Update app.py

app.py

@@ -75,15 +75,7 @@ st.title("Bangla and English Summarizer:")
 st.subheader("Please, Upload your PDF/Images or write texts to see the summarize options: ")
 #@st.cache_resource(experimental_allow_widgets=True)
 def main():
-    #global tokenizer, model
-    #tokenizer = AutoTokenizer.from_pretrained('t5-base')
-    #model = AutoModelWithLMHead.from_pretrained('t5-base', return_dict=True)
     """ NLP Based Application with Streamlit """
-    st.markdown("""
-    	#### Description
-    	##This is a Natural Language Processing(NLP) base Application that is useful for basic NLP tasks such as follows:
-        PDF Document/Image's Text Summarization.
-    	""")
     def change_photo_state():
         st.session_state["photo"]="done"
     message = st.text_input("Type your text here!")
@@ -126,24 +118,24 @@ def main():
             if st.button("English"): 
                 text=pytesseract.image_to_string(img)
                 engsum(text)
-        elif uploaded_photo==None and camera_photo==None:
+        elif:
             text = message
             if st.button("Bangla"):
                 bansum(text)
             if st.button("English"): 
                 engsum(text)  
-        if st.button("English Text Generation"): 
-            def query(payload):
-            	response = requests.post(API_URL2, headers=headers2, json=payload)
-            	return response.json()
+        # if st.button("English Text Generation"): 
+        #     def query(payload):
+        #     	response = requests.post(API_URL2, headers=headers2, json=payload)
+        #     	return response.json()
             	
-            out = query({
-            	"inputs": text,
-            })
-            if isinstance(out, list) and out[0].get("generated_text"):
-                text_output = out[0]["generated_text"]
-                st.success(text_output)
-                #text=text_output
+        #     out = query({
+        #     	"inputs": text,
+        #     })
+        #     if isinstance(out, list) and out[0].get("generated_text"):
+        #         text_output = out[0]["generated_text"]
+        #         st.success(text_output)
+        #         #text=text_output
 
 if __name__ == '__main__':
     main()