Update pages/type_text.py

pages/type_text.py

@@ -54,14 +54,16 @@ def load_model():
     return model
 model = load_model() 
 
-#selected_chapters_dict = {0: [], 1:[], 2:[] }
-
 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", index_col=0, usecols=["from_row_index", "to_row_index"])
+dfallchaps = pd.read_csv("SBS/SBS_V2_Chapter_Index_Rows.csv", index_col="Chapter", usecols=["from_row_index", "to_row_index"])
 st.write("UUUUUUUUUUU: ", dfallchaps)
+selected_chapters_dict = dfallchaps.to_dict(orient='records')
+st.write("VVVVVVVVVVV: ", selected_chapters_dict)
+
+
 #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