# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

import streamlit as st


def rag_chat_page():
    st.title("RAG")

    # File/Directory Upload Section
    st.sidebar.subheader("Upload Documents")
    uploaded_files = st.sidebar.file_uploader(
        "Upload file(s) or directory",
        accept_multiple_files=True,
        type=["txt", "pdf", "doc", "docx"],  # Add more file types as needed
    )

    # Process uploaded files
    if uploaded_files:
        st.sidebar.success(f"Successfully uploaded {len(uploaded_files)} files")

    # Chat Interface
    if "messages" not in st.session_state:
        st.session_state.messages = []

    # Display chat history
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

    # Chat input
    if prompt := st.chat_input("Ask a question about your documents"):
        # Add user message to chat history
        st.session_state.messages.append({"role": "user", "content": prompt})

        # Display user message
        with st.chat_message("user"):
            st.markdown(prompt)

        # Here you would add your RAG logic to:
        # 1. Process the uploaded documents
        # 2. Create embeddings
        # 3. Perform similarity search
        # 4. Generate response using LLM

        # For now, just echo a placeholder response
        response = f"I received your question: {prompt}\nThis is where the RAG response would go."

        # Add assistant response to chat history
        st.session_state.messages.append({"role": "assistant", "content": response})

        # Display assistant response
        with st.chat_message("assistant"):
            st.markdown(response)


rag_chat_page()