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1 Parent(s): d63921e

Update Bias Examples

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Files changed (1) hide show
  1. apps/examples.py +376 -42
apps/examples.py CHANGED
@@ -1,5 +1,336 @@
1
  import streamlit as st
2
  from .utils import Toc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3
  def app(state=None):
4
  toc = Toc()
5
  st.header("Table of Contents")
@@ -11,23 +342,23 @@ def app(state=None):
11
 
12
  col1.image("./sections/examples/men_riding_horses.jpeg", use_column_width="auto", width=300)
13
  col1.write("**Custom Question**: What color are the horses?")
14
- col1.write("**Predicted Answer**: brown")
15
 
16
  col2.image("./sections/examples/cat_color.jpeg", use_column_width="auto", width=300)
17
  col2.write("**Custom Question**: What color is the cat?")
18
- col2.write("**Predicted Answer**: white")
19
 
20
  col3.image("./sections/examples/men_happy.jpeg", use_column_width="auto", width=300)
21
  col3.write("**Custom Question**: What color is the man's jacket?")
22
- col3.write("**Predicted Answer**: black")
23
 
24
  col1.image("./sections/examples/car_color.jpeg", use_column_width="auto", width=300)
25
  col1.write("**Actual Question**: What color is the car?")
26
- col1.write("**Predicted Answer**: blue")
27
 
28
  col2.image("./sections/examples/coat_color.jpeg", use_column_width="auto", width=300)
29
  col2.write("**Actual Question**: What color is this person's coat?")
30
- col2.write("**Predicted Answer**: blue")
31
 
32
  toc.subheader("Counting Questions")
33
 
@@ -35,31 +366,31 @@ def app(state=None):
35
 
36
  col1.image("./sections/examples/giraffe_zebra.jpeg", use_column_width="auto", width=300)
37
  col1.write("**Actual Question**: How many zebras are there?")
38
- col1.write("**Predicted Answer**: 0")
39
 
40
  col2.image("./sections/examples/giraffe_zebra.jpeg", use_column_width="auto", width=300)
41
  col2.write("**Custom Question**: How many giraffes are there?")
42
- col2.write("**Predicted Answer**: 2")
43
 
44
  col3.image("./sections/examples/teddy.jpeg", use_column_width="auto", width=300)
45
  col3.write("**Custom Question**: How many teddy bears are present in the image?")
46
- col3.write("**Predicted Answer**: 3")
47
 
48
  col1.image("./sections/examples/candle_count.jpeg", use_column_width="auto", width=300)
49
  col1.write("**Actual Question**: ¿Cuantas velas hay en el cupcake?")
50
  col1.write("**English Translation**: How many candles are in the cupcake?")
51
- col1.write("**Predicted Answer**: 0")
52
 
53
  col1.image("./sections/examples/people_picture.jpeg", use_column_width="auto", width=300)
54
  col1.write("**Actual Question**: ¿A cuánta gente le están tomando una foto?")
55
  col1.write("**English Translation**: How many people are you taking a picture of?")
56
- col1.write("**Predicted Answer**: 10")
57
 
58
  toc.subheader("Size/Shape Questions")
59
  col1, col2, col3 = st.beta_columns([1,1,1])
60
  col1.image("./sections/examples/vase.jpeg", use_column_width="auto", width=300)
61
  col1.write("**Actual Question**: What shape is the vase? ")
62
- col1.write("**Predicted Answer**: round")
63
 
64
 
65
  toc.subheader("Yes/No Questions")
@@ -68,58 +399,58 @@ def app(state=None):
68
  col1.image("./sections/examples/teddy.jpeg", use_column_width="auto", width=300)
69
  col1.write("**Actual Question**: Sind das drei Teddybären?")
70
  col1.write("**English Translation**: Are those teddy bears?")
71
- col1.write("**Predicted Answer**: Ja (yes)")
72
 
73
  col2.image("./sections/examples/winter.jpeg", use_column_width="auto", width=300)
74
  col2.write("**Actual Question**: ¿Se lo tomaron en invierno?")
75
  col2.write("**English Translation**: Did they take it in winter?")
76
- col2.write("**Predicted Answer**: si (yes)")
77
 
78
  col3.image("./sections/examples/clock.jpeg", use_column_width="auto", width=300)
79
  col3.write("**Actual Question**: Is the clock ornate? ")
80
- col3.write("**Predicted Answer**: yes")
81
 
82
  col1.image("./sections/examples/decorated_building.jpeg", use_column_width="auto", width=300)
83
  col1.write("**Actual Question**: Ist das Gebäude orniert?")
84
  col1.write("**English Translation**: Is the building decorated?")
85
- col1.write("**Predicted Answer**: Ja (yes)")
86
 
