apps/examples.py

import streamlit as st
from .utils import Toc
def app(state=None):
    toc = Toc()
    st.header("Table of Contents")
    toc.placeholder()

    toc.header("VQA Examples")
    toc.subheader("Color Questions")
    col1, col2, col3 = st.beta_columns([1,1,1])

    col1.image("./sections/examples/men_riding_horses.jpeg", use_column_width="auto", width=300)
    col1.write("**Custom Question**: What color are the horses?")
    col1.write("**Predicted Answer**: brown✅")

    col2.image("./sections/examples/cat_color.jpeg", use_column_width="auto", width=300)
    col2.write("**Custom Question**: What color is the cat?")
    col2.write("**Predicted Answer**: white✅")

    col3.image("./sections/examples/men_happy.jpeg", use_column_width="auto", width=300)
    col3.write("**Custom Question**: What color is the man's jacket?")
    col3.write("**Predicted Answer**: black⚫")

    col1.image("./sections/examples/car_color.jpeg", use_column_width="auto", width=300)
    col1.write("**Actual Question**: What color is the car?")
    col1.write("**Predicted Answer**: blue❎")

    col2.image("./sections/examples/coat_color.jpeg", use_column_width="auto", width=300)
    col2.write("**Actual Question**: What color is this person's coat?")
    col2.write("**Predicted Answer**: blue✅")

    toc.subheader("Counting Questions")

    col1, col2, col3 = st.beta_columns([1,1, 1])

    col1.image("./sections/examples/giraffe_zebra.jpeg", use_column_width="auto", width=300)
    col1.write("**Actual Question**: How many zebras are there?")
    col1.write("**Predicted Answer**: 0❎")

    col2.image("./sections/examples/giraffe_zebra.jpeg", use_column_width="auto", width=300)
    col2.write("**Custom Question**: How many giraffes are there?")
    col2.write("**Predicted Answer**: 2❎")

    col3.image("./sections/examples/teddy.jpeg", use_column_width="auto", width=300)
    col3.write("**Custom Question**: How many teddy bears are present in the image?")
    col3.write("**Predicted Answer**: 3✅")

    col1.image("./sections/examples/candle_count.jpeg", use_column_width="auto", width=300)
    col1.write("**Actual Question**: ¿Cuantas velas hay en el cupcake?")
    col1.write("**English Translation**: How many candles are in the cupcake?")
    col1.write("**Predicted Answer**: 0❎")

    col1.image("./sections/examples/people_picture.jpeg", use_column_width="auto", width=300)
    col1.write("**Actual Question**: ¿A cuánta gente le están tomando una foto?")
    col1.write("**English Translation**: How many people are you taking a picture of?")
    col1.write("**Predicted Answer**: 10❎")

    toc.subheader("Size/Shape Questions")
    col1, col2, col3 = st.beta_columns([1,1,1])
    col1.image("./sections/examples/vase.jpeg", use_column_width="auto", width=300)
    col1.write("**Actual Question**:  What shape is the vase? ")
    col1.write("**Predicted Answer**: round✅")
    

    toc.subheader("Yes/No Questions")
    col1, col2, col3 = st.beta_columns([1,1,1])

    col1.image("./sections/examples/teddy.jpeg", use_column_width="auto", width=300)
    col1.write("**Actual Question**: Sind das drei Teddybären?")
    col1.write("**English Translation**: Are those teddy bears?")
    col1.write("**Predicted Answer**: Ja (yes)✅")

    col2.image("./sections/examples/winter.jpeg", use_column_width="auto", width=300)
    col2.write("**Actual Question**: ¿Se lo tomaron en invierno?")
    col2.write("**English Translation**: Did they take it in winter?")
    col2.write("**Predicted Answer**: si (yes)✅")

    col3.image("./sections/examples/clock.jpeg", use_column_width="auto", width=300)
    col3.write("**Actual Question**: Is the clock ornate? ")
    col3.write("**Predicted Answer**: yes✅")

    col1.image("./sections/examples/decorated_building.jpeg", use_column_width="auto", width=300)
    col1.write("**Actual Question**: Ist das Gebäude orniert?")
    col1.write("**English Translation**: Is the building decorated?")
    col1.write("**Predicted Answer**: Ja (yes)✅")
    
    col2.image("./sections/examples/commuter_train.jpeg", use_column_width="auto", width=300)
    col2.write("**Actual Question**: Ist das ein Pendler-Zug?")
    col2.write("**English Translation**: Is that a commuter train?")
    col2.write("**Predicted Answer**: Ja (yes)❎")

    col3.image("./sections/examples/is_in_a_restaurant.jpeg", use_column_width="auto", width=300)
    col3.write("**Actual Question**: Elle est dans un restaurant?")
    col3.write("**English Translation**: Is she in a restaurant?")
    col3.write("**Predicted Answer**: Oui (yes)❎")

    col1.image("./sections/examples/giraffe_eyes.jpeg", use_column_width="auto", width=300)
    col1.write("**Actual Question**: Est-ce que l'œil de la girafe est fermé?")
    col1.write("**English Translation**: Are the giraffe's eyes closed?")
    col1.write("**Predicted Answer**: Oui (yes)❎")

