gradio update

app.py

@@ -136,10 +136,15 @@ title = """
 <div style="text-align: center;max-width: 700px;">
     <h1>CauseWriter Chat with PDF • OpenAI</h1>
     <p style="text-align: center;">Upload a .PDF from your computer, click the "Load PDF to LangChain" button, <br />
-    when everything is ready, you can start asking questions about the pdf. <br />
-    This version is set to erase chat history and uses OpenAI as LLM.</p>
+    when everything is ready, you can start asking questions about the pdf. Limit ~11k words. <br />
+    This version is set to erase chat history automatically after page timeout and uses OpenAI as LLM.</p>
 </div>
 """
+# Global variable for tracking last interaction time
+last_interaction_time = 0
+full_summary = ""  # Added global full_summary
+
+# ... (rest of your imports and function definitions)
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
@@ -151,13 +156,22 @@ with gr.Blocks(css=css) as demo:
                 langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
                 load_pdf = gr.Button("Convert PDF to Magic AI language")
                 clear_btn = gr.Button("Clear Data")
+            
+            # New Textbox to display summary
+            summary_box = gr.Textbox(label="Document Summary", placeholder="Summary will appear here.", 
+                                     value=full_summary, interactive=False, rows=5)
         
         chatbot = gr.Chatbot([], elem_id="chatbot").style(height=450)
         question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter")
         submit_btn = gr.Button("Send Message")
 
+        # Function to update summary box
+        def update_summary_box():
+            global full_summary
+            summary_box.value = full_summary
+
     load_pdf.click(loading_pdf, None, langchain_status, queue=False)
-    load_pdf.click(pdf_changes, inputs=[pdf_doc], outputs=[langchain_status], queue=False)
+    load_pdf.click(pdf_changes, inputs=[pdf_doc], outputs=[langchain_status], queue=False).then(update_summary_box)  # Then update the summary_box
     clear_btn.click(clear_data, outputs=[langchain_status], queue=False)
     question.submit(add_text, [chatbot, question], [chatbot, question]).then(
         bot, chatbot, chatbot
@@ -166,4 +180,4 @@ with gr.Blocks(css=css) as demo:
         bot, chatbot, chatbot
     )
 
-demo.launch()
+demo.launch()