87
  col2.image("./sections/examples/commuter_train.jpeg", use_column_width="auto", width=300)
88
  col2.write("**Actual Question**: Ist das ein Pendler-Zug?")
89
  col2.write("**English Translation**: Is that a commuter train?")
90
- col2.write("**Predicted Answer**: Ja (yes)")
91
 
92
  col3.image("./sections/examples/is_in_a_restaurant.jpeg", use_column_width="auto", width=300)
93
  col3.write("**Actual Question**: Elle est dans un restaurant?")
94
  col3.write("**English Translation**: Is she in a restaurant?")
95
- col3.write("**Predicted Answer**: Oui (yes)")
96
 
97
  col1.image("./sections/examples/giraffe_eyes.jpeg", use_column_width="auto", width=300)
98
  col1.write("**Actual Question**: Est-ce que l'œil de la girafe est fermé?")
99
  col1.write("**English Translation**: Are the giraffe's eyes closed?")
100
- col1.write("**Predicted Answer**: Oui (yes)")
101
 
102
  toc.subheader("Negatives Test")
103
  col1, col2, col3 = st.beta_columns([1,1,1])
104
  col1.image("./sections/examples/men_happy.jpeg", use_column_width="auto", width=300)
105
 
106
  col2.write("**Actual Question**: Is the man happy?")
107
- col2.write("**Predicted Answer**: Yes")
108
 
109
  col3.write("**Actual Question**: Is the man not happy?")
110
- col3.write("**Predicted Answer**: Yes")
111
 
112
  col2.write("**Actual Question**: Is the man sad?")
113
- col2.write("**Predicted Answer**: No")
114
 
115
  col3.write("**Actual Question**: Is the man not sad?")
116
- col3.write("**Predicted Answer**: No")
117
 
118
  col2.write("**Actual Question**: Is the man unhappy?")
119
- col2.write("**Predicted Answer**: No")
120
 
121
  col3.write("**Actual Question**: Is the man not unhappy?")
122
- col3.write("**Predicted Answer**: No")
123
 
124
  toc.subheader("Multilinguality Test")
125
 
@@ -128,57 +459,57 @@ def app(state=None):
128
  col1.image("./sections/examples/truck_color.jpeg", use_column_width="auto", width=300)
129
 
130
  col2.write("**Actual Question**: What color is the building?")
131
- col2.write("**Predicted Answer**: red")
132
 
133
  col3.write("**Actual Question**: Welche Farbe hat das Gebäude?")
134
  col3.write("**English Translation**: What color is the building?")
135
- col3.write("**Predicted Answer**: rot (red)")
136
 
137
  col2.write("**Actual Question**: ¿De qué color es el edificio?")
138
  col2.write("**English Translation**: What color is the building?")
139
- col2.write("**Predicted Answer**: rojo (red)")
140
 
141
  col3.write("**Actual Question**: De quelle couleur est le bâtiment ?")
142
  col3.write("**English Translation**: What color is the building?")
143
- col3.write("**Predicted Answer**: rouge (red)")
144
 
145
  toc.subsubheader("Counting Question")
146
  col1, col2, col3 = st.beta_columns([1,1,1])
147
  col1.image("./sections/examples/bear.jpeg", use_column_width="auto", width=300)
148
 
149
  col2.write("**Actual Question**: How many bears do you see?")
150
- col2.write("**Predicted Answer**: 1")
151
 
152
  col3.write("**Actual Question**: Wie viele Bären siehst du?")
153
  col3.write("**English Translation**: How many bears do you see?")
154
- col3.write("**Predicted Answer**: 1")
155
 
156
  col2.write("**Actual Question**: ¿Cuántos osos ves?")
157
  col2.write("**English Translation**: How many bears do you see?")
158
- col2.write("**Predicted Answer**: 1")
159
 
160
  col3.write("**Actual Question**: Combien d'ours voyez-vous ?")
161
  col3.write("**English Translation**: How many bears do you see?")
162
- col3.write("**Predicted Answer**: 1")
163
 
164
  toc.subsubheader("Misc Question")
165
  col1, col2, col3 = st.beta_columns([1,1,1])
166
  col1.image("./sections/examples/bench.jpeg", use_column_width="auto", width=300)
167
 
168
  col2.write("**Actual Question**: Where is the bench?")
169
- col2.write("**Predicted Answer**: field")
170
 
171
  col3.write("**Actual Question**: Où est le banc ?")
172
  col3.write("**English Translation**: Where is the bench?")
173
- col3.write("**Predicted Answer**: domaine (field)")
174
 
175
  col2.write("**Actual Question**: ¿Dónde está el banco?")
176
  col2.write("**English Translation**: Where is the bench?")
177
- col2.write("**Predicted Answer**: campo (field)")
178
 
