Update app.py

app.py

@@ -32,6 +32,13 @@ from langchain.chains import ConversationalRetrievalChain
 #  from langchain.llms import HuggingFaceHub
 from langchain_community.llms import HuggingFaceHub
 
+##################################################################################
+#  Admin flags
+DISPLAY_DIALOG_LINES=6
+
+SESSION_STARTED = False
+
+
 ##################################################################################
 def extract_pdf_text(pdf_docs):
     text = ""
@@ -102,17 +109,21 @@ def prepare_conversation(vectorstore):
 def process_user_question(user_question):
 
     print('process_user_question called: \n')
+
+    if (! SESSION_STARTED):
+        print('No Session')
+        st.write( 'Please upload and analyze your PDF files first!')
+        return
+        
     if user_question == None :
         print('question is null')
         return
     if user_question == '' :
         print('question is blank')
         return
-
     if st == None :
         print('session is null')
         return
-
     if st.session_state == None :
         print('session STATE is null')
         return
@@ -133,12 +144,12 @@ def process_user_question(user_question):
 
     for i, message in enumerate(st.session_state.chat_history):
 
-        #  Scrolling looses the last printed line, only print the last 6 lines
+        #  Scrolling does not display the last printed line, 
+        #    so only print the last 6 lines
         #
-        print('results_size on msg: ', results_size, i, ( results_size - 6 ) )
-        if results_size > 6:
-            if i < ( results_size - 6 ):
-                print( 'skipped line', i)
+        print('results_size on msg: ', results_size, i, ( results_size - DISPLAY_DIALOG_LINES ) )
+        if results_size > DISPLAY_DIALOG_LINES:
+            if i < ( results_size - DISPLAY_DIALOG_LINES ):
                 continue
                 
         if i % 2 == 0:
@@ -205,7 +216,7 @@ def main():
     st.header(f"Pennwick File Analyzer")
 
     user_question = None
-    user_question = st.text_input("Ask the Open Source - Flan-t5 Model a question about your uploaded documents:")
+    user_question = st.text_input("Ask the  Open Source - Flan-T5 Model  a question about your uploaded documents:")
     if user_question != None:
         print( 'calling process question', user_question)
         process_user_question(user_question)
@@ -249,6 +260,8 @@ def main():
                 # # create conversation chain
                 st.session_state.conversation = prepare_conversation(vectorstore)
 
+                SESSION_STARTED = True
+                
                 # Mission Complete!
                 global_later = datetime.now()
                 st.write("Files Vectorized - Total EXECUTION Time =",