app.py

import streamlit as st

from streamlit_pdf_viewer import pdf_viewer

st.set_page_config(layout="wide")

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
    )

    # Temperature
    temperature = st.slider(
        "Temperature",
        min_value=0.0, 
        max_value=1.0, 
        value=0.0, 
        step=0.05, 
    )

    # 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 col1:
    st.write('testing', uploaded_file)

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