Update app.py

app.py

@@ -22,6 +22,7 @@ from langchain.chains import ConversationalRetrievalChain
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.runnables import RunnablePassthrough
 from langchain import hub
+from state_manager import StateManager
 
 
 
@@ -120,10 +121,10 @@ def main():
     st.header("Chat with multiple PDFs :books:")
 
     
-
+    state_manager = StateManager()
 
     if user_question := st.text_input("Ask a question about your documents:"):
-        handle_userinput(user_question)
+        handle_userinput(user_question,state_manager)
     
    
     with st.sidebar:
@@ -139,11 +140,10 @@ def main():
                 text_chunks = get_text_chunks(raw_text)
 
                 # create vector store
-                vectorstore = get_vectorstore(text_chunks)
+                state_manager.create_vectorstore(text_chunks)
 
                 # create conversation chain
-                st.session_state.conversation = get_conversation_chain(
-                    vectorstore)
+                state_manager.create_conversation_chain()
 
     
     
@@ -156,35 +156,32 @@ def main():
     
     
 
-def handle_userinput(user_question ):
-    
+def handle_userinput(user_question, state_manager):
     if "chat_history" not in st.session_state:
         st.session_state["chat_history"] = [
-        {"role": "assistant", "content": "Hi, I'm a Q&A chatbot who is based on your imported pdf documents  . How can I help you?"}
-    ]
-    
-    
+            {"role": "assistant", "content": "Hi, I'm a Q&A chatbot who is based on your imported pdf documents. How can I help you?"}
+        ]
+
     st.session_state.chat_history.append({"role": "user", "content": user_question})
-    
-    
 
-    
-    # Invoke conversation chain
-    response = st.session_state.conversation({"question": user_question})
-    st.session_state.chat_history.append({"role": "assistant", "content": response})
+    if state_manager.vectorstore is not None and state_manager.conversation_chain is not None:
+        # Invoke conversation chain
+        response = state_manager.conversation_chain({"question": user_question})
+        st.session_state.chat_history.append({"role": "assistant", "content": response})
 
     for i, message in enumerate(st.session_state.chat_history):
         if i % 2 == 0:
             st.write(user_template.replace(
-                "{{MSG}}", message['content']), unsafe_allow_html=True) 
+                "{{MSG}}", message['content']), unsafe_allow_html=True)
         else:
             st.write(bot_template.replace(
                 "{{MSG}}", message['content']), unsafe_allow_html=True)
 
     st.subheader("Your documents")
-        
-    for doc in docs:
-        st.write(f"Document: {doc}")
+    if state_manager.vectorstore is not None:
+        docs = state_manager.vectorstore.as_retriever().get_relevant_documents(user_question)
+        for doc in docs:
+            st.write(f"Document: {doc}")