Update app.py

app.py

@@ -16,12 +16,15 @@ def generate_summary(text):
     return important_points
 
 def extract_important_points(summary):
-    # This is a very basic example of how you might filter for key phrases
-    # You can refine this with more sophisticated NLP techniques
+    # Filter the summary for sentences containing keywords related to change requests or important points
     keywords = ["change", "request", "important", "needs", "must", "critical", "required", "suggested"]
     filtered_lines = [line for line in summary.split('. ') if any(keyword in line.lower() for keyword in keywords)]
     return '. '.join(filtered_lines)
 
+# Initialize session state for input history if it doesn't exist
+if 'input_history' not in st.session_state:
+    st.session_state['input_history'] = []
+
 # Streamlit interface
 st.title("Text Summarization App")
 
@@ -32,10 +35,22 @@ if st.button("Generate Summary"):
     if user_input:
         with st.spinner("Generating summary..."):
             summary = generate_summary(user_input)
+            
+        # Save the input and summary to the session state history
+        st.session_state['input_history'].append({"input": user_input, "summary": summary})
+        
         st.subheader("Filtered Summary:")
         st.write(summary)
     else:
         st.warning("Please enter text to summarize.")
 
+# Display the history of inputs and summaries
+if st.session_state['input_history']:
+    st.subheader("History")
+    for i, entry in enumerate(st.session_state['input_history']):
+        st.write(f"**Input {i+1}:** {entry['input']}")
+        st.write(f"**Summary {i+1}:** {entry['summary']}")
+        st.write("---")
+
 # Instructions for using the app
 st.write("Enter your text in the box above and click 'Generate Summary' to get a summarized version of your text.")