from apps import mlm, vqa
import os
import streamlit as st
from multiapp import MultiApp

def read_markdown(path, parent="./sections/"):
    with open(os.path.join(parent, path)) as f:
        return f.read()

def main():
    st.set_page_config(
        page_title="Multilingual VQA",
        layout="wide",
        initial_sidebar_state="collapsed",
        page_icon="./misc/mvqa-logo-3-white.png",
    )

    st.title("Multilingual Visual Question Answering")
    st.write(
        "[Gunjan Chhablani](https://huggingface.co/gchhablani), [Bhavitvya Malik](https://huggingface.co/bhavitvyamalik)"
    )

    image_col, intro_col = st.beta_columns([3, 8])
    image_col.image("./misc/mvqa-logo-3-white.png", use_column_width="always")
    intro_col.write(read_markdown("intro.md"))
    with st.beta_expander("Usage"):
        st.write(read_markdown("usage.md"))

    with st.beta_expander("Article"):
        st.write(read_markdown("abstract.md"))
        st.write(read_markdown("caveats.md"))
        st.write("## Methodology")
        col1, col2 = st.beta_columns([1,1])
        col1.image(
            "./misc/article/Multilingual-VQA.png",
            caption="Masked LM model for Image-text Pretraining.",
        )
        col2.markdown(read_markdown("pretraining.md"))
        st.markdown(read_markdown("finetuning.md"))
        st.write(read_markdown("challenges.md"))
        st.write(read_markdown("social_impact.md"))
        st.write(read_markdown("references.md"))
        st.write(read_markdown("checkpoints.md"))
        st.write(read_markdown("acknowledgements.md"))

    app = MultiApp()
    app.add_app("Visual Question Answering", vqa.app)
    app.add_app("Mask Filling", mlm.app)
    app.run()

if __name__ == "__main__":
    main()