Update app.py

app.py

@@ -9,8 +9,16 @@ dictionary = joblib.load('doc2bow.sav')  # Load the dictionary
 lda_model = joblib.load('ldamodel.sav')  # Load the LDA model
 
 # Function to preprocess input text and get topic distribution
+def preprocess(text):
+    # Define your preprocessing logic here, as it was in your original code
+    result = []
+    for token in gensim.utils.simple_preprocess(text):
+        if token not in gensim.parsing.preprocessing.STOPWORDS and token not in newStopWords and len(token) > 3:
+            result.append(lemmatize_stemming(token))
+    return result
+
 def get_topics(text):
-    bow_vector = dictionary.doc2bow(text.split())
+    bow_vector = dictionary.doc2bow(preprocess(text))
     topics = lda_model[bow_vector]
     return topics