Updated for index search

semanticsearch.py

@@ -33,31 +33,32 @@ sen_embeddings = model.encode(en_translations)
 def preprocess(input:str):
     if input.startswith('/'):
         #TODO
-        return True
+        return False
     values = [int(s) for s in re.findall(r'-?\d+\.?\d*', input)]
-
     if values:
         index=values[0]
         return kural_definition(index)
     else:
        return False
 def find_similarities(input:str):
+    response = preprocess(input)
+    if response:
+        return response
+    input_embeddings = model.encode([input.lower()])
+    from sklearn.metrics.pairwise import cosine_similarity
+    #let's calculate cosine similarity for sentence 0:
+    similarity_matrix=cosine_similarity(
+        [input_embeddings[0]],
+        sen_embeddings[1:]
+    )
 
-    if(not preprocess(input)):
-        input_embeddings = model.encode([input.lower()])
-        from sklearn.metrics.pairwise import cosine_similarity
-        #let's calculate cosine similarity for sentence 0:
-        similarity_matrix=cosine_similarity(
-            [input_embeddings[0]],
-            sen_embeddings[1:]
-        )
+    indices=[numpy.argpartition(similarity_matrix[0],-3)[-3:]]
+    indices.sort(reverse=True)
+    response=''
+    for index in indices[0]:
+       response+=kural_definition(index)
+    return response
 
-        indices=[numpy.argpartition(similarity_matrix[0],-3)[-3:]]
-        indices.sort(reverse=True)
-        response=''
-        for index in indices[0]:
-           response+=kural_definition(index)
-        return response
 
 def kural_definition(index:int):
     response=''
@@ -65,6 +66,7 @@ def kural_definition(index:int):
     response += "\n".join(kurals[index + 1]) + "\n"
     response += en_translations[index + 1] + "\n\n"
     print("\n".join(kurals[index + 1]))
+    return response
 
 # while True:
 #     text=input('Ask valluvar: ')