| 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) |