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| 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) | |
| 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: <PERSON> 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 <PERSON>, 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. <PERSON> es un estudiante de posgrado.', use_column_width='always') | |
| with col3: | |
| st.image("./misc/examples/women_cricket.jpg", width=350, caption = 'English Caption: <PERSON> of India bats against <PERSON> 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: <PERSON> auf dem Motorrad von <PERSON>.', use_column_width='always') | |
| toc.header("Challenges and Technical Difficulties") | |
| st.write(read_markdown("challenges.md")) | |
| toc.header("Limitations and Biases") | |
| st.write(read_markdown("bias.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_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() |