Update app.py

app.py

@@ -35,9 +35,6 @@ def decode(tokenizer, pred_idx, top_clean):
 def encode(tokenizer, text_sentence, add_special_tokens=True):
   
   text_sentence = text_sentence.replace('<mask>', tokenizer.mask_token)
-    # if <mask> is the last token, append a "." so that models dont predict punctuation.
-  #if tokenizer.mask_token == text_sentence.split()[-1]:
-  #  text_sentence += ' .'
 
   tokenized_text = tokenizer.tokenize(text_sentence) 
   input_ids = torch.tensor([tokenizer.encode(text_sentence, add_special_tokens=add_special_tokens)])
@@ -142,32 +139,19 @@ def main():
   st.markdown("""
         <small style="font-size:18px; color: #7f7f7f">Pretrained BERT models can be used as is, <a href="https://ajitrajasekharan.github.io/2021/01/02/my-first-post.html"><b>with no fine tuning to perform tasks like NER</b></a> <i>ideally if both fill-mask and CLS predictions are good, or minimally if fill-mask predictions are adequate</i></small>
         """, unsafe_allow_html=True)
-  #st.write("https://ajitrajasekharan.github.io/2021/01/02/my-first-post.html")
+
   st.write("This app can be used to examine both model prediction for a masked  position as well as the neighborhood of CLS vector")
   st.write("   - To examine model prediction for a position, enter the token [MASK] or <mask>")
   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.sidebar.slider("Select how many predictions do you need", 1 , 50, 20,key='my_slider',on_change=on_results_count_change) #some times it is possible to have less words
 
 
-
-
-  #if st.button("Submit"):
-    
-  #  with st.spinner("Computing"):
   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'], index=0,  key = "my_model1",on_change=on_model_change1)
       init_selectbox()
       st.text_input("Enter text below", "",on_change=on_text_change,key='my_text')
       st.text_input("Model not listed on left? Type the model name (fill-mask BERT models only)", "",key="my_model2",on_change=on_model_change2)
-      #if (len(custom_model_name) > 0):
-      #  model_name = custom_model_name
-      # st.info("Custom model selected: " + model_name)
-      #  bert_tokenizer, bert_model  = load_bert_model(model_name)
-      #if len(input_text) > 0:
-      #  run_test(input_text,top_k,model_name)
-      #else:
-       # if len(option) > 0:
-       #   run_test(option,top_k,model_name)
+
 
       st.info("Currently selected results count = " + str(st.session_state['top_k']))
       st.info("Currently selected Model name = " + st.session_state['model_name'])