import streamlit as st
from upload import upload_file_to_vectara
#from query import process_queries
import os
from st_app import launch_bot
import nest_asyncio
import asyncio
import uuid



# Setup for HTTP API Calls to Amplitude Analytics
if 'device_id' not in st.session_state:
    st.session_state.device_id = str(uuid.uuid4())

if "feedback_key" not in st.session_state:
    st.session_state.feedback_key = 0

if __name__ == "__main__":
    # Ensure set_page_config is the first Streamlit command
    st.set_page_config(page_title="STC Bank Assistant", layout="centered")

        # Load external CSS for custom styling
    with open("style.css", "r") as f:
        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)

    # Main UI layout
    st.markdown(
        """
        <h1>Digital Bank</h1>

        <div class="icon-container">
            <!-- This yellowish box is the icon background -->
        </div>

        <h4>Add additional files here</h4>
        """,
        unsafe_allow_html=True
    )

    # Fetch credentials from environment variables
    customer_id = os.getenv("VECTARA_CUSTOMER_ID", "")
    api_key = os.getenv("VECTARA_API_KEY", "")
    corpus_id = os.getenv("VECTARA_CORPUS_ID", "")
    corpus_key = os.getenv("VECTARA_CORPUS_KEY", "")

    # File uploader with drag-and-drop text + limit note
    uploaded_files = st.file_uploader(
        "Drag and drop file here\nLimit 200MB per file",
        type=["pdf", "docx", "xlsx"],
        accept_multiple_files=True
    )

    # If credentials exist and files are uploaded, handle them
    if uploaded_files and customer_id and api_key and corpus_id and corpus_key:
        for file in uploaded_files:
            response = upload_file_to_vectara(file, customer_id, api_key, corpus_key)
            st.write(f"Uploaded {file.name}: {response}")

    #if st.button("Run Queries"):
    #    results = process_queries(customer_id, api_key, corpus_key)
    #    for question, answer in results.items():
    #        st.subheader(question)
    #        st.write(answer)

    nest_asyncio.apply()
    asyncio.run(launch_bot())