Update app.py

app.py

@@ -111,6 +111,8 @@ def entity_analyzer(my_text):
 	allData = ['"Token":{},\n"Entities":{}'.format(tokens,entities)]
 	return allData
 def main():
+    tokenizer = AutoTokenizer.from_pretrained('t5-base')
+    model = AutoModelWithLMHead.from_pretrained('t5-base', return_dict=True)
     """ NLP Based Application with Streamlit """
     st.markdown("""
     	#### Description
@@ -192,8 +194,6 @@ def main():
         #     st.success(summary_result)
         if st.checkbox("Mark to English Text Summarization!"):
 			#st.title("Summarize Your Text for English only!")
-            tokenizer = AutoTokenizer.from_pretrained('t5-base')
-            model = AutoModelWithLMHead.from_pretrained('t5-base', return_dict=True)
 			#st.text("Using Google T5 Transformer ..")
             inputs = tokenizer.encode("summarize: " + text,
 						return_tensors='pt',
@@ -202,7 +202,7 @@ def main():
             summary_ids = model.generate(inputs, max_length=150, min_length=80, length_penalty=5., num_beams=2)
             summary = tokenizer.decode(summary_ids[0])
             st.success(summary)
-        if st.button("refresh"):
-            st.experimental_rerun()
+    if st.button("refresh"):
+       st.experimental_rerun()
 if __name__ == '__main__':
     main()