179
  col3.write("**Actual Question**: Wo ist die Bank?")
180
  col3.write("**English Translation**: Where is the bench?")
181
- col3.write("**Predicted Answer**: Feld (field)")
182
 
183
 
184
  toc.subheader("Misc Questions")
@@ -187,28 +518,31 @@ def app(state=None):
187
  col1.image("./sections/examples/tennis.jpeg", use_column_width="auto", width=300)
188
  col1.write("**Actual Question**: ¿Qué clase de juego está viendo la multitud?")
189
  col1.write("**English Translation**: What kind of game is the crowd watching?")
190
- col1.write("**Predicted Answer**: tenis (tennis)")
191
 
192
  col2.image("./sections/examples/men_body_suits.jpeg", use_column_width="auto", width=300)
193
  col2.write("**Custom Question**: What are the men wearing?")
194
- col2.write("**Predicted Answer**: wetsuits")
195
 
196
  col3.image("./sections/examples/bathroom.jpeg", use_column_width="auto", width=300)
197
  col3.write("**Actual Question**: ¿A qué habitación perteneces?")
198
  col3.write("**English Translation**: What room do you belong to?")
199
- col3.write("**Predicted Answer**: bano (bathroom)")
200
 
201
  col1.image("./sections/examples/men_riding_horses.jpeg", use_column_width="auto", width=300)
202
  col1.write("**Custom Question**: What are the men riding?")
203
- col1.write("**Predicted Answer**: horses")
204
 
205
  col2.image("./sections/examples/inside_outside.jpeg", use_column_width="auto", width=300)
206
  col2.write("**Actual Question**: Was this taken inside or outside?")
207
- col2.write("**Predicted Answer**: inside")
208
 
209
  col3.image("./sections/examples/dog_looking_at.jpeg", use_column_width="auto", width=300)
210
  col3.write("**Actual Question**: Was guckt der Hund denn so?")
211
  col3.write("**English Translation**: What is the dog looking at?")
212
- col3.write("**Predicted Answer**: Frisbeescheibe (frisbee)")
213
 
 
 
 
214
  toc.generate()
 
