Update app.py

app.py

@@ -95,15 +95,12 @@ def on_results_count_change():
    st.session_state['top_k'] = int(st.session_state.my_slider)
    st.info("Results count changed " + str(st.session_state['top_k']))
 
-def on_model_change1():
-  st.session_state['model_name'] = st.session_state.my_model1
-  st.info("Pre-selected model chosen: " + st.session_state['model_name'])
-  st.session_state['bert_tokenizer'], st.session_state['bert_model']  = load_bert_model(st.session_state['model_name'])
-
-def on_model_change2(model_name): 
-  st.session_state['model_name'] = model_name
-  st.info("Custom model chosen: " + st.session_state['model_name'])
-  st.session_state['bert_tokenizer'], st.session_state['bert_model']  = load_bert_model(st.session_state['model_name'])
+
+def on_model_change(model_name): 
+  if (model_name != st.session_state['model_name']):
+    st.session_state['model_name'] = model_name
+    st.info("Custom model chosen: " + st.session_state['model_name'])
+    st.session_state['bert_tokenizer'], st.session_state['bert_model']  = load_bert_model(st.session_state['model_name'])
   
 def init_selectbox():
   return st.selectbox(
@@ -134,35 +131,32 @@ def main():
   st.write("   - To examine fill-mask predictions, enter the token [MASK] or <mask> in a sentence")
   st.write("   - To examine just the [CLS] vector, enter a word/phrase or sentence. Example: eGFR or EGFR or non small cell lung cancer")
   st.write("Pretrained BERT models from three domains (biomedical,PHI [person,location,org, etc.], and legal) are listed on the left. Their performance on domain specific sentences reveal both their strength and weakness.")
-  st.sidebar.slider("Select count of predictions to display", 1 , 50, 20,key='my_slider',on_change=on_results_count_change) #some times it is possible to have less words
+  
 
 
   try:
-      st.sidebar.selectbox(label='Select Model to Apply',  options=['ajitrajasekharan/biomedical', 'bert-base-cased','bert-large-cased','microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext','allenai/scibert_scivocab_cased','dmis-lab/biobert-v1.1','nlpaueb/legal-bert-base-uncased'], index=0,  key = "my_model1",on_change=on_model_change1)
-      
-      
-      
-
-      
       
-      #if (st.session_state['bert_tokenizer'] is None):
-      #  st.session_state['bert_tokenizer'], st.session_state['bert_model']  = load_bert_model(st.session_state['model_name'])
       
       with st.form('my_form'):
         selected_sentence = init_selectbox()
         text_input = st.text_input("Type any sentence below", "",key='my_text')
-        custom_model_selection = st.text_input("Model not listed on left? Type the model name (**fill-mask BERT models only**)", "",key="my_model2")
+        selected_model = st.selectbox(label='Select Model to Apply',  options=['ajitrajasekharan/biomedical', 'bert-base-cased','bert-large-cased','microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext','allenai/scibert_scivocab_cased','dmis-lab/biobert-v1.1','nlpaueb/legal-bert-base-uncased'], index=0,  key = "my_model1")
+        custom_model_selection = st.text_input("Model not listed on above? Type the model name (**fill-mask BERT models only**)", "",key="my_model2")
+        results_count = st.slider("Select count of predictions to display", 1 , 50, 20,key='my_slider') #some times it is possible to have less words
         submit_button = st.form_submit_button('Submit')
         
         input_status_area = st.empty()
         display_area = st.empty()
-        display_area.text("Current selections: results count = " + str(st.session_state['top_k']) + ";" + " Model name = " + st.session_state['model_name'])
         if 	submit_button:
             start = time.time()
             if (len(text_input) == 0):
               text_input = selected_sentence
+            st.session_state['top_k'] = results_count
             if (len(custom_model_selection) != 0):
-              on_model_change2(custom_model_selection)
+              on_model_change(custom_model_selection)
+            else:
+              on_model_change(selected_model)    
+                
             input_status_area.text("Input sentence:  " + text_input)
             results = on_text_change(text_input,display_area)
             display_area.empty()