Optimize the code for converting text into tokens

app.py

@@ -14,7 +14,7 @@ with open("tokenizer.pckl", "rb") as file:
 def classify(text: str, response: str):
     question = list(tokenizer.texts_to_sequences([text.lower(),])[0])
     answer = list(tokenizer.texts_to_sequences([response.lower(),])[0])
-    arr = np.array([(list(question)+[0,]*l1)[:l1]+(list(answer)+[0,]*l2)[:l2],])
+    arr = np.array([question+[0,]*l1)[:l1]+(answer+[0,]*l2)[:l2],])
     prediction = model.predict(arr)[0][0]
     if prediction > 0.9:
         return "Surely relevant "+str(prediction)