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from apps import mlm, vqa, article, examples | |
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="auto", | |
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)" | |
) | |
st.sidebar.title("Multilingual VQA") | |
logo = st.sidebar.image("./misc/mvqa-logo-3-white.png") | |
st.sidebar.write("Multilingual VQA addresses the challenge of visual question answering in a multilingual setting. Here, we fuse CLIP Vision transformer into BERT and perform pre-training and fine-tuning on translated versions of Conceptual-12M and VQAv2 datasets. Please use the radio buttons below to navigate.") | |
app = MultiApp(state) | |
app.add_app("Article", article.app) | |
app.add_app("Visual Question Answering", vqa.app) | |
app.add_app("Mask Filling", mlm.app) | |
app.add_app("Examples", examples.app) | |
app.run() | |
state.sync() | |
if __name__ == "__main__": | |
main() | |