Update app.py

app.py

@@ -9,7 +9,18 @@ def generate_summary(text):
     inputs = tokenizer([text], max_length=1024, return_tensors='pt', truncation=True)
     summary_ids = model.generate(inputs['input_ids'], max_new_tokens=100, do_sample=False)
     summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-    return summary
+    
+    # Post-process the summary to include only specific points
+    important_points = extract_important_points(summary)
+    
+    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
+    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)
 
 # Streamlit interface
 st.title("Text Summarization App")
@@ -21,7 +32,7 @@ if st.button("Generate Summary"):
     if user_input:
         with st.spinner("Generating summary..."):
             summary = generate_summary(user_input)
-        st.subheader("Summary:")
+        st.subheader("Filtered Summary:")
         st.write(summary)
     else:
         st.warning("Please enter text to summarize.")