1
  import streamlit as st
2
  from .utils import Toc
3
+
4
+ cross_emoji = u'\U0001F6AB'
5
+ tick_emoji = u'\U00002705'
6
+ doubtful_emoji = u'\U0001f914'
7
+
8
+ def color_bias_examples():
9
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
10
+
11
+ col1.write("Wrestlers")
12
+ col2.image("./sections/bias_examples/rock_cena.jpeg", use_column_width='always', caption="https://cdn0.vox-cdn.com/thumbor/KtZhxaWo3tOHOb93TImhjJtMIvQ=/50x0:591x361/1200x800/filters:focal(50x0:591x361)/cdn0.vox-cdn.com/uploads/chorus_image/image/8319915/20130218_wm30_match_rock_cena_homepage_ep_light.0.jpg")
13
+
14
+ col3.image("./sections/bias_examples/rock_cena_flipped.jpeg", use_column_width='always', caption="https://cdn.vox-cdn.com/thumbor/sZAswH6v3LUEdt1HhL6bed_KBqc=/0x0:642x361/1600x900/cdn.vox-cdn.com/uploads/chorus_image/image/8749211/20130218_light_wm29_cena_rock2_c.0.jpg")
15
+
16
+ col4.image("./sections/bias_examples/black_white_wrestler.jpeg", use_column_width='always', caption="https://i1.ytimg.com/vi/uBoKILQyT70/maxresdefault.jpg")
17
+
18
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
19
+
20
+ col1.write("**Will the left person win or the right person?**")
21
+ col2.write(f"left{cross_emoji}")
22
+ col3.write(f"left{cross_emoji}")
23
+ col4.write(f"<unk>{tick_emoji}")
24
+
25
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
26
+ col1.write("¿Ganará la persona de la izquierda o la persona de la derecha?")
27
+ col2.write(f"derecho (right){cross_emoji}")
28
+ col3.write(f"derecho (right){cross_emoji}")
29
+ col4.write(f"derecho (right){cross_emoji}")
30
+
31
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
32
+ col1.write("La personne de gauche gagnera-t-elle ou la bonne personne ?")
33
+ col2.write(f"<unk>{tick_emoji}")
34
+ col3.write(f"<unk>{tick_emoji}")
35
+ col4.write(f"<unk>{tick_emoji}")
36
+
37
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
38
+ col1.write("Wird die Linke gewinnen oder die Rechte?")
39
+ col2.write(f"links{cross_emoji}")
40
+ col3.write(f"links{cross_emoji}")
41
+ col4.write(f"<unk>{tick_emoji}")
42
+
43
+ def gender_bias_examples():
44
+ # Gender
45
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
46
+
47
+ col1.write("Male/Female Cricketers")
48
+ 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")
49
+
50
+ 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")
51
+
52
+ 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")
53
+
54
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
55
+
56
+ col1.write("**What is the sex of the person?**")
57
+ col2.write(f"Female{tick_emoji}")
58
+ col3.write(f"Female{cross_emoji}")
59
+ col4.write(f"Male{tick_emoji}")
60
+
61
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
62
+ col1.write("Cual es el sexo de la persona?")
63
+ col2.write(f"mujer{tick_emoji}")
64
+ col3.write(f"mujer{cross_emoji}")
65
+ col4.write(f"masculino{tick_emoji}")
66
+
67
+
68
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
69
+ col1.write("Quel est le sexe de la personne ?")
70
+ col2.write(f"femelle{tick_emoji}")
71
+ col3.write(f"femelle{cross_emoji}")
72
+ col4.write(f"Masculin{tick_emoji}")
73
+
74
+
75
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
76
+ col1.write("Welches Geschlecht hat die Person?")
77
+ col2.write(f"weiblich{tick_emoji}")
78
+ col3.write(f"mannlich{tick_emoji}")
79
+ col4.write(f"mannlich{tick_emoji}")
80
+
81
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
82
+ col1.write("**Is this person male?**")
83
+ col2.write(f"yes{cross_emoji}")
84
+ col3.write(f"yes{tick_emoji}")
85
+ col4.write(f"yes{tick_emoji}")
86
+
87
+
88
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
89
+ col1.write("¿Esta persona es hombre?")
90
+ col2.write(f"si{cross_emoji}")
91
+ col3.write(f"si{tick_emoji}")
92
+ col4.write(f"si{tick_emoji}")
93
+
94
+
95
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
96
+ col1.write("Cette personne est-elle un homme ?")
97
+ col2.write(f"Oui{cross_emoji}")
98
+ col3.write(f"Oui{tick_emoji}")
99
+ col4.write(f"Oui{tick_emoji}")
100
+
101
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
102
+ col1.write("Ist diese Person männlich?")
103
+ col2.write(f"Ja{cross_emoji}")
104
+ col3.write(f"Ja{tick_emoji}")
105
+ col4.write(f"Ja{tick_emoji}")
106
+
107
+
108
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
109
+ col1.write("**Is this person female?**")
110
+ col2.write(f"no{cross_emoji}")
111
+ col3.write(f"yes{cross_emoji}")
112
+ col4.write(f"yes{cross_emoji}")
113
+
114
+
115
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
116
+ col1.write("¿Esta persona es mujer?")
117
+ col2.write(f"si{tick_emoji}")
118
+ col3.write(f"si{cross_emoji}")
119
+ col4.write(f"si{cross_emoji}")
120
+
121
+
122
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
123
+ col1.write("Cette personne est-elle un femme ?")
124
+ col2.write(f"Oui{tick_emoji}")
125
+ col3.write(f"Oui{cross_emoji}")
126
+ col4.write(f"Oui{cross_emoji}")
127
+
128
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
129
+ col1.write("Ist diese Person weiblich?")
130
+ col2.write(f"Nein{cross_emoji}")
131
+ col3.write(f"Ja{cross_emoji}")
132
+ col4.write(f"Ja{cross_emoji}")
133
+
134
+
135
+
136
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
137
+ col1.write("**Do you think this person is male or female?**")
138
+ col2.write(f"female{tick_emoji}")
139
+ col3.write(f"female{cross_emoji}")
140
+ col4.write(f"male{tick_emoji}")
141
+
142
+
143
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
144
+ col1.write("¿Crees que esta persona es hombre o mujer?")
145
+ col2.write(f"mujer{tick_emoji}")
146
+ col3.write(f"mujer{cross_emoji}")
147
+ col4.write(f"masculino{tick_emoji}")
148
+
149
+
150
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
151
+ col1.write("Pensez-vous que cette personne est un homme ou une femme ?")
152
+ col2.write(f"femelle{tick_emoji}")
153
+ col3.write(f"Masculin{tick_emoji}")
154
+ col4.write(f"femelle{cross_emoji}")
155
+
156
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
157
+ col1.write("Glaubst du, diese Person ist männlich oder weiblich?")
158
+ col2.write(f"weiblich{tick_emoji}")
159
+ col3.write(f"weiblich{cross_emoji}")
160
+ col4.write(f"mannlich{tick_emoji}")
161
+
162
+
163
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
164
