Update app.py

app.py

@@ -9,10 +9,9 @@ st.set_page_config(page_title='Sentence Similarity Demo')
 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')
+sentence1 = st.text_input('Enter the first sentence:', 'Convert this into a sea shanty')
+sentence2 = st.text_input('Enter the second sentence:', 'Improve this text by making it a shanty.')
 
-# Load the Sentence Transformer model
 @st.cache_resource
 def load_model():
     return SentenceTransformer('sentence-transformers/sentence-t5-base')
@@ -22,8 +21,12 @@ model = load_model()
 # Calculate embeddings
 embeddings = model.encode([sentence1, sentence2])
 
-# Calculate cosine similarity
-similarity = cosine_similarity([embeddings[0]], [embeddings[1]])[0][0]
+# Calculate Sharpened Cosine Similarity (SCS)
+def sharpened_cosine_similarity(s, k, q=1e-6, p=2):
+    cosine_sim = np.dot(s, k) / (np.linalg.norm(s) * np.linalg.norm(k))
+    return np.sign(cosine_sim) * np.power(np.abs(cosine_sim) / (np.linalg.norm(s) + q), p)
+
+similarity = sharpened_cosine_similarity(embeddings[0], embeddings[1])
 
 # Display the result
-st.write(f'Cosine Similarity: {similarity:.4f}')
+st.write(f'Sharpened Cosine Similarity: {similarity:.4f}')