app.py

import streamlit as st
from sentence_transformers import SentenceTransformer, util

@st.cache_resource  # Cache the model for faster loading
def load_model():
    return SentenceTransformer('sentence-transformers/all-mpnet-base-v2')

model = load_model()

st.title("Text Similarity Checker")

text1 = st.text_input("Enter the first text:")
text2 = st.text_input("Enter the second text:")

if text1 and text2:
    # Calculate embeddings and similarity
    embedding1 = model.encode(text1)
    embedding2 = model.encode(text2)
    similarity = util.cos_sim(embedding1, embedding2).item() * 100
    st.write(f"**Similarity Score:** {similarity:.2f}%")