Update app.py

app.py

@@ -60,24 +60,28 @@ embeds = co.embed(texts=list_of_titles,
   					model="small").embeddings
 
 embeds_npy = np.array(embeds)
-# Load and initialize BERTopic to use KMeans clustering with 8 clusters only.
-cluster_model = KMeans(n_clusters=8)
-topic_model = BERTopic(hdbscan_model=cluster_model)
 
-# df is a dataframe. df['title'] is the column of text we're modeling
-df['topic'], probabilities = topic_model.fit_transform(df['content.title'], embeds_npy)
+def get_visualizations():
+    # Load and initialize BERTopic to use KMeans clustering with 8 clusters only.
+    cluster_model = KMeans(n_clusters=8)
+    topic_model = BERTopic(hdbscan_model=cluster_model)
+    
+    # df is a dataframe. df['title'] is the column of text we're modeling
+    df['topic'], probabilities = topic_model.fit_transform(df['content.title'], embeds_npy)
+    
+    app = Topically(os.environ["CO_API_KEY"])
+    
+    df['topic_name'], topic_names = app.name_topics((df['content.title'], df['topic']), num_generations=5)
+    
+    #st.write("Topics extracted are:", topic_names)
+    
+    topic_model.set_topic_labels(topic_names)
+    fig1 = topic_model.visualize_documents(df['content.title'].values, 
+                                    embeddings=embeds_npy,
+                                    topics = list(range(8)),
+                                    custom_labels=True)
+    fig2 = topic_model.visualize_barchart()
+    st.plotly_chart(fig1)
+    st.plotly_chart(fig2)
 
-app = Topically(os.environ["CO_API_KEY"])
-
-df['topic_name'], topic_names = app.name_topics((df['content.title'], df['topic']), num_generations=5)
-
-#st.write("Topics extracted are:", topic_names)
-
-topic_model.set_topic_labels(topic_names)
-fig = topic_model.visualize_documents(df['content.title'].values, 
-                                embeddings=embeds_npy,
-                                topics = list(range(8)),
-                                custom_labels=True)
-
-st.plotly_chart(fig)