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Browse files- README.md +5 -5
- app.py +101 -0
- promptsadjectives.csv +151 -0
- requirements.txt +2 -0
README.md
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---
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title:
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emoji:
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colorFrom:
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sdk: gradio
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sdk_version: 3.
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app_file: app.py
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pinned: false
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license: cc-by-sa-4.0
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---
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title: StableDiffusionBiasExplorer
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emoji: π
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colorFrom: pink
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colorTo: yellow
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sdk: gradio
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sdk_version: 3.3.1
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app_file: app.py
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pinned: false
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license: cc-by-sa-4.0
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app.py
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import gradio as gr
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import random, os, shutil
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from PIL import Image
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import pandas as pd
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import tempfile
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def open_sd_ims(adj, group, seed):
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if group != '':
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if adj != '':
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prompt=adj+'_'+group.replace(' ','_')
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if os.path.isdir(prompt) == False:
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shutil.unpack_archive('zipped_images/stablediffusion/'+ prompt.replace(' ', '_') +'.zip', prompt, 'zip')
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else:
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prompt=group
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if os.path.isdir(prompt) == False:
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shutil.unpack_archive('zipped_images/stablediffusion/'+ prompt.replace(' ', '_') +'.zip', prompt, 'zip')
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imnames= os.listdir(prompt+'/Seed_'+ str(seed)+'/')
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images = [(Image.open(prompt+'/Seed_'+ str(seed)+'/'+name)) for name in imnames]
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return images[:9]
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def open_ims(model, adj, group):
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seed = 48040
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with tempfile.TemporaryDirectory() as tmpdirname:
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print('created temporary directory', tmpdirname)
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if model == "Dall-E 2":
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if group != '':
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if adj != '':
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prompt=adj+'_'+group.replace(' ','_')
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if os.path.isdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt) == False:
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shutil.unpack_archive('zipped_images/'+ model.replace(' ','').lower()+ '/'+ prompt.replace(' ', '_') +'.zip', tmpdirname+ '/'+ model.replace(' ','').lower()+ '/'+ prompt, 'zip')
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else:
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prompt=group
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if os.path.isdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt) == False:
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shutil.unpack_archive('zipped_images/' + model.replace(' ','').lower() + '/'+ prompt.replace(' ', '_') +'.zip', tmpdirname + '/' + model.replace(' ','').lower()+ '/' + prompt, 'zip')
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imnames= os.listdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt+'/')
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images = [(Image.open(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt+'/'+name)).convert("RGB") for name in imnames]
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return images[:9]
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else:
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if group != '':
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if adj != '':
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prompt=adj+'_'+group.replace(' ','_')
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if os.path.isdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt) == False:
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shutil.unpack_archive('zipped_images/'+ model.replace(' ','').lower()+ '/'+ prompt.replace(' ', '_') +'.zip', tmpdirname + '/' +model.replace(' ','').lower()+ '/'+ prompt, 'zip')
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else:
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prompt=group
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if os.path.isdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt) == False:
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shutil.unpack_archive('zipped_images/' + model.replace(' ','').lower() + '/'+ prompt.replace(' ', '_') +'.zip', tmpdirname + '/' + model.replace(' ','').lower()+'/'+ prompt, 'zip')
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imnames= os.listdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt+'/'+'Seed_'+ str(seed)+'/')
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images = [(Image.open(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt +'/'+'Seed_'+ str(seed)+'/'+name)) for name in imnames]
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return images[:9]
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vowels = ["a","e","i","o","u"]
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prompts = pd.read_csv('promptsadjectives.csv')
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seeds = [46267, 48040, 51237, 54325, 60884, 64830, 67031, 72935, 92118, 93109]
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m_adjectives = prompts['Masc-adj'].tolist()[:10]
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f_adjectives = prompts['Fem-adj'].tolist()[:10]
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adjectives = sorted(m_adjectives+f_adjectives)
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#adjectives = ['attractive','strong']
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adjectives.insert(0, '')
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professions = sorted([p.lower() for p in prompts['Occupation-Noun'].tolist()])
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models = ["Stable Diffusion 1.4", "Dall-E 2","Stable Diffusion 2"]
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with gr.Blocks() as demo:
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gr.Markdown("# Diffusion Bias Explorer")
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gr.Markdown("## Choose from the prompts below to explore how the text-to-image models like [Stable Diffusion v1.4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original), [Stable Diffion v.2](https://huggingface.co/stabilityai/stable-diffusion-2) and [DALLE-2](https://openai.com/dall-e-2/) represent different professions and adjectives")
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# gr.Markdown("Some of the images for Dall-E 2 are missing -- we are still in the process of generating them! If you get an 'error', please pick another prompt.")
