Update app.py

app.py

@@ -70,10 +70,8 @@ def get_conversation_chain(vectorstore, model_name):
                                                                memory=memory,
                                                                return_source_documents=True
                                                                )
-    
-    result = conversation_chain
 
-    return result, result['source_documents'][0]
+    return conversation_chain
 
 
 def handle_userinput(user_question):
@@ -81,6 +79,8 @@ def handle_userinput(user_question):
 
     st.session_state.chat_history = response['chat_history']
 
+    st.session_state.retrieved_text = response['source_documents'][0]
+
     for i, message in enumerate(st.session_state.chat_history):
         if i % 2 == 0:
             st.write(user_template.replace(
@@ -89,6 +89,14 @@ def handle_userinput(user_question):
             st.write(bot_template.replace(
                 "{{MSG}}", message.content), unsafe_allow_html=True)
 
+    for i, message in enumerate(st.session_state.retrieved_text):
+        if i % 2 == 0:
+            st.write(user_template.replace(
+                "{{MSG}}", message.content), unsafe_allow_html=True)
+        else:
+            st.write(bot_template.replace(
+                "{{MSG}}", message.content), unsafe_allow_html=True)
+
 
 # main code
 load_dotenv()
@@ -101,6 +109,8 @@ if "conversation" not in st.session_state:
     st.session_state.conversation = None
 if "chat_history" not in st.session_state:
     st.session_state.chat_history = None
+if "retrieved_text" not in st.session_state:
+    st.session_state.retrieved_text = None
 
 st.header("Chat with multiple PDFs :books:")
 user_question = st.text_input("Ask a question about your documents:")
@@ -124,6 +134,6 @@ with st.sidebar:
             vectorstore = get_vectorstore(text_chunks)
 
             # create conversation chain
-            st.session_state.conversation, retrieved_docs = get_conversation_chain(vectorstore, model_name)
+            st.session_state.conversation = get_conversation_chain(vectorstore, model_name)
             st.text_area(retrieved_docs)