Update app.py

app.py

@@ -108,7 +108,7 @@ countOfAdmission = len(pastHistoryEpDate)
 
 
 # List of Model available
-model = st.sidebar.selectbox('Select Model', ('BertSummarizer','BertGPT2','t5seq2eq','t5','gensim','pysummarizer'))
+model = st.sidebar.selectbox('Select Model', ('BertSummarizer'))
 
 # ===== to display selected patient and admission id on main page
 col3,col4 = st.columns(2) 
@@ -205,7 +205,18 @@ if not(btnPastHistory) and not(btnDailyNarrative):
 #                             entlist = ",".join(ent)
 #                             st.markdown(f'<p style="background-color:{ent_col[entity]};color:#080808;font-size:16px;">{entlist}</p>', #unsafe_allow_html=True)
 
+##===== BERT Summary tokenizer =====
 
+def BertSummarizer(input_text):
+    from transformers import BigBirdTokenizer
+    from summarizer import Summarizer
+
+    bertsummarizer = Summarizer()
+
+    model = Summarizer()
+    result = model(input_text,ratio=0.4)
+    
+    return result
 ##======================== Start of NER Tagging ========================
 # ====== Old NER ======
 # doc = nlp(str(original_text2))
@@ -301,14 +312,16 @@ ent_html = displacy.render(doc0, style='ent', options=options)
 
 def run_model(input_text):    
     if model == "BertSummarizer":
-        output = original_text['BertSummarizer2s'].values
+#        output = original_text['BertSummarizer2s'].values
+#        st.write('Summary')
+        output = BertSummarizer(input_text)
         st.write('Summary')
-
+        st.success(output)
+        
     elif model == "BertGPT2":
         output = original_text['BertGPT2'].values
         st.write('Summary')
-        
-      
+              
     elif model == "t5seq2eq": 
         output = original_text['t5seq2eq'].values
         st.write('Summary')