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| import streamlit as st | |
| from sentence_transformers import SentenceTransformer, util | |
| model = SentenceTransformer("all-MiniLM-L6-v2") | |
| st.title("Semantic Analyzer") | |
| sentence1 = st.text_input("Enter first sentence") | |
| sentence2 = st.text_input("Enter second sentence") | |
| if st.button("Analyze"): | |
| embedding1 = model.encode(sentence1, convert_to_tensor=True) | |
| embedding2 = model.encode(sentence2, convert_to_tensor=True) | |
| similarity = util.cos_sim(embedding1, embedding2) | |
| score = float(similarity[0][0]) | |
| st.write("Similarity Score:", round(score, 2)) |