import uuid
import streamlit as st
from dotenv import load_dotenv
import scholar_integration
import rag_chatbot
from document_processor import DocumentProcessor
from utils import sample_suggestions

# Load environment variables from .env file
load_dotenv()

st.set_page_config(page_title="3Step AI Chatbot", layout="wide")

# Initialize session variables
if "messages" not in st.session_state:
    st.session_state.messages = []
if "toggle" not in st.session_state:
    st.session_state.toggle = False
if "vector_store" not in st.session_state:
    st.session_state.vector_store = None
if "user_id" not in st.session_state:
    st.session_state.user_id = uuid.uuid4().hex[:8]  # Unique ID per user
if "processing_canceled" not in st.session_state:
    st.session_state.processing_canceled = False
if "selected_question" not in st.session_state:
    st.session_state.selected_question = None
if "suggested_questions" not in st.session_state:
    st.session_state.suggested_questions = sample_suggestions
if "processing_question" not in st.session_state:
    st.session_state.processing_question = False
if "current_question" not in st.session_state:
    st.session_state.current_question = None

def main():
    # Initialize document processor
    doc_processor = DocumentProcessor()

    mode = st.sidebar.radio("Choose mode:", ["RAG Chatbot", "Academic Research Assistant"])

    if mode == "RAG Chatbot":
        # Your existing RAG chatbot code
        rag_chatbot.add_rag_chatbot_interface()

    else:
        st.empty()
        # Add the scholar integration
        scholar_integration.add_scholarly_chat_interface()

if __name__ == "__main__":
    main()