| import gradio as gr | |
| from datasets import load_from_disk | |
| import numpy as np | |
| gender_labels = ['man', 'non-binary', 'woman', 'no_gender_specified', ] | |
| ethnicity_labels = ['African-American', 'American_Indian', 'Black', 'Caucasian', 'East_Asian', | |
| 'First_Nations', 'Hispanic', 'Indigenous_American', 'Latino', 'Latinx', | |
| 'Multiracial', 'Native_American', 'Pacific_Islander', 'South_Asian', | |
| 'Southeast_Asian', 'White', 'no_ethnicity_specified'] | |
| models = ['DallE', 'SD_14', 'SD_2'] | |
| nos = [1,2,3,4,5,6,7,8,9,10] | |
| ds = load_from_disk("color-sorted") | |
| def get_nearest(gender, ethnicity, model, no): | |
| df = ds.remove_columns(["image","image_path"]).to_pandas() | |
| ix = df.loc[(df['ethnicity'] == ethnicity) & (df['gender'] == gender) & (df['no'] == no) & (df['model'] == model)].index[0] | |
| image = ds.select([ix])["image"][0] | |
| neighbors = ds.select(range(max(ix-10, 0), min(ix+10, len(ds)-1))) | |
| neighbor_images = neighbors["image"] | |
| neighbor_captions = [caption.split("/")[-1] for caption in neighbors["image_path"]] | |
| neighbor_captions = [' '.join(caption.split("_")[4:-3]) for caption in neighbor_captions] | |
| neighbor_models = neighbors["model"] | |
| neighbor_captions = [f"{a} {b}" for a,b in zip(neighbor_captions,neighbor_models)] | |
| return image, list(zip(neighbor_images, neighbor_captions)) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Colorfulness Nearest Neighbors Explorer") | |
| gr.Markdown("### Colorfulness 1-D index of the _identities_ dataset of images generated by 3 models") | |
| gr.Markdown("#### Choose one of the generated identity images to see its nearest neighbors according to colorfulness") | |
| gr.HTML("""<span style="color:red">⚠️ <b>DISCLAIMER: the images displayed by this tool were generated by text-to-image models and may depict offensive stereotypes or contain explicit content.</b></span>""") | |
| with gr.Row(): | |
| with gr.Column(): | |
| model = gr.Radio(models, label="Model") | |
| gender = gr.Radio(gender_labels, label="Gender label") | |
| no = gr.Radio(nos, label="Image number") | |
| with gr.Column(): | |
| ethnicity = gr.Radio(ethnicity_labels, label="Ethnicity label") | |
| button = gr.Button(value="Get nearest neighbors") | |
| with gr.Row(): | |
| image = gr.Image() | |
| gallery = gr.Gallery().style(grid=4) | |
| button.click(get_nearest, inputs=[gender, ethnicity, model, no], outputs=[image, gallery]) | |
| demo.launch() |