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import gradio as gr | |
from datasets import load_dataset | |
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] | |
indexes = [768, 1536, 10752] | |
ds = load_dataset("tti-bias/identities", split="train") | |
def get_nearest_64(gender, ethnicity, model, no, index): | |
df = ds.remove_columns(["image","image_path"]).to_pandas() | |
index = np.load(f"indexes/knn_{index}_65.npy") | |
ix = df.loc[(df['ethnicity'] == ethnicity) & (df['gender'] == gender) & (df['no'] == no) & (df['model'] == model)].index[0] | |
image = ds.select([index[ix][0]])["image"][0] | |
neighbors = ds.select(index[ix][1:25]) | |
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("# BoVW Nearest Neighbors Explorer") | |
gr.Markdown("### TF-IDF index of the _identities_ dataset of images generated by 3 models using a visual vocabulary of 10,752 words.") | |
gr.Markdown("#### Choose one of the generated identity images to see its nearest neighbors according to a bag-of-visual-words model.") | |
with gr.Row(): | |
with gr.Column(): | |
model = gr.Radio(models, label="Model") | |
index = gr.Radio(indexes, label="Visual vocabulary size") | |
gender = gr.Radio(gender_labels, label="Gender label") | |
with gr.Column(): | |
ethnicity = gr.Radio(ethnicity_labels, label="Ethnicity label") | |
no = gr.Radio(nos, label="Image number") | |
button = gr.Button(value="Get nearest neighbors") | |
with gr.Row(): | |
image = gr.Image() | |
gallery = gr.Gallery().style(grid=4) | |
button.click(get_nearest_64, inputs=[gender, ethnicity, model, no