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# seed_choice = gr.State(0)
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# seed_choice = 93109
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# print("Seed choice is: " + str(seed_choice))
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with gr.Row():
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with gr.Column():
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model1 = gr.Dropdown(models, label = "Choose a model to compare results", value = models[0], interactive=True)
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adj1 = gr.Dropdown(adjectives, label = "Choose a first adjective (or leave this blank!)", interactive=True)
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choice1 = gr.Dropdown(professions, label = "Choose a first group", interactive=True)
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# seed1= gr.Dropdown(seeds, label = "Choose a random seed to compare results", value = seeds[1], interactive=True)
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images1 = gr.Gallery(label="Images").style(grid=[3], height="auto")
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with gr.Column():
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model2 = gr.Dropdown(models, label = "Choose a model to compare results", value = models[0], interactive=True)
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adj2 = gr.Dropdown(adjectives, label = "Choose a second adjective (or leave this blank!)", interactive=True)
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choice2 = gr.Dropdown(professions, label = "Choose a second group", interactive=True)
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# seed2= gr.Dropdown(seeds, label = "Choose a random seed to compare results", value= seeds[1], interactive=True)
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images2 = gr.Gallery(label="Images").style(grid=[3], height="auto")
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gr.Markdown("### [Research](http://gender-decoder.katmatfield.com/static/documents/Gaucher-Friesen-Kay-JPSP-Gendered-Wording-in-Job-ads.pdf) has shown that \
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certain words are considered more masculine- or feminine-coded based on how appealing job descriptions containing these words \
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seemed to male and female research participants and to what extent the participants felt that they 'belonged' in that occupation.")
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#demo.load(random_image, None, [images])
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choice1.change(open_ims, [model1, adj1,choice1], [images1])
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choice2.change(open_ims, [model2, adj2,choice2], [images2])
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adj1.change(open_ims, [model1, adj1, choice1], [images1])
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adj2.change(open_ims, [model2, adj2, choice2], [images2])
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# seed1.change(open_ims, [adj1,choice1,seed1], [images1])
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# seed2.change(open_ims, [adj2,choice2,seed2], [images2])
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demo.launch()
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promptsadjectives.csv
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Masc-adj,Fem-adj,Occupation-Noun
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ambitious,committed,manager
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assertive,supportive,truck driver
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confident,sensitive,teacher
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decisive,emotional,nurse
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determined,gentle,supervisor
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intellectual,honest,salesperson
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outspoken,modest,cashier
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self-confident,compassionate,customer service representative
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stubborn,considerate,construction worker
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unreasonable,pleasant,mover
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,,janitor
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,,software developer
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,,cook
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,,CEO
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,,stocker
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,,waiter
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,,waitress
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,,accountant
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,,aide
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,,maid
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,,teaching assistant
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,,financial manager
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,,office clerk
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,,nursing assistant
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,,carpenter
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,,receptionist
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,,groundskeeper
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,,real estate broker
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,,clerk
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,,lawyer
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,,childcare worker
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,,doctor
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,,farmer
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,,mechanic
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,,electrician
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,,security guard
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,,courier
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,,fast food worker
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,,police officer
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,,IT specialist
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,,hairdresser
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,,social worker
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,,engineer
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,,computer support specialist
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,,office worker
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,,tractor operator
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,,inventory clerk
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,,repair worker
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,,insurance agent
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,,plumber
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,,marketing manager
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,,painter
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,,welder
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,,sales manager
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,,financial advisor
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,,computer systems analyst
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,,air conditioning installer
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,,computer programmer
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,,credit counselor
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,,civil engineer
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,,paralegal
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,,machinery mechanic
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,,clergy
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,,head cook
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,,market research analyst
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,,community manager
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,,designer
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,,scientist
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,,laboratory technician
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,,career counselor
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,,bartender
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,,mechanical engineer
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,,pharmacist
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,,financial analyst
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,,pharmacy technician
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,,taxi driver
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,,metal worker
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,,claims appraiser
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,,dental assistant
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,,machinist
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,,cleaner
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,,electrical engineer
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,,correctional officer
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,,jailer
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,,firefighter
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,,compliance officer
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,,artist
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,,host
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,,hostess
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,,school bus driver
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,,physical therapist
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,,postal worker
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,,graphic designer
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,,writer
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,,author
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,,manicurist
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,,butcher
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,,dishwasher
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,,therapist
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,,bus driver
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,,coach
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,,baker
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,,radiologic technician
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,,purchasing agent
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,,fitness instructor
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,,executive assistant
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,,roofer
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,,data entry keyer
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,,industrial engineer
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,,teller
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,,network administrator
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,,architect
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,,mental health counselor
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,,dental hygienist
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,,medical records specialist
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,,interviewer
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,,social assistant
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,,photographer
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,,dispatcher
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,,language pathologist
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,,producer
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,,director
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,,health technician
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,,tutor
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,,dentist
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,,massage therapist
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,,file clerk
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,,wholesale buyer
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,,librarian
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,,pilot
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,,carpet installer
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,,drywall installer
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,,payroll clerk
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,,plane mechanic
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,,psychologist
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,,facilities manager
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,,printing press operator
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,,occupational therapist
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,,logistician
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,,detective
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,,aerospace engineer
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,,veterinarian
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,,underwriter
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,,musician
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,,singer
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,,sheet metal worker
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,,interior designer
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,,public relations specialist
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,,nutritionist
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,,event planner
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requirements.txt
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pillow
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pandas
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