Update app.py

app.py

@@ -65,35 +65,43 @@ vectorstore = get_vectorstore(docs)
 
 
 
-def main(vectorstore):
-    
-    
-    st.set_page_config(page_title="Conversational RAG Chatbot", page_icon=":robot:")
-    st.title("Conversational RAG Chatbot")
+def main():
 
-    
+    st.set_page_config(page_title="Chat with multiple PDFs",
+                       page_icon=":books:")
+    st.write(css, unsafe_allow_html=True)
 
- 
-    
+    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 prompt := st.text_input("Enter your question:"):
-        msgs = st.session_state.get("chat_history", StreamlitChatMessageHistory(key="special_app_key"))
-        st.chat_message("human").write(prompt)
+    st.header("Chat with multiple PDFs :books:")
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        handle_userinput(user_question)
 
-        conversation_chain = create_conversational_rag_chain(vectorstore)
-        input_dict = {"input": prompt, "chat_history": msgs.messages}
-        config = {"configurable": {"session_id": "any"}}
-        response = conversation_chain.invoke(input_dict, config)
-        st.chat_message("ai").write(response["answer"])
 
-        if "docs" in response and response["documents"]:
-            for index, doc in enumerate(response["documents"]):
-                with st.expander(f"Document {index + 1}"):
-                    st.write(doc)
+def handle_userinput(user_question,vectorstore):
+    Rag_chain  create_conversational_rag_chain()
+    response = Rag_chain({'question': user_question})
+    st.session_state.chat_history = response['chat_history']
+
+    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)
         else:
-            st.error("Conversation chain is not available.")
+            st.write(bot_template.replace(
+                "{{MSG}}", message.content), unsafe_allow_html=True)
+
+    if 'retrieved_documents' in response:
+        st.subheader("Retrieved Documents")
+        for doc in response['source_documents']:
+            st.write(f"Document: {doc.metadata['source']}")
+            st.write(doc.page_content)       
+
 
-        st.session_state["chat_history"] = msgs
 
 def create_conversational_rag_chain(vectorstore):
     
@@ -135,14 +143,7 @@ def create_conversational_rag_chain(vectorstore):
     rag_chain = create_retrieval_chain(ha_retriever, question_answer_chain)
     msgs = StreamlitChatMessageHistory(key="special_app_key")
 
-    conversation_chain = RunnableWithMessageHistory(
-        rag_chain,
-        lambda session_id: msgs,
-        input_messages_key="input",
-        history_messages_key="chat_history",
-        output_messages_key="answer",
-    )
-    return conversation_chain
+    return rag_chain