Update app.py

app.py

@@ -13,13 +13,18 @@ sentence_embed = pd.read_csv('Reference_file_2 (1).csv')
 
 #st.write(sentence_embed.head(5))
 
-# Define the backend function
+# Function to compute cosine similarity
+def cosine_similarity(v1, v2):
+    """Compute cosine similarity between two vectors."""
+    return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))
+
+# Backend function for mapping
 def mapping_code(user_input):
     emb1 = model.encode(user_input, convert_to_tensor=True)
     similarities = []
-    for sentence in model.encode(sentence_embed['embeds'], convert_to_tensor=True):
-        #util.cos_sim(sentence, emb1) 
-        similarity = util.paraphrase_mining(sentence, emb1,top_k=10)
+    for sentence_emb in sentence_embed['embeds']:
+        sentence_emb = np.array(sentence_emb)
+        similarity = cosine_similarity(sentence_emb, emb1)
         similarities.append(similarity)
 
     # Combine similarity scores with 'code' and 'description'
@@ -55,6 +60,5 @@ def main():
             for i, result in enumerate(mapping_results, 1):
                 st.write(f"{i}. Code: {result['Code']}, Description: {result['Description']}, Similarity Score: {result['Similarity Score']:.4f}")
 
-# Run the app
 if __name__ == "__main__":
     main()