Update pages/type_text.py

pages/type_text.py

@@ -2,7 +2,7 @@ import streamlit as st
 import pandas as pd
 from io import StringIO
 import json
-import torch
+#import torch
 from transformers import pipeline, # AutoTokenizer, AutoModelForCausalLM, AutoModelForTokenClassification
 from sentence_transformers import SentenceTransformer, util 
 
@@ -13,8 +13,8 @@ def get_device_map() -> str:
     return 'cuda' if torch.cuda.is_available() else 'cpu'
 device = get_device_map()  # 'cpu'
 
-def on_click():
-    st.session_state.user_input = ""
+#def on_click():
+#    st.session_state.user_input = ""
     
 #@st.cache
 def convert_df(df:pd.DataFrame):
@@ -33,7 +33,7 @@ INTdesc_input = st.text_input("Type internal description and hit Enter", key="us
 createSBScodes, right_column = st.columns(2)
 createSBScodes_clicked = createSBScodes.button("Map to SBS codes", key="user_createSBScodes")
 right_column.button("Reset", on_click=on_click)
-"""
+
 #numMAPPINGS_input = 5
 #numMAPPINGS_input = st.text_input("Type number of mappings and hit Enter", key="user_input_numMAPPINGS")
 #st.button("Clear text", on_click=on_click)
@@ -125,4 +125,3 @@ if INTdesc_input is not None and createSBScodes_clicked == True:
     with b3:
         #jsonbutton = download_button(results, "results.json", "📥 Download .json")
         jsonbutton = st.download_button(label="📥 Download .json", data=convert_json(dfALL), file_name= "results.json", mime='application/json',  key='json_b')
-"""