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 Image Captioning 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.write(read_markdown("intro/intro.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")) toc.subsubheader("Dataset") st.write(read_markdown("pretraining/dataset.md")) _, col2, _ = st.beta_columns([1,3,1]) with col2: st.image("./misc/Multilingual-IC.png", use_column_width='always') toc.subsubheader("Model") st.write(read_markdown("pretraining/model.md")) # toc.subsubheader("MLM Training 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") # st_tensorboard(logdir='./logs/pretrain_logs', port=6006) toc.header("Challenges and Technical Difficulties") st.write(read_markdown("challenges.md")) toc.header("Limitations and Biases") st.write(read_markdown("bias.md")) _, col2, col3, _ = st.beta_columns([0.5,2.5,2.5,0.5]) with col2: st.image("./misc/examples/female_dev_1.jpg", width=350, caption = 'German Caption: arbeitet an einem Computer.', use_column_width='always') with col3: st.image("./misc/examples/female_doctor.jpg", width=350, caption = 'English Caption: A portrait of , a doctor who specializes in health care.', use_column_width='always') _, col2, col3, _ = st.beta_columns([0.5,2.5,2.5,0.5]) with col2: st.image("./misc/examples/female_doctor_1.jpg", width=350, caption = 'Spanish Caption: El Dr. es un estudiante de posgrado.', use_column_width='always') with col3: st.image("./misc/examples/women_cricket.jpg", width=350, caption = 'English Caption: of India bats against of Australia during the first Twenty20 match between India and Australia at Indian Bowl Stadium in New Delhi on Friday. - PTI', use_column_width='always') _, col2, col3, _ = st.beta_columns([0.5,2.5,2.5,0.5]) with col2: st.image("./misc/examples/female_dev_2.jpg", width=350, caption = "French Caption: Un écran d'ordinateur avec un écran d'ordinateur ouvert.", use_column_width='always') with col3: st.image("./misc/examples/female_biker_resized.jpg", width=350, caption = 'German Caption: auf dem Motorrad von .', use_column_width='always') 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_scope.md")) toc.subheader("Social Impact") st.write(read_markdown("conclusion_future_work/social_impact.md")) toc.header("References") st.write(read_markdown("references.md")) toc.header("Acknowledgements") st.write(read_markdown("acknowledgements.md")) toc.generate()