Update pages/type_text_v10.py

pages/type_text_v10.py

@@ -59,8 +59,17 @@ if len(combined_chapters_rows_indexes_list) == 0:
     st.warning("Please select at least one chapter")
 st.write("COMBINED CHAPTERS ROWS INDEXES LIST: ", combined_chapters_rows_indexes_list)
 df_SBS = pd.read_csv("SBS_V2_0/Code_Table.csv", header=0, skip_blank_lines=False, skiprows = lambda x: x not in combined_chapters_rows_indexes_list)
-st.write(df_SBS.head(5))
 
+#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_line = 850 # Imaging services chapter start, adjust as needed
+#to_line = 1250 # Imaging services chapter end, adjust as needed
+#nrows = to_line - from_line + 1
+#skiprows = list(range(1,from_line - 1))
+#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()
 
 
 
@@ -109,16 +118,6 @@ INTdesc_embedding = model.encode(INTdesc_input)
 
 # Semantic search, Compute cosine similarity between all pairs of SBS descriptions
 
-#df_SBS = pd.read_csv("SBS_V2_Table.csv", index_col="SBS_Code", usecols=["Long_Description"]) # na_values=['NA']
-#df_SBS = pd.read_csv("SBS_V2_Table.csv", usecols=["SBS_Code_Hyphenated","Long_Description"]) 
-#from_line = 850 # Imaging services chapter start, adjust as needed
-#to_line = 1250 # Imaging services chapter end, adjust as needed
-#nrows = to_line - from_line + 1
-#skiprows = list(range(1,from_line - 1))
-#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()
 SBScorpus_embeddings = model.encode(SBScorpus)
 
 #my_model_results = pipeline("ner", model= "checkpoint-92")