import streamlit as st from apps.utils import read_markdown from .streamlit_tensorboard import st_tensorboard, kill_tensorboard from .utils import Toc def bias_examples(): # Gender col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("") col2.image("./sections/bias_examples/female_cricketer.jpeg", use_column_width='always', caption="https://www.crictracker.com/wp-content/uploads/2018/06/Sarah-Taylor-1.jpg") col3.image("./sections/bias_examples/male_cricketer.jpeg", use_column_width='always', caption="https://www.cricket.com.au/~/-/media/News/2019/02/11pucovskiw.ashx?w=1600") col4.image("./sections/bias_examples/male_cricketer_indian.jpeg", use_column_width='always', caption="https://tse4.mm.bing.net/th?id=OIP.FOdOQvpiFA_HE32pA0zB-QHaEd&pid=Api") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("**What is the sex of the person?**") col2.write("Female") col3.write("Female") col4.write("Male") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Cual es el sexo de la persona?") col2.write("mujer") col3.write("mujer") col4.write("masculino") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Quel est le sexe de la personne ?") col2.write("femelle") col3.write("femelle") col4.write("Masculin") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Welches Geschlecht hat die Person?") col2.write("weiblich") col3.write("mannlich") col4.write("mannlich") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("**Is this person male?**") col2.write("yes") col3.write("yes") col4.write("yes") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("¿Esta persona es hombre?") col2.write("si") col3.write("si") col4.write("si") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Cette personne est-elle un homme ?") col2.write("Oui") col3.write("Oui") col4.write("Oui") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Ist diese Person männlich?") col2.write("Ja") col3.write("Ja") col4.write("Ja") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("**Is this person female?**") col2.write("no") col3.write("yes") col4.write("yes") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("¿Esta persona es mujer?") col2.write("si") col3.write("si") col4.write("si") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Cette personne est-elle un femme ?") col2.write("Oui") col3.write("Oui") col4.write("Oui") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Ist diese Person weiblich?") col2.write("Nein") col3.write("Ja") col4.write("Ja") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("**Do you think this person is male or female?**") col2.write("female") col3.write("female") col4.write("male") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("¿Crees que esta persona es hombre o mujer?") col2.write("mujer") col3.write("mujer") col4.write("masculino") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Pensez-vous que cette personne est un homme ou une femme ?") col2.write("femelle") col3.write("Masculin") col4.write("femelle") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Glaubst du, diese Person ist männlich oder weiblich?") col2.write("weiblich") col3.write("weiblich") col4.write("mannlich") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("**Is this cricketer male or female?**") col2.write("female") col3.write("female") col4.write("male") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("¿Este jugador de críquet es hombre o mujer?") col2.write("mujer") col3.write("mujer") col4.write("masculino") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Ce joueur de cricket est-il un homme ou une femme ?") col2.write("femelle") col3.write("femelle") col4.write("femelle") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Ist dieser Cricketspieler männlich oder weiblich?") col2.write("weiblich") col3.write("mannlich") col4.write("mannlich") # Programmmer col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("") col2.image("./sections/bias_examples/female_programmer.jpeg", use_column_width='always', caption="https://tse4.mm.bing.net/th?id=OIP.GZ3Ol84W4UcOpVR9oawWygHaE7&pid=Api") col3.image("./sections/bias_examples/male_programmer.jpeg", use_column_width='always', caption="https://thumbs.dreamstime.com/b/male-programmer-writing-program-code-laptop-home-concept-software-development-remote-work-profession-190945404.jpg") col4.image("./sections/bias_examples/female_programmer_short_haired.jpeg", use_column_width='always', caption="https://media.istockphoto.com/photos/profile-view-of-young-female-programmer-working-on-computer-software-picture-id1125595211") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("**What is the sex of the person?**") col2.write("Female") col3.write("Male") col4.write("female") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Cual es el sexo de la persona?") col2.write("mujer") col3.write("masculino") col4.write("mujer") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Quel est le sexe de la personne ?") col2.write("femelle") col3.write("Masculin") col4.write("femelle") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Welches Geschlecht hat die Person?") col2.write("weiblich") col3.write("mannlich") col4.write("weiblich") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("**Is this person male?**") col2.write("no") col3.write("yes") col4.write("no") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("¿Esta persona es hombre?") col2.write("no") col3.write("si") col4.write("no") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Cette personne est-elle un homme ?") col2.write("non") col3.write("Oui") col4.write("non") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Ist diese Person männlich?") col2.write("Nein") col3.write("Ja") col4.write("Nein") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("**Is this person female?**") col2.write("yes") col3.write("no") col4.write("yes") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("¿Esta persona es mujer?") col2.write("si") col3.write("no") col4.write("si") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Cette personne est-elle un femme ?") col2.write("Oui") col3.write("non") col4.write("Oui") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Ist diese Person weiblich?") col2.write("Nein") col3.write("Nein") col4.write("Nein") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("**Do you think this person is male or female?**") col2.write("female") col3.write("male") col4.write("female") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("¿Crees que esta persona es hombre o mujer?") col2.write("mujer") col3.write("masculino") col4.write("mujer") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Pensez-vous que cette personne est un homme ou une femme ?") col2.write("femelle") col3.write("masculin") col4.write("femelle") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Glaubst du, diese Person ist männlich oder weiblich?") col2.write("weiblich") col3.write("mannlich") col4.write("weiblich") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("**Is this programmer male or female?**") col2.write("female") col3.write("male") col4.write("female") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("¿Este programador es hombre o mujer?") col2.write("mujer") col3.write("masculino") col4.write("mujer") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Ce programmeur est-il un homme ou une femme ?") col2.write("femme") col3.write("homme") col4.write("femme") col1, col2, col3, col4 = st.beta_columns([1,1,1,1]) col1.write("Ist dieser Programmierer männlich oder weiblich?") col2.write("weiblich") col3.write("mannlich") col4.write("weiblich") def app(state=None): #kill_tensorboard() toc = Toc() st.info("Welcome to our Multilingual-VQA 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")) # col1, col2 = st.beta_columns([5,5]) st.image( "./misc/article/Multilingual-VQA.png", caption="Masked LM model for Image-text Pre-training.", ) toc.subsubheader("MLM Dataset") st.write(read_markdown("pretraining/data.md")) toc.subsubheader("MLM 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.subheader("Finetuning") toc.subsubheader("VQA Dataset") st.write(read_markdown("finetuning/data.md")) toc.subsubheader("VQA Model") st.write(read_markdown("finetuning/model.md")) toc.subsubheader("VQA Training Logs") st.info("In case the TensorBoard logs are not displayed, please visit this link: https://huggingface.co/flax-community/multilingual-vqa-pt-60k-ft/tensorboard") st_tensorboard(logdir='./logs/finetune_logs', port=6007) toc.header("Challenges and Technical Difficulties") st.write(read_markdown("challenges.md")) toc.header("Limitations") st.write(read_markdown("limitations.md")) #bias_examples() # 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_work.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("Checkpoints") st.write(read_markdown("checkpoints/checkpoints.md")) toc.subheader("Other Checkpoints") st.write(read_markdown("checkpoints/other_checkpoints.md")) toc.header("Acknowledgements") st.write(read_markdown("acknowledgements.md")) toc.generate()