Update app.py

app.py

@@ -13,13 +13,13 @@ st.set_page_config(page_title='eRupt Topic Trendy (e-Commerce x Social Media)',
 st.markdown("<h1 style='text-align: center;'>Topic Trendy</h1>", unsafe_allow_html=True)
 #BerTopic_model = BERTopic.load("my_topics_model")
 
-sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
-umap_model = UMAP(n_neighbors=15, n_components=2, min_dist=0.1, metric="cosine")
-hdbscan_model = HDBSCAN(min_cluster_size=5, min_samples = 3, metric="euclidean", prediction_data=True)
-vectorizer_model = CountVectorizer(lowercase = True, ngram_range=(1, 3), analyzer="word", max_df=1.0, min_df=0.5, stop_words="english")
+#sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
+#umap_model = UMAP(n_neighbors=15, n_components=2, min_dist=0.1, metric="cosine")
+#hdbscan_model = HDBSCAN(min_cluster_size=5, min_samples = 3, metric="euclidean", prediction_data=True)
+#vectorizer_model = CountVectorizer(lowercase = True, ngram_range=(1, 3), analyzer="word", max_df=1.0, min_df=0.5, stop_words="english")
 
-kw_model = BERTopic(embedding_model=sentence_model, umap_model = umap_model, hdbscan_model = hdbscan_model, vectorizer_model = vectorizer_model, nr_topics = "auto", calculate_probabilities = True)
-BerTopic_model = kw_model
+#kw_model = BERTopic(embedding_model=sentence_model, umap_model = umap_model, hdbscan_model = hdbscan_model, vectorizer_model = vectorizer_model, nr_topics = "auto", calculate_probabilities = True)
+#BerTopic_model = kw_model
 input_text = st.text_area("Enter product topic here")
 
 topic = pd.read_csv('./Data/tiktok_utf8.csv')
@@ -29,7 +29,7 @@ tiktok = topic.text.to_list()
 
 vectorizer_model = CountVectorizer(stop_words="english")
 topic_model = BERTopic(verbose=True,vectorizer_model=vectorizer_model)
-topics, probs = topic_model.fit_transform(tiktok)
+#topics, probs = topic_model.fit_transform(tiktok)
 
 similar_topics, similarity = topic_model.find_topics(input_text, top_n=20)