Update pages/type_text.py

pages/type_text.py

@@ -15,10 +15,7 @@ for k, v in st.session_state.items():
 #selected_chapters_floatlist = list(st.session_state.items())[0][1]
 #selected_chapters_intlist = [int(i) for i in selected_chapters_floatlist]
 #st.write("SELECTED CHAPTERS: ", selected_chapters_intlist)
-#selected_chapters_floatlist = list(st.session_state.items())[0][1]
-#selected_chapters_intlist = [int(i) for i in selected_chapters_floatlist]
 selected_rows_list = list(st.session_state.items())[1][1]
-
 st.write("SELECTED ROWS: ", selected_rows_list)
 
 def get_device_map() -> str:
@@ -64,22 +61,15 @@ INTdesc_embedding = model.encode(INTdesc_input)
 
 # Semantic search, Compute cosine similarity between all pairs of SBS descriptions
 
-df_allchaps = pd.read_csv("SBS_V2_0/Chapter_Index_Rows.csv", usecols=["Chapter", "from_row_index", "to_row_index"])
+#df_allchaps = pd.read_csv("SBS_V2_0/Chapter_Index_Rows.csv", usecols=["Chapter", "from_row_index", "to_row_index"])
 #st.dataframe(df_allchaps)
 #df_selectedchaps = df.loc[df['City'] == 'Chicago']
 
-dict_allchaps = df_allchaps.to_dict(orient='index')
-st.write("VVVVVVVVVVV: ", dict_allchaps)
-for chapter in dict_allchaps.get("Chapter"): 
-    st.write(chapter)
-
-
-
-
-
-
+#dict_allchaps = df_allchaps.to_dict(orient='index')
+#st.write("ALL CHAPTERS: ", dict_allchaps)
+#for chapter in dict_allchaps.get("Chapter"): 
+#    st.write(chapter)
     
-selected_rows_list = [] #dict_allchaps
 if len(selected_rows_list) == 0: 
     st.warning("Please select at least one chapter") 
     selected_rows_list = [0, 10080]