    toc.subheader("Negatives Test")
    col1, col2, col3 = st.beta_columns([1,1,1])
    col1.image("./sections/examples/men_happy.jpeg", use_column_width="auto", width=300)

    col2.write("**Actual Question**: Is the man happy?")
    col2.write("**Predicted Answer**: Yes✅")

    col3.write("**Actual Question**: Is the man not happy?")
    col3.write("**Predicted Answer**: Yes❎")

    col2.write("**Actual Question**: Is the man sad?")
    col2.write("**Predicted Answer**: No✅")

    col3.write("**Actual Question**: Is the man not sad?")
    col3.write("**Predicted Answer**: No❎")

    col2.write("**Actual Question**: Is the man unhappy?")
    col2.write("**Predicted Answer**: No✅")

    col3.write("**Actual Question**: Is the man not unhappy?")
    col3.write("**Predicted Answer**: No❎")

    toc.subheader("Multilinguality Test")

    toc.subsubheader("Color Question")
    col1, col2, col3 = st.beta_columns([1,1,1])
    col1.image("./sections/examples/truck_color.jpeg", use_column_width="auto", width=300)

    col2.write("**Actual Question**: What color is the building?")
    col2.write("**Predicted Answer**: red✅")

    col3.write("**Actual Question**: Welche Farbe hat das Gebäude?")
    col3.write("**English Translation**: What color is the building?")
    col3.write("**Predicted Answer**: rot (red)✅")

    col2.write("**Actual Question**: ¿De qué color es el edificio?")
    col2.write("**English Translation**: What color is the building?")
    col2.write("**Predicted Answer**: rojo (red)✅")

    col3.write("**Actual Question**: De quelle couleur est le bâtiment ?")
    col3.write("**English Translation**: What color is the building?")
    col3.write("**Predicted Answer**: rouge (red)✅")

    toc.subsubheader("Counting Question")
    col1, col2, col3 = st.beta_columns([1,1,1])
    col1.image("./sections/examples/bear.jpeg", use_column_width="auto", width=300)

    col2.write("**Actual Question**: How many bears do you see?")
    col2.write("**Predicted Answer**: 1✅")

    col3.write("**Actual Question**: Wie viele Bären siehst du?")
    col3.write("**English Translation**: How many bears do you see?")
    col3.write("**Predicted Answer**: 1✅")

    col2.write("**Actual Question**: ¿Cuántos osos ves?")
    col2.write("**English Translation**: How many bears do you see?")
    col2.write("**Predicted Answer**: 1✅")

    col3.write("**Actual Question**: Combien d'ours voyez-vous ?")
    col3.write("**English Translation**: How many bears do you see?")
    col3.write("**Predicted Answer**: 1✅")

    toc.subsubheader("Misc Question")
    col1, col2, col3 = st.beta_columns([1,1,1])
    col1.image("./sections/examples/bench.jpeg", use_column_width="auto", width=300)

    col2.write("**Actual Question**: Where is the bench?")
    col2.write("**Predicted Answer**: field✅")

    col3.write("**Actual Question**: Où est le banc ?")
    col3.write("**English Translation**: Where is the bench?")
    col3.write("**Predicted Answer**: domaine (field)✅")

    col2.write("**Actual Question**: ¿Dónde está el banco?")
    col2.write("**English Translation**: Where is the bench?")
    col2.write("**Predicted Answer**: campo (field)✅")

    col3.write("**Actual Question**: Wo ist die Bank?")
    col3.write("**English Translation**: Where is the bench?")
    col3.write("**Predicted Answer**: Feld (field)✅")


    toc.subheader("Misc Questions")
    col1, col2, col3 = st.beta_columns([1,1,1])

    col1.image("./sections/examples/tennis.jpeg", use_column_width="auto", width=300)
    col1.write("**Actual Question**: ¿Qué clase de juego está viendo la multitud?")
    col1.write("**English Translation**: What kind of game is the crowd watching?")
    col1.write("**Predicted Answer**: tenis (tennis)✅")

    col2.image("./sections/examples/men_body_suits.jpeg", use_column_width="auto", width=300)
    col2.write("**Custom Question**: What are the men wearing?")
    col2.write("**Predicted Answer**: wetsuits✅")

    col3.image("./sections/examples/bathroom.jpeg", use_column_width="auto", width=300)
    col3.write("**Actual Question**: ¿A qué habitación perteneces?")
    col3.write("**English Translation**: What room do you belong to?")
    col3.write("**Predicted Answer**: bano (bathroom)✅")

    col1.image("./sections/examples/men_riding_horses.jpeg", use_column_width="auto", width=300)
    col1.write("**Custom Question**: What are the men riding?")
    col1.write("**Predicted Answer**: horses✅")

    col2.image("./sections/examples/inside_outside.jpeg", use_column_width="auto", width=300)
    col2.write("**Actual Question**: Was this taken inside or outside?")
    col2.write("**Predicted Answer**: inside✅")

    col3.image("./sections/examples/dog_looking_at.jpeg", use_column_width="auto", width=300)
    col3.write("**Actual Question**: Was guckt der Hund denn so?")
    col3.write("**English Translation**: What is the dog looking at?")
    col3.write("**Predicted Answer**: Frisbeescheibe (frisbee)❎")