Update app.py

app.py

@@ -5,30 +5,31 @@ from transformers import pipeline
 
 @st.cache(allow_output_mutation=True)
 def get_model(model):
-	return pipeline("fill-mask", model=model, top_k=100)
+	return pipeline("fill-mask", model=model, top_k=100)#seto maximum of tokens to be retrieved after each inference to model
 
 HISTORY_WEIGHT = 100 # set history weight (if found any keyword from history, it will priorities based on its weight)
 
-
+st.caption("This is a simple auto-completion where the next token is predicted per probability and a weigh if appears in user's history")
 
 history_keyword_text = st.text_input("Enter users's history keywords (optional, i.e., 'Gates')", value="Gates")
 
 text = st.text_input("Enter a text for auto completion...", value='Where is Bill')
 
-model = st.selectbox("choose a model", ["roberta-base", "bert-base-uncased", "gpt2", "t5"])
+model = st.selectbox("choose a model", ["roberta-base", "bert-base-uncased"])
 
 data_load_state = st.text('Loading model...')
 nlp = get_model(model)
 
 if text:
-
     data_load_state = st.text('Inference to model...')
     result = nlp(text+' '+nlp.tokenizer.mask_token)
     data_load_state.text('')
     for index, r in enumerate(result):
-        if r['token_str'].lower().strip() in history_keyword_text.lower().strip():
+        if r['token_str'].lower().strip() in history_keyword_text.lower().strip() and len(r['token_str'].lower().strip())>1:
+            #found from history, then increase the score of tokens
             result[index]['score']*=HISTORY_WEIGHT
+            
+    #sort the results        
     df=pd.DataFrame(result).sort_values(by='score', ascending=False)
-    
-    #result={k: v for k, v in sorted(result.items(), key=lambda item: item[0])}
+    #show the results as a table
     st.table(df)