Delete pages

pages/chatbot.py

@@ -1,47 +0,0 @@
-import streamlit as st
-from app import rag_query, memory, process_feedback
-
-st.title("🛡️ RAG Chatbot MB Ageas Life 🛡️")
-
-# Initialize session history
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-# Hiển thị lại tin nhắn cũ
-for i, message in enumerate(st.session_state.messages):
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-        if message["role"] == "assistant":
-            col1, col2 = st.columns([1, 15])
-            with col1:
-                if st.button("👍", key=f"thumbs_up_{i}"):
-                    process_feedback(st.session_state.messages[i-1]["content"], message["content"], True)
-            with col2:
-                if st.button("👎", key=f"thumbs_down_{i}"):
-                    process_feedback(st.session_state.messages[i-1]["content"], message["content"], False)
-
-# Nhận input người dùng
-if prompt := st.chat_input("Ask me any question related to MBAL"):
-    # Hiển thị tin nhắn người dùng
-    st.chat_message("user").markdown(prompt)
-    st.session_state.messages.append({"role": "user", "content": prompt})
-    memory.chat_memory.add_user_message(prompt)
-
-    # Gọi hàm RAG để trả lời
-    response = rag_query(prompt)
-
-    # Hiển thị tin nhắn chatbot
-    with st.chat_message("assistant"):
-        st.markdown(response)
-    st.session_state.messages.append({"role": "assistant", "content": response})
-    memory.chat_memory.add_ai_message(response)
-
-    st.rerun()
-
-# Sidebar
-# with st.sidebar:
-#     st.header("Lựa chọn khác")
-#     if st.button("Xóa lịch sử chat"):
-#         st.session_state.messages = []
-#         memory.clear()
-        # st.rerun()

pages/management.py

@@ -1,68 +0,0 @@
-import os
-import streamlit as st
-from langchain.document_loaders import DirectoryLoader, TextLoader, PyPDFLoader
-from langchain.text_splitter import CharacterTextSplitter
-from app import vectorstore
-
-
-st.title("Document Management")
-
-# File uploader
-uploaded_file = st.file_uploader("Choose a file", type=['txt', 'pdf', 'docx'])
-
-if uploaded_file is not None:
-    # Create a temporary directory to store the uploaded file
-    temp_dir = "temp_uploads"
-    os.makedirs(temp_dir, exist_ok=True)
-    file_path = os.path.join(temp_dir, uploaded_file.name)
-    
-    # Save the uploaded file temporarily
-    with open(file_path, "wb") as f:
-        f.write(uploaded_file.getbuffer())
-    
-    st.success(f"File {uploaded_file.name} successfully uploaded!")
-
-    # Process the uploaded file
-    if st.button("Process Document"):
-        with st.spinner("Processing document..."):
-            try:
-                # Load the document based on file type
-                if uploaded_file.type == "application/pdf":
-                    loader = PyPDFLoader(file_path)
-                elif uploaded_file.type == "text/plain":
-                    loader = TextLoader(file_path)
-                else:
-                    st.error("Unsupported file type.")
-                    st.stop()
-                
-                documents = loader.load()
-                
-                # Split the document into chunks
-                text_splitter = CharacterTextSplitter(chunk_size=800, chunk_overlap=150)
-                texts = text_splitter.split_documents(documents)
-                
-                # Add the chunks to the vectorstore
-                vectorstore.add_documents(texts)
-                
-                st.success(f"Document processed and added to the knowledge base!")
-            except Exception as e:
-                st.error(f"An error occurred: {e}")
-        
-        # Clean up: remove the temporary file
-        os.remove(file_path)
-
-# Display current documents in the knowledge base
-# st.subheader("Current Documents in Knowledge Base")
-# # This is a placeholder. You'll need to implement a method to retrieve and display
-# # the list of documents currently in your Chroma database.
-# st.write("Placeholder for document list")
-
-# # Option to clear the entire knowledge base
-# if st.button("Clear Knowledge Base"):
-#     if st.sidebar.checkbox("Are you sure you want to clear the entire knowledge base? This action cannot be undone."):
-#         try:
-#             # Clear the Chroma database
-#             vectorstore.delete()
-#             st.success("Knowledge base cleared!")
-#         except Exception as e:
-#             st.error(f"An error occurred: {e}")