Update app.py

app.py

@@ -6,9 +6,6 @@ from sklearn.feature_extraction.text import CountVectorizer
 import numpy as np
 from sentence_transformers import SentenceTransformer
 
-
-
-
 # Retrieve the token from environment variables
 huggingface_token = os.getenv('LLAMA_ACCES_TOKEN')
 
@@ -16,8 +13,6 @@ huggingface_token = os.getenv('LLAMA_ACCES_TOKEN')
 tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf", token=huggingface_token)
 model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", token=huggingface_token)
 
-
-
 # Function to load bad words from a file
 def load_bad_words(filepath):
     with open(filepath, 'r', encoding='utf-8') as file:
@@ -30,8 +25,7 @@ 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 
-embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
-topic_model = BERTopic.load("MaartenGr/BERTopic_Wikipedia", embedding_model=embedding_model)
+topic_model = BERTopic.load("MaartenGr/BERTopic_Wikipedia")
 
 def is_inappropriate_or_offtopic(message, selected_topics):
     # Assume bad_words loading and check_content function are defined here