Update app.py

app.py

@@ -1,40 +1,21 @@
 import streamlit as st
 import pandas as pd
-
+from streamlit import cli as stcli
 from transformers import pipeline
 from sentence_transformers import SentenceTransformer, util
-semantic_model = SentenceTransformer('all-MiniLM-L6-v2')
+import sys
+
+HISTORY_WEIGHT = 100 # set history weight (if found any keyword from history, it will priorities based on its weight)
 
 @st.cache(allow_output_mutation=True)
 def get_model(model):
 	return pipeline("fill-mask", model=model, top_k=100)#set the 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="")
-#history_keyword_text=''
-
-text = st.text_input("Enter a text for auto completion...", value='Where is Bill')
-#text='Where is Bill'
-
-semantic_text = st.text_input("Enter users's history semantic (optional, i.e., 'Microsoft or President')", value="Microsoft")
-#semantic_text='President'
-
-model = st.selectbox("choose a model", ["roberta-base", "bert-base-uncased"])
-#model='roberta-base'
-nlp = get_model(model)
-#data_load_state = st.text('Loading model...')
-
-
-
-if text:
-#    data_load_state = st.text('Inference to model...')
+def main(nlp, semantic_model):
+    data_load_state = st.text('Inference to model...')
     result = nlp(text+' '+nlp.tokenizer.mask_token)
-#    data_load_state.text('')
-    sem_list=[_.strip() for _ in semantic_text.split(',')]
+    data_load_state.text('')
+    sem_list=[semantic_text.strip()]
     if len(semantic_text):
         predicted_seq=[rec['sequence'] for rec in result]
         predicted_embeddings = semantic_model.encode(predicted_seq, convert_to_tensor=True)
@@ -43,9 +24,8 @@ if text:
     
     for index, r in enumerate(result):
         if len(semantic_text):
-#            for j_index in  range(len(sem_list)):
                 if len(r['token_str'])>2: #skip spcial chars such as "?"
-                    result[index]['score']+=float(sum(cosine_scores[index]))
+                    result[index]['score']+=float(sum(cosine_scores[index]))*HISTORY_WEIGHT
         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
@@ -54,4 +34,27 @@ if text:
     df=pd.DataFrame(result).sort_values(by='score', ascending=False)
     
 #    show the results as a table
-    st.table(df)
+    st.table(df)
+#    print(df)
+    
+    
+if __name__ == '__main__':
+    if st._is_running_with_streamlit:
+        st.caption("This is a simple auto-completion where the next token is predicted per probability and a weight if it is appeared in keyword user's history or there is a similarity to semantic user's history")
+        history_keyword_text = st.text_input("Enter users's history <keywords matc> (optional, i.e., 'Gates')", value="")
+        
+        text = st.text_input("Enter a text for auto completion...", value='Where is Bill')
+        
+        semantic_text = st.text_input("Enter users's history <semantic> (optional, i.e., 'Microsoft or President')", value="Microsoft")
+        
+        model = st.selectbox("Choose a model", ["roberta-base", "bert-base-uncased"])
+        
+        data_load_state = st.text('Loading model...')
+
+        semantic_model = SentenceTransformer('all-MiniLM-L6-v2')
+        nlp = get_model(model)
+        
+        main(nlp, semantic_model)
+    else:
+        sys.argv = ['streamlit', 'run', sys.argv[0]]
+        sys.exit(stcli.main())