rechanged to previous version

app.py

@@ -1090,11 +1090,11 @@ else:
             )
             
             cA, cB, cC = st.columns([1, 1, 2])
-            # max_topics = cA.slider("Max topics", 5, 120, 40, 5)
-            topic_info = tm.get_topic_info()
-            n_topics_no_outliers = int((topic_info.Topic != -1).sum())
-            max_topics = n_topics_no_outliers
-            st.caption(f"Will label all topics (excluding outliers): {max_topics}")
+            max_topics = cA.slider("Max topics", 5, 120, 40, 5)
+            # topic_info = tm.get_topic_info()
+            # n_topics_no_outliers = int((topic_info.Topic != -1).sum())
+            # max_topics = n_topics_no_outliers
+            # st.caption(f"Will label all topics (excluding outliers): {max_topics}")
 
             
             force = cB.checkbox("Force regenerate", value=False)
@@ -1121,22 +1121,22 @@ else:
             api_map = st.session_state.get("llm_names", {}) or {}
             final_name_map = {**default_map, **api_map}
 
-            #option to choose to include outliers or not
-            include_outliers_plot = st.checkbox("Include outliers in plot (-1)", value=False)
+            # #option to choose to include outliers or not
+            # include_outliers_plot = st.checkbox("Include outliers in plot (-1)", value=False)
 
-            topics_arr = np.asarray(tm.topics_)
-            labs_all = [final_name_map.get(int(t), "Unlabelled") for t in topics_arr]
-            if include_outliers_plot:
-                final_name_map[-1] = "Outliers"
-                reduced_plot = reduced
-                labs = labs_all
-            else:
-                mask = topics_arr != -1
-                reduced_plot = reduced[mask]
-                labs = list(np.asarray(labs_all)[mask])
+            # topics_arr = np.asarray(tm.topics_)
+            # labs_all = [final_name_map.get(int(t), "Unlabelled") for t in topics_arr]
+            # if include_outliers_plot:
+            #     final_name_map[-1] = "Outliers"
+            #     reduced_plot = reduced
+            #     labs = labs_all
+            # else:
+            #     mask = topics_arr != -1
+            #     reduced_plot = reduced[mask]
+            #     labs = list(np.asarray(labs_all)[mask])
 
             
-            # labs = [final_name_map.get(t, "Unlabelled") for t in tm.topics_]
+            labs = [final_name_map.get(t, "Unlabelled") for t in tm.topics_]
             ##### ADDED FOR LLM (END)