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from apps import mlm, vqa | |
import streamlit as st | |
from session import _get_state | |
from multiapp import MultiApp | |
from apps.utils import read_markdown | |
def main(): | |
state = _get_state() | |
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)" | |
) | |
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(state) | |
app.add_app("Visual Question Answering", vqa.app) | |
app.add_app("Mask Filling", mlm.app) | |
app.run() | |
state.sync() | |
if __name__ == "__main__": | |
main() | |