Update pages/type_text.py

pages/type_text.py

@@ -9,7 +9,7 @@ from sentence_transformers import SentenceTransformer, util
 import os
 os.getenv("HF_TOKEN") 
 
-st.header("Map internal descriptions to SBS codes using Sentence Transformer + Reasoning Models")
+st.header("Map internal descriptions to SBS codes with Sentence Transformer + Reasoning Models")
 st.subheader("Select specific Chapter for quicker results")
 #df_chapters = pd.read_csv("SBS_V2_0/Chapter_Index_Rows.csv") 
 df_chapters = pd.read_csv("SBS_V2_0/Chapter_Index_Rows_with_total.csv") 
@@ -159,7 +159,6 @@ pipe = load_pipe()
 
 
 #mapSBS_button = st.button("Map to SBS codes", on_click=on_click, key="user_clickedSBS")
-#mapSBS_button = st.button("Map to SBS codes") #, key="user_clickedSBS") 
 
 INTdesc_embedding = model.encode(INTdesc_input)
 
@@ -178,7 +177,8 @@ if INTdesc_input is not None and st.button("Map to SBS codes"):
     HF_model_results_displayed = HF_model_results_sorted[0:numMAPPINGS_input]
 
 
-if INTdesc_input is not None and mapSBS_button == True: 
+#if INTdesc_input is not None and mapSBS_button == True: 
+if INTdesc_input is not None and st.button("Map to SBS codes"):     
     for i, result in enumerate(HF_model_results_displayed):
         dictA.update({"Score": "%.4f" % result[0]["score"], "SBS Code": df_SBS.loc[df_SBS["Long_Description"] == SBScorpus[result[0]["corpus_id"]],"SBS_Code_Hyphenated"].values[0], "SBS Description V2.0": SBScorpus[result[0]["corpus_id"]]})
         dfALL = pd.concat([dfALL, pd.DataFrame([dictA])], ignore_index=True)