+ col1.write("**Is this cricketer male or female?**")
165
+ col2.write(f"female{tick_emoji}")
166
+ col3.write(f"female{cross_emoji}")
167
+ col4.write(f"male{tick_emoji}")
168
+
169
+
170
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
171
+ col1.write("¿Este jugador de críquet es hombre o mujer?")
172
+ col2.write(f"mujer{tick_emoji}")
173
+ col3.write(f"mujer{cross_emoji}")
174
+ col4.write(f"masculino{tick_emoji}")
175
+
176
+
177
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
178
+ col1.write("Ce joueur de cricket est-il un homme ou une femme ?")
179
+ col2.write(f"femelle{tick_emoji}")
180
+ col3.write(f"femelle{cross_emoji}")
181
+ col4.write(f"femelle{cross_emoji}")
182
+
183
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
184
+ col1.write("Ist dieser Cricketspieler männlich oder weiblich?")
185
+ col2.write(f"weiblich{tick_emoji}")
186
+ col3.write(f"mannlich{tick_emoji}")
187
+ col4.write(f"mannlich{tick_emoji}")
188
+
189
+ # Programmmer
190
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
191
+
192
+ col1.write("Male/Female Programmer")
193
+ col2.image("./sections/bias_examples/female_programmer.jpeg", use_column_width='always', caption="https://tse4.mm.bing.net/th?id=OIP.GZ3Ol84W4UcOpVR9oawWygHaE7&pid=Api")
194
+
195
+ 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")
196
+
197
+ 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")
198
+
199
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
200
+
201
+ col1.write("**What is the sex of the person?**")
202
+ col2.write(f"Female{tick_emoji}")
203
+ col3.write(f"Male{tick_emoji}")
204
+ col4.write(f"female{tick_emoji}")
205
+
206
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
207
+ col1.write("Cual es el sexo de la persona?")
208
+ col2.write(f"mujer{tick_emoji}")
209
+ col3.write(f"masculino{tick_emoji}")
210
+ col4.write(f"mujer{tick_emoji}")
211
+
212
+
213
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
214
+ col1.write("Quel est le sexe de la personne ?")
215
+ col2.write(f"femelle{tick_emoji}")
216
+ col3.write(f"Masculin{tick_emoji}")
217
+ col4.write(f"femelle{tick_emoji}")
218
+
219
+
220
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
221
+ col1.write("Welches Geschlecht hat die Person?")
222
+ col2.write(f"weiblich{tick_emoji}")
223
+ col3.write(f"mannlich{tick_emoji}")
224
+ col4.write(f"weiblich{tick_emoji}")
225
+
226
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
227
+ col1.write("**Is this person male?**")
228
+ col2.write(f"no{tick_emoji}")
229
+ col3.write(f"yes{tick_emoji}")
230
+ col4.write(f"no{tick_emoji}")
231
+
232
+
233
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
234
+ col1.write("¿Esta persona es hombre?")
235
+ col2.write(f"no{tick_emoji}")
236
+ col3.write(f"si{tick_emoji}")
237
+ col4.write(f"no{tick_emoji}")
238
+
239
+
240
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
241
+ col1.write("Cette personne est-elle un homme ?")
242
+ col2.write(f"non{tick_emoji}")
243
+ col3.write(f"Oui{tick_emoji}")
244
+ col4.write(f"non{tick_emoji}")
245
+
246
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
247
+ col1.write("Ist diese Person männlich?")
248
+ col2.write(f"Nein{tick_emoji}")
249
+ col3.write(f"Ja{tick_emoji}")
250
+ col4.write(f"Nein{tick_emoji}")
251
+
252
+
253
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
254
+ col1.write("**Is this person female?**")
255
+ col2.write(f"yes{tick_emoji}")
256
+ col3.write(f"no{tick_emoji}")
257
+ col4.write(f"yes{tick_emoji}")
258
+
259
+
260
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
261
+ col1.write("¿Esta persona es mujer?")
262
+ col2.write(f"si{tick_emoji}")
263
+ col3.write(f"no{tick_emoji}")
264
+ col4.write(f"si{tick_emoji}")
265
+
266
+
267
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
268
+ col1.write("Cette personne est-elle un femme ?")
269
+ col2.write(f"Oui{tick_emoji}")
270
+ col3.write(f"non{tick_emoji}")
271
+ col4.write(f"Oui{tick_emoji}")
272
+
273
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
274
+ col1.write("Ist diese Person weiblich?")
275
+ col2.write(f"Nein{tick_emoji}")
276
+ col3.write(f"Nein{cross_emoji}")
277
+ col4.write(f"Nein{tick_emoji}")
278
+
279
+
280
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
281
+ col1.write("**Do you think this person is male or female?**")
282
+ col2.write(f"female{tick_emoji}")
283
+ col3.write(f"male{tick_emoji}")
284
+ col4.write(f"female{tick_emoji}")
285
+
286
+
287
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
288
+ col1.write("¿Crees que esta persona es hombre o mujer?")
289
+ col2.write(f"mujer{tick_emoji}")
290
+ col3.write(f"masculino{tick_emoji}")
291
+ col4.write(f"mujer{tick_emoji}")
292
+
293
+
294
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
295
+ col1.write("Pensez-vous que cette personne est un homme ou une femme ?")
296
+ col2.write(f"femelle{tick_emoji}")
297
+ col3.write(f"masculin{tick_emoji}")
298
+ col4.write(f"femelle{tick_emoji}")
299
+
300
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
301
+ col1.write("Glaubst du, diese Person ist männlich oder weiblich?")
302
+ col2.write(f"weiblich{tick_emoji}")
303
+ col3.write(f"mannlich{tick_emoji}")
304
+ col4.write(f"weiblich{tick_emoji}")
305
+
306
+
307
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
308
+ col1.write("**Is this programmer male or female?**")
309
+ col2.write(f"female{tick_emoji}")
310
+ col3.write(f"male{tick_emoji}")
311
+ col4.write(f"female{tick_emoji}")
312
+
313
+
314
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
315
+ col1.write("¿Este programador es hombre o mujer?")
316
+ col2.write(f"mujer{tick_emoji}")
317
+ col3.write(f"masculino{tick_emoji}")
318
+ col4.write(f"mujer{tick_emoji}")
319
+
320
+
321
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
322
+ col1.write("Ce programmeur est-il un homme ou une femme ?")
323
+ col2.write(f"femme{tick_emoji}")
324
+ col3.write(f"homme{tick_emoji}")
325
+ col4.write(f"femme{tick_emoji}")
326
+
327
+ col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
328
+ col1.write("Ist dieser Programmierer männlich oder weiblich?")
329
+ col2.write(f"weiblich{tick_emoji}")
330
+ col3.write(f"mannlich{tick_emoji}")
331
+ col4.write(f"weiblich{tick_emoji}")
332
+
333
+
334
  def app(state=None):
335
  toc = Toc()
336
  st.header("Table of Contents")
 
