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import gradio as gr
import random, os
from PIL import Image
import pandas as pd
def open_ims(adj, group):
if group != '':
if adj != '':
if adj[0] in vowels:
dirname='images/'+'Photo_portrait_of_an_'+ adj+'_'+ group.replace(' ','_')+'/Seed_'+ str(seed_choice)+'/'
else:
dirname='images/'+'Photo_portrait_of_a_'+ adj+ '_'+ group.replace(' ','_')+'/Seed_'+ str(seed_choice)+'/'
else:
if group[0] in vowels:
dirname='images/'+'Photo_portrait_of_an_'+ group.replace(' ','_')+'/Seed_'+ str(seed_choice)+'/'
else:
dirname='images/'+'Photo_portrait_of_a_'+ group.replace(' ','_')+'/Seed_'+ str(seed_choice)+'/'
imnames= os.listdir(dirname)
images = [(Image.open(dirname+name)) for name in imnames]
return images[:9]
vowels = ["a","e","i","o","u"]
prompts = pd.read_csv('promptsadjectives.csv')
seeds = [46267, 48040, 51237, 54325, 60884, 64830, 67031, 72935, 92118, 93109]
m_adjectives = prompts['Masc-adj'].tolist()[:11]
f_adjectives = prompts['Fem-adj'].tolist()[:11]
adjectives = sorted(m_adjectives+f_adjectives)
#adjectives = ['attractive','strong']
adjectives.insert(0, '')
professions = sorted([p.lower() for p in prompts['Occupation-Noun'].tolist()])
with gr.Blocks() as demo:
gr.Markdown("# Stable Diffusion Explorer")
gr.Markdown("## Choose from the prompts below to explore how the Stable Diffusion model represents different professions and adjectives")
seed_choice = gr.State(0)
seed_choice = 93109
print("Seed choice is: " + str(seed_choice))
with gr.Row():
with gr.Column():
adj1 = gr.Dropdown(adjectives, label = "Choose a first adjective (or leave this blank!)", interactive=True)
choice1 = gr.Dropdown(professions, label = "Choose a first group", interactive=True)
images1 = gr.Gallery(label="Images").style(grid=[3], height="auto")
with gr.Column():
adj2 = gr.Dropdown(adjectives, label = "Choose a second adjective (or leave this blank!)", interactive=True)
choice2 = gr.Dropdown(professions, label = "Choose a second group", interactive=True)
images2 = gr.Gallery(label="Images").style(grid=[3], height="auto")
gr.Markdown("### [Research](http://gender-decoder.katmatfield.com/static/documents/Gaucher-Friesen-Kay-JPSP-Gendered-Wording-in-Job-ads.pdf) has shown that \
certain words are considered more masculine- or feminine-coded based on how appealing job descriptions containing these words \
seemed to male and female research participants and to what extent the participants felt that they 'belonged' in that occupation.")
#demo.load(random_image, None, [images])
choice1.change(open_ims, [adj1,choice1], [images1])
choice2.change(open_ims, [adj2,choice2], [images2])
adj1.change(open_ims, [adj1,choice1], [images1])
adj2.change(open_ims, [adj2,choice2], [images2])
demo.launch()
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