import streamlit as st
import os
from dotenv import load_dotenv
from langsmith import traceable

from app.chat import (
    initialize_session_state,
    display_chat_history,
)
from app.data_loader import get_data, load_docs
from app.document_processor import process_documents, save_vector_store_to_supabase, load_vector_store_from_supabase
from app.db import supabase
from app.config import Config
# Modularized helpers
import app.rag as rag
import app.vector_store as vs
import app.auth as auth
import app.ui as ui
from app.session import restore_user_session_if_needed, ensure_active_session
from app.workflows import prepare_vector_store_if_needed
from uuid import uuid4

load_dotenv()


BUCKET_NAME = Config.BUCKET_NAME
VECTOR_STORE_PREFIX = Config.VECTOR_STORE_PREFIX

@traceable(name="Main Chatbot RAG App")
def main():
    try:
        load_dotenv()
    except Exception:
        pass
    initialize_session_state()
    st.set_page_config(
        page_title="PNP-Bot", 
        page_icon="assets/favicon.ico",    
    )
    # Try restore Supabase session if user missing
    restore_user_session_if_needed()
    # Authentication gate
    user = st.session_state.get("user")
    if not user:
        auth.auth_view()
        return

    # Ensure we have an active chat session
    ensure_active_session(user["id"])

    # Sidebar: delegate completely to UI module
    with st.sidebar:
        ui.render_sidebar_sessions()

    # Vector store orchestration (delegated)
    vector_store = prepare_vector_store_if_needed(len(st.session_state["history"]))
    st.session_state["vector_store"] = vector_store

    if st.session_state["vector_store"] is not None:
        chain = rag.create_conversational_chain(st.session_state["vector_store"])
        display_chat_history(chain)


if __name__ == "__main__":
    main()