Create app.py

app.py

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+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}')