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 plus Reasoning Models")
+st.header("Map internal descriptions to SBS codes using 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 +159,7 @@ 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") 
+#mapSBS_button = st.button("Map to SBS codes") #, key="user_clickedSBS") 
 
 INTdesc_embedding = model.encode(INTdesc_input)
 
@@ -171,7 +171,7 @@ dictA = {"Score": [], "SBS Code": [], "SBS Description V2.0": []}
 dfALL = pd.DataFrame.from_dict(dictA)
 
 
-if INTdesc_input is not None and st.button("user_clickedSBS"): 
+if INTdesc_input is not None and st.button("Map to SBS codes"): 
     #my_model_results = pipeline("ner", model= "checkpoint-92")
     HF_model_results = util.semantic_search(INTdesc_embedding, SBScorpus_embeddings)
     HF_model_results_sorted = sorted(HF_model_results, key=lambda x: x[1], reverse=True)