import streamlit as st

from streamlit_pdf_viewer import pdf_viewer

st.set_page_config(layout="wide")

def main():
    with st.sidebar:
        st.title('Document Summarization and QA System')
        # st.markdown('''
        # ## About this application
        # Upload a pdf to ask questions about it. This retrieval-augmented generation (RAG) workflow uses:
        # - [Streamlit](https://streamlit.io/)
        # - [LlamaIndex](https://docs.llamaindex.ai/en/stable/)
        # - [OpenAI](https://platform.openai.com/docs/models)
        # ''')

        # st.write('Made by ***Nate Mahynski***')
        # st.write('nathan.mahynski@nist.gov')

        # Select Provider
        provider = st.selectbox(
            label="Select LLM Provider",
            options=['openai', 'huggingface'],
            index=0
        )

        # Select LLM
        if provider == 'openai':
            llm_list = ['gpt-3.5-turbo', 'gpt-4', 'gpt-4-turbo', 'gpt-4o']
        else:
            llm_list = []

        llm = st.selectbox(
            label="Select LLM Model",
            options=llm_list,
            index=0
        )

        # Enter Token
        token = st.text_input(
            "Enter your token",
            value=None
        )

        uploaded_file = st.file_uploader(
            "Choose a PDF file to upload", 
            type=['pdf'], 
            accept_multiple_files=False
        )

        if uploaded_file is not None:
            # Parse the file
            pass

    col1, col2 = st.columns(2)

    with col2:
        if uploaded_file is not None:
            # Display the pdf
            pdf_viewer(input=uploaded_file, width=700)