Update app.py

app.py

@@ -4,6 +4,9 @@ import os
 from bertopic import BERTopic
 from sklearn.feature_extraction.text import CountVectorizer
 import numpy as np
+from sentence_transformers import SentenceTransformer
+
+
 
 
 # Retrieve the token from environment variables
@@ -27,7 +30,8 @@ bad_words = load_bad_words('badwords.txt')  # Adjust the path to your bad words
 topics_list = ['Aviation', 'Science', 'Education', 'Air Force Pilot', 'Space Exploration', 'Technology']
 
 #Load BerTopic model 
-topic_model = BERTopic.load("MaartenGr/BERTopic_Wikipedia")
+embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
+topic_model = BERTopic.load("MaartenGr/BERTopic_Wikipedia", embedding_model=embedding_model)
 
 def is_inappropriate_or_offtopic(message, selected_topics):
     # Assume bad_words loading and check_content function are defined here