import streamlit as st from apps.utils import read_markdown from streamlit_tensorboard import st_tensorboard from toc import Toc def app(state): toc = Toc() st.title("Table of contents") 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. 🤗") 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")) toc.title("Title") for a in range(10): st.write("Blabla...") toc.header("Header 1") for a in range(10): st.write("Blabla...") toc.header("Header 2") for a in range(10): st.write("Blabla...") toc.subheader("Subheader 1") for a in range(10): st.write("Blabla...") toc.subheader("Subheader 2") for a in range(10): st.write("Blabla...") toc.generate()