apps/article.py

import streamlit as st
from apps.utils import read_markdown
from streamlit_tensorboard import st_tensorboard
from .utils import Toc
def app(state):
    toc = Toc()
    st.info("Welcome to our Multilingual-VQA demo. Please use the navigation sidebar to move to our demo, or scroll below to read all about our project. 🤗")
    st.header("Table of contents")
    toc.placeholder()
    toc.header("Introduction and Motivation")
    st.write(read_markdown("intro.md"))
    toc.subheader("Novel Contributions")
    st.write(read_markdown("contributions.md"))
    toc.header("Methodology")
    toc.subheader("Pre-training")
    st.write(read_markdown("pretraining.md"))
    st.image(
        "./misc/article/Multilingual-VQA.png",
        caption="Masked LM model for Image-text Pre-training.",
    )
    st.write("**Training Logs**")
    st_tensorboard(logdir='./logs/pretrain_logs', port=6006)
    toc.subheader("Finetuning")
    st.write(read_markdown("finetuning.md"))
    st.write("**Training Logs**")
    st_tensorboard(logdir='./logs/finetune_logs', port=6007)
    toc.header("Challenges and Technical Difficulties")
    st.write(read_markdown("challenges.md"))
    toc.header("Limitations")
    st.write(read_markdown("limitations.md"))
    toc.header("Conclusion, Future Work, and Social Impact")
    toc.subheader("Conclusion")
    st.write(read_markdown("conclusion.md"))
    toc.subheader("Future Work")
    st.write(read_markdown("future_work.md"))
    toc.subheader("Social Impact")
    st.write(read_markdown("social_impact.md"))
    toc.header("References")
    st.write(read_markdown("references.md"))
    toc.header("Checkpoints")
    st.write(read_markdown("checkpoints.md"))
    toc.subheader("Other Checkpoints")
    st.write(read_markdown("other_checkpoints.md"))
    toc.header("Acknowledgements")
    st.write(read_markdown("acknowledgements.md"))