Update app.py

app.py

@@ -125,44 +125,42 @@ def main():
         #img = cv2.imread("scholarly_text.jpg")
             text = message
         if st.checkbox("Show Named Entities English/Bangla"):
-		   entity_result = entity_analyzer(text)
-		   st.json(entity_result)
-		if st.checkbox("Show Sentiment Analysis for English"):
-		   blob = TextBlob(text)
-		   result_sentiment = blob.sentiment
-		   st.success(result_sentiment)
-		if st.checkbox("Spell Corrections for English"):
-		   st.success(TextBlob(text).correct())
-		if st.checkbox("Text Generation"):
-		   ok = st.button("Generate")
-		   if ok:
-		      tokenizer, model = load_models()
-		      input_ids = tokenizer(text, return_tensors='pt').input_ids
-		      st.text("Using Hugging Face Transformer, Contrastive Search ..")
-		      output = model.generate(input_ids, max_length=128)
-		      st.success(tokenizer.decode(output[0], skip_special_tokens=True))
-		if st.checkbox("Mark here, Text Summarization for English or Bangla!"):
+            entity_result = entity_analyzer(text)
+            st.json(entity_result)
+        if st.checkbox("Show Sentiment Analysis for English"):
+            blob = TextBlob(text)
+            result_sentiment = blob.sentiment
+            st.success(result_sentiment)
+        if st.checkbox("Spell Corrections for English"):
+            st.success(TextBlob(text).correct())
+        if st.checkbox("Text Generation"):
+            ok = st.button("Generate")
+            if ok:
+                tokenizer, model = load_models()
+                input_ids = tokenizer(text, return_tensors='pt').input_ids
+                st.text("Using Hugging Face Transformer, Contrastive Search ..")
+                output = model.generate(input_ids, max_length=128)
+                st.success(tokenizer.decode(output[0], skip_special_tokens=True))
+        if st.checkbox("Mark here, Text Summarization for English or Bangla!"):
 			#st.subheader("Summarize Your Text for English and Bangla Texts!")
 			#message = st.text_area("Enter the Text","Type please ..")
 			#st.text("Using Gensim Summarizer ..")
 			#st.success(mess)
-			summary_result = summarize(text)
-			st.success(summary_result)
-		if st.checkbox("Mark to better English Text Summarization!"):
+            summary_result = summarize(text)
+            st.success(summary_result)
+        if st.checkbox("Mark to better 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)
+            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,
+            inputs = tokenizer.encode("summarize: " + text,
 						return_tensors='pt',
 										max_length=512,
 										truncation=True)
-			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)
-	
-	st.sidebar.subheader("About App")
-	st.sidebar.markdown("By [Soumen Sarker](https://soumen-sarker-personal-website.streamlitapp.com/)")
-
+            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)
+    st.sidebar.subheader("About App")
+    st.sidebar.markdown("By [Soumen Sarker](https://soumen-sarker-personal-website.streamlitapp.com/)")
 if __name__ == '__main__':
-	main()
+    main()