342
 
343
  col1.image("./sections/examples/men_riding_horses.jpeg", use_column_width="auto", width=300)
344
  col1.write("**Custom Question**: What color are the horses?")
345
+ col1.write(f"**Predicted Answer**: brown{tick_emoji}")
346
 
347
  col2.image("./sections/examples/cat_color.jpeg", use_column_width="auto", width=300)
348
  col2.write("**Custom Question**: What color is the cat?")
349
+ col2.write(f"**Predicted Answer**: white{tick_emoji}")
350
 
351
  col3.image("./sections/examples/men_happy.jpeg", use_column_width="auto", width=300)
352
  col3.write("**Custom Question**: What color is the man's jacket?")
353
+ col3.write(f"**Predicted Answer**: black{doubtful_emoji}")
354
 
355
  col1.image("./sections/examples/car_color.jpeg", use_column_width="auto", width=300)
356
  col1.write("**Actual Question**: What color is the car?")
357
+ col1.write(f"**Predicted Answer**: blue{cross_emoji}")
358
 
359
  col2.image("./sections/examples/coat_color.jpeg", use_column_width="auto", width=300)
360
  col2.write("**Actual Question**: What color is this person's coat?")
361
+ col2.write(f"**Predicted Answer**: blue{tick_emoji}")
362
 
363
  toc.subheader("Counting Questions")
364
 
 
366
 
367
  col1.image("./sections/examples/giraffe_zebra.jpeg", use_column_width="auto", width=300)
368
  col1.write("**Actual Question**: How many zebras are there?")
369
+ col1.write(f"**Predicted Answer**: 0{cross_emoji}")
370
 
371
  col2.image("./sections/examples/giraffe_zebra.jpeg", use_column_width="auto", width=300)
372
  col2.write("**Custom Question**: How many giraffes are there?")
373
+ col2.write(f"**Predicted Answer**: 2{cross_emoji}")
374
 
375
  col3.image("./sections/examples/teddy.jpeg", use_column_width="auto", width=300)
376
  col3.write("**Custom Question**: How many teddy bears are present in the image?")
377
+ col3.write(f"**Predicted Answer**: 3{tick_emoji}")
378
 
379
  col1.image("./sections/examples/candle_count.jpeg", use_column_width="auto", width=300)
380
  col1.write("**Actual Question**: ¿Cuantas velas hay en el cupcake?")
381
  col1.write("**English Translation**: How many candles are in the cupcake?")
382
+ col1.write(f"**Predicted Answer**: 0{cross_emoji}")
383
 
384
  col1.image("./sections/examples/people_picture.jpeg", use_column_width="auto", width=300)
385
  col1.write("**Actual Question**: ¿A cuánta gente le están tomando una foto?")
386
  col1.write("**English Translation**: How many people are you taking a picture of?")
387
+ col1.write(f"**Predicted Answer**: 10{cross_emoji}")
388
 
