Update pages/type_text.py

pages/type_text.py

@@ -58,7 +58,7 @@ INTdesc_embedding = model.encode(INTdesc_input)
 
 # Semantic search, Compute cosine similarity between all pairs of SBS descriptions
 
-df_allchaps = pd.read_csv("SBS/SBS_V2_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']
 
@@ -70,15 +70,15 @@ 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_V2_0/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"]) 
+#df_SBS = pd.read_csv("SBS_V2_0/Code_Table.csv", index_col="SBS_Code", usecols=["Long_Description"]) # na_values=['NA']
+#df_SBS = pd.read_csv("SBS_V2_0/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
 #nrows = to_row_index - from_row_index + 1
 #skiprows = list(range(1,from_row_index - 1))
-#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_V2_0/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()