import streamlit as st
from apps.utils import read_markdown
from .streamlit_tensorboard import st_tensorboard, kill_tensorboard
from .utils import Toc


    
def app(state=None):
    #kill_tensorboard()
    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. 🤗 In case the sidebar isn't properly rendered, please change to a smaller window size and back to full screen.")
    
    st.header("Table of Contents")
    toc.placeholder()

    toc.header("Introduction and Motivation")
    st.info("**News**: Two days back, a paper using CLIP-Vision and BERT has been posted on arXiv! The paper uses LXMERT objects and achieves 80% on the English VQAv2 dataset. It would be interesting to see how it performs on our multilingual dataset. Check it out here: https://arxiv.org/pdf/2107.06383.pdf")
    st.write(read_markdown("intro/intro_part_1.md"))
    with st.beta_expander("FasterRCNN Approach"):
        st.write(read_markdown("intro/faster_rcnn_approach.md"))
    st.write(read_markdown("intro/intro_part_2.md"))
    toc.subheader("Novel Contributions")
    st.write(read_markdown("intro/contributions.md"))
    
    toc.header("Methodology")

    toc.subheader("Pre-training")
    st.write(read_markdown("pretraining/intro.md"))
    # col1, col2 = st.beta_columns([5,5])
    st.image(
        "./misc/article/Multilingual-VQA.png",
        caption="Masked LM model for Image-text Pre-training.",
    )
    toc.subsubheader("MLM Dataset")
    st.write(read_markdown("pretraining/data.md"))
    toc.subsubheader("MLM Model")
    st.write(read_markdown("pretraining/model.md"))
    toc.subsubheader("MLM Training Logs")
    st.write("Click on the expandable region to see the TensorBoard logs.")
    st.info("In case the TensorBoard logs are not displayed, please visit this link: https://huggingface.co/flax-community/multilingual-vqa-pt-ckpts/tensorboard")
    with st.beta_expander("MLM TensorBoard Logs"):
        st_tensorboard(logdir='./logs/pretrain_logs', port=6006)
    
    
    toc.subheader("Finetuning")
    toc.subsubheader("VQA Dataset")
    st.write(read_markdown("finetuning/data.md"))
    toc.subsubheader("VQA Model")
    st.write(read_markdown("finetuning/model.md"))
    toc.subsubheader("VQA Training Logs")
    st.write("Click on the expandable region to see the TensorBoard logs.")
    st.info("In case the TensorBoard logs are not displayed, please visit this link: https://huggingface.co/flax-community/multilingual-vqa-pt-60k-ft/tensorboard")
    with st.beta_expander("VQA TensorBoard Logs"):
        st_tensorboard(logdir='./logs/finetune_logs', port=6007)
    
    
    toc.header("Challenges and Technical Difficulties")
    st.write(read_markdown("challenges.md"))
    
    toc.header("Limitations and Bias")
    st.write(read_markdown("limitations.md"))
    
    toc.header("Conclusion, Future Work, and Social Impact")
    # toc.subheader("Conclusion")
    # st.write(read_markdown("conclusion_future_work/conclusion.md"))
    # toc.subheader("Future Work")
    # st.write(read_markdown("conclusion_future_work/future_work.md"))
    # toc.subheader("Social Impact")
    st.write(read_markdown("conclusion_future_work/social_impact.md"))
    
    toc.header("References")
    toc.subheader("Papers")
    st.write(read_markdown("references/papers.md"))
    toc.subheader("Useful Links")
    st.write(read_markdown("references/useful_links.md"))


    toc.header("Checkpoints")
    st.write(read_markdown("checkpoints/checkpoints.md"))
    toc.subheader("Other Checkpoints")
    st.write(read_markdown("checkpoints/other_checkpoints.md"))
    
    toc.header("Acknowledgements")
    st.write(read_markdown("acknowledgements.md"))
    toc.generate()