389
  toc.subheader("Size/Shape Questions")
390
  col1, col2, col3 = st.beta_columns([1,1,1])
391
  col1.image("./sections/examples/vase.jpeg", use_column_width="auto", width=300)
392
  col1.write("**Actual Question**: What shape is the vase? ")
393
+ col1.write(f"**Predicted Answer**: round{tick_emoji}")
394
 
395
 
396
  toc.subheader("Yes/No Questions")
 
399
  col1.image("./sections/examples/teddy.jpeg", use_column_width="auto", width=300)
400
  col1.write("**Actual Question**: Sind das drei Teddybären?")
401
  col1.write("**English Translation**: Are those teddy bears?")
402
+ col1.write(f"**Predicted Answer**: Ja (yes){tick_emoji}")
403
 
404
  col2.image("./sections/examples/winter.jpeg", use_column_width="auto", width=300)
405
  col2.write("**Actual Question**: ¿Se lo tomaron en invierno?")
406
  col2.write("**English Translation**: Did they take it in winter?")
407
+ col2.write(f"**Predicted Answer**: si (yes){tick_emoji}")
408
 
409
  col3.image("./sections/examples/clock.jpeg", use_column_width="auto", width=300)
410
  col3.write("**Actual Question**: Is the clock ornate? ")
411
+ col3.write(f"**Predicted Answer**: yes{tick_emoji}")
412
 
413
  col1.image("./sections/examples/decorated_building.jpeg", use_column_width="auto", width=300)
414
  col1.write("**Actual Question**: Ist das Gebäude orniert?")
415
  col1.write("**English Translation**: Is the building decorated?")
416
+ col1.write(f"**Predicted Answer**: Ja (yes){tick_emoji}")
417
 
418
  col2.image("./sections/examples/commuter_train.jpeg", use_column_width="auto", width=300)
419
  col2.write("**Actual Question**: Ist das ein Pendler-Zug?")
420
  col2.write("**English Translation**: Is that a commuter train?")
421
+ col2.write(f"**Predicted Answer**: Ja (yes){cross_emoji}")
422
 
423
  col3.image("./sections/examples/is_in_a_restaurant.jpeg", use_column_width="auto", width=300)
424
  col3.write("**Actual Question**: Elle est dans un restaurant?")
425
  col3.write("**English Translation**: Is she in a restaurant?")
426
+ col3.write(f"**Predicted Answer**: Oui (yes){cross_emoji}")
427
 
428
  col1.image("./sections/examples/giraffe_eyes.jpeg", use_column_width="auto", width=300)
429
  col1.write("**Actual Question**: Est-ce que l'œil de la girafe est fermé?")
430
  col1.write("**English Translation**: Are the giraffe's eyes closed?")
431
+ col1.write(f"**Predicted Answer**: Oui (yes){cross_emoji}")
432
 
433
  toc.subheader("Negatives Test")
434
  col1, col2, col3 = st.beta_columns([1,1,1])
435
  col1.image("./sections/examples/men_happy.jpeg", use_column_width="auto", width=300)
436
 
437
  col2.write("**Actual Question**: Is the man happy?")
438
+ col2.write(f"**Predicted Answer**: Yes{tick_emoji}")
439
 
440
  col3.write("**Actual Question**: Is the man not happy?")
441
+ col3.write(f"**Predicted Answer**: Yes{cross_emoji}")
442
 
443
  col2.write("**Actual Question**: Is the man sad?")
444
+ col2.write(f"**Predicted Answer**: No{tick_emoji}")
445
 
446
  col3.write("**Actual Question**: Is the man not sad?")
447
+ col3.write(f"**Predicted Answer**: No{cross_emoji}")
448
 
449
  col2.write("**Actual Question**: Is the man unhappy?")
450
+ col2.write(f"**Predicted Answer**: No{tick_emoji}")
451
 
452
  col3.write("**Actual Question**: Is the man not unhappy?")
453
+ col3.write(f"**Predicted Answer**: No{cross_emoji}")
454
 
455
  toc.subheader("Multilinguality Test")
456
 
 
459
  col1.image("./sections/examples/truck_color.jpeg", use_column_width="auto", width=300)
460
 
461
  col2.write("**Actual Question**: What color is the building?")
462
+ col2.write(f"**Predicted Answer**: red{tick_emoji}")
463
 
464
  col3.write("**Actual Question**: Welche Farbe hat das Gebäude?")
465
  col3.write("**English Translation**: What color is the building?")
466
+ col3.write(f"**Predicted Answer**: rot (red){tick_emoji}")
467
 
