Update pages/type_text.py

pages/type_text.py

@@ -58,23 +58,23 @@ INTdesc_embedding = model.encode(INTdesc_input)
 
 # Semantic search, Compute cosine similarity between all pairs of SBS descriptions
 
-dfallchaps = pd.read_csv("SBS/SBS_V2_Chapter_Index_Rows.csv", usecols=["Chapter", "from_row_index", "to_row_index"])
-st.dataframe(dfallchaps)
-selected_chapters_dict = dfallchaps.to_dict(orient='index')
-st.write("VVVVVVVVVVV: ", selected_chapters_dict)
-
+df_allchapsallchaps = pd.read_csv("SBS/SBS_V2_Chapter_Index_Rows.csv", usecols=["Chapter", "from_row_index", "to_row_index"])
+st.dataframe(df_allchaps)
+dict_allchaps = df_allchaps.to_dict(orient='index')
+st.write("VVVVVVVVVVV: ", dict_allchaps)
+selected_rows_list = [] #dict_allchaps
+if len(selected_rows_list) == 0: 
+    st.warning("Please select at least one chapter") 
+    selected_rows_list = [0, 10080]
+#df_SBS = pd.read_csv("SBS/SBS_V2_Code_Table.csv", header=0, skip_blank_lines=False, skiprows = lambda x: x not in selected_rows_list)
 
 #df_SBS = pd.read_csv("SBS/SBS_V2_Code_Table.csv", index_col="SBS_Code", usecols=["Long_Description"]) # na_values=['NA']
 #df_SBS = pd.read_csv("SBS/SBS_V2_Code_Table.csv", usecols=["SBS_Code_Hyphenated","Long_Description"]) 
-from_row_index = 7725 # Imaging services chapter start, adjust as needed
-to_row_index = 8239 # Imaging services chapter end, adjust as needed
+#from_row_index = 7725 # Imaging services chapter start, adjust as needed
+#to_row_index = 8239 # Imaging services chapter end, adjust as needed
 #nrows = to_row_index - from_row_index + 1
 #skiprows = list(range(1,from_row_index - 1))
-
-
-#selected_rows_to_read = 
-#df_SBS = pd.read_csv("SBS/SBS_V2_Code_Table.csv", skiprows = lambda x: x not in specific_rows)
-df_SBS = pd.read_csv("SBS/SBS_V2_Code_Table.csv", header=0, skip_blank_lines=False, skiprows=skiprows, nrows=nrows)
+#df_SBS = pd.read_csv("SBS/SBS_V2_Code_Table.csv", header=0, skip_blank_lines=False, skiprows=skiprows, nrows=nrows)
 #st.write(df_SBS.head(5))
 
 SBScorpus = df_SBS['Long_Description'].values.tolist()