Update app.py

app.py

@@ -70,27 +70,6 @@ def main():
     	This is a Natural Language Processing(NLP) Based Application useful for basic NLP tasks
         Named Entity Recognition, Sentiment Analysis, Spell Corrections, Human Level Text Generation, and Summarization
     	""")                         
-	# Entity Extraction
-	text = st.text_input("Type your text!")
-	if st.checkbox("Show Named Entities"):
-	   entity_result = entity_analyzer(text)
-	   st.json(entity_result)
-	# Sentiment Analysis
-	if st.checkbox("Show Sentiment Analysis"):
-	   blob = TextBlob(text)
-	   result_sentiment = blob.sentiment
-	   st.success(result_sentiment)
-    #Text Corrections
-	if st.checkbox("Spell Corrections"):
-	   st.success(TextBlob(text).correct())
-	if st.checkbox("Text Generation"):
-		ok = st.button("Generate")
-		tokenizer, model = load_models()
-		if ok:
-		    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))
 	def change_photo_state():
 		st.session_state["photo"]="done"
 	st.subheader("Summary section, feed your image!")
@@ -117,6 +96,25 @@ def main():
 			#our_image=load_image("image.jpg")
 			#img = cv2.imread("scholarly_text.jpg")
 			text = message
+		if st.checkbox("Show Named Entities"):
+		   entity_result = entity_analyzer(text)
+	       st.json(entity_result)
+	    # Sentiment Analysis
+	    if st.checkbox("Show Sentiment Analysis"):
+	       blob = TextBlob(text)
+	       result_sentiment = blob.sentiment
+	       st.success(result_sentiment)
+	    #Text Corrections
+	    if st.checkbox("Spell Corrections"):
+	       st.success(TextBlob(text).correct())
+	    if st.checkbox("Text Generation"):
+	       ok = st.button("Generate")
+	       tokenizer, model = load_models()
+	       if ok:
+	          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))
 		# Summarization
 		if st.checkbox("Mark here, Text Summarization for English or Bangla!"):
 			#st.subheader("Summarize Your Text for English and Bangla Texts!")