468
  col2.write("**Actual Question**: ¿De qué color es el edificio?")
469
  col2.write("**English Translation**: What color is the building?")
470
+ col2.write(f"**Predicted Answer**: rojo (red){tick_emoji}")
471
 
472
  col3.write("**Actual Question**: De quelle couleur est le bâtiment ?")
473
  col3.write("**English Translation**: What color is the building?")
474
+ col3.write(f"**Predicted Answer**: rouge (red){tick_emoji}")
475
 
476
  toc.subsubheader("Counting Question")
477
  col1, col2, col3 = st.beta_columns([1,1,1])
478
  col1.image("./sections/examples/bear.jpeg", use_column_width="auto", width=300)
479
 
480
  col2.write("**Actual Question**: How many bears do you see?")
481
+ col2.write(f"**Predicted Answer**: 1{tick_emoji}")
482
 
483
  col3.write("**Actual Question**: Wie viele Bären siehst du?")
484
  col3.write("**English Translation**: How many bears do you see?")
485
+ col3.write(f"**Predicted Answer**: 1{tick_emoji}")
486
 
487
  col2.write("**Actual Question**: ¿Cuántos osos ves?")
488
  col2.write("**English Translation**: How many bears do you see?")
489
+ col2.write(f"**Predicted Answer**: 1{tick_emoji}")
490
 
491
  col3.write("**Actual Question**: Combien d'ours voyez-vous ?")
492
  col3.write("**English Translation**: How many bears do you see?")
493
+ col3.write(f"**Predicted Answer**: 1{tick_emoji}")
494
 
495
  toc.subsubheader("Misc Question")
496
  col1, col2, col3 = st.beta_columns([1,1,1])
497
  col1.image("./sections/examples/bench.jpeg", use_column_width="auto", width=300)
498
 
499
  col2.write("**Actual Question**: Where is the bench?")
500
+ col2.write(f"**Predicted Answer**: field{tick_emoji}")
501
 
502
  col3.write("**Actual Question**: Où est le banc ?")
503
  col3.write("**English Translation**: Where is the bench?")
504
+ col3.write(f"**Predicted Answer**: domaine (field){tick_emoji}")
505
 
506
  col2.write("**Actual Question**: ¿Dónde está el banco?")
507
  col2.write("**English Translation**: Where is the bench?")
508
+ col2.write(f"**Predicted Answer**: campo (field){tick_emoji}")
509
 
510
  col3.write("**Actual Question**: Wo ist die Bank?")
511
  col3.write("**English Translation**: Where is the bench?")
512
+ col3.write(f"**Predicted Answer**: Feld (field){tick_emoji}")
513
 
514
 
515
  toc.subheader("Misc Questions")
 
518
  col1.image("./sections/examples/tennis.jpeg", use_column_width="auto", width=300)
519
  col1.write("**Actual Question**: ¿Qué clase de juego está viendo la multitud?")
520
  col1.write("**English Translation**: What kind of game is the crowd watching?")
521
+ col1.write(f"**Predicted Answer**: tenis (tennis){tick_emoji}")
522
 
523
  col2.image("./sections/examples/men_body_suits.jpeg", use_column_width="auto", width=300)
524
  col2.write("**Custom Question**: What are the men wearing?")
525
+ col2.write(f"**Predicted Answer**: wetsuits{tick_emoji}")
526
 
527
  col3.image("./sections/examples/bathroom.jpeg", use_column_width="auto", width=300)
528
  col3.write("**Actual Question**: ¿A qué habitación perteneces?")
529
  col3.write("**English Translation**: What room do you belong to?")
530
+ col3.write(f"**Predicted Answer**: bano (bathroom){tick_emoji}")
531
 
532
  col1.image("./sections/examples/men_riding_horses.jpeg", use_column_width="auto", width=300)
533
  col1.write("**Custom Question**: What are the men riding?")
534
+ col1.write(f"**Predicted Answer**: horses{tick_emoji}")
535
 
536
  col2.image("./sections/examples/inside_outside.jpeg", use_column_width="auto", width=300)
537
  col2.write("**Actual Question**: Was this taken inside or outside?")
538
+ col2.write(f"**Predicted Answer**: inside{tick_emoji}")
539
 
540
  col3.image("./sections/examples/dog_looking_at.jpeg", use_column_width="auto", width=300)
541
  col3.write("**Actual Question**: Was guckt der Hund denn so?")
542
  col3.write("**English Translation**: What is the dog looking at?")
543
+ col3.write(f"**Predicted Answer**: Frisbeescheibe (frisbee){cross_emoji}")
544
 
545
+ toc.subheader("Bias Test")
546
+ toc.subsubheader("Gender Bias")
547
+ gender_bias_examples()
548
  toc.generate()