

model = load_model()

# Calculate embeddings
embeddings = model.encode([sentence1, sentence2])

# Calculate cosine similarity
similarity = cosine_similarity([embeddings[0]], [embeddings[1]])[0][0]

# Display the result
st.write(f'Cosine Similarity: {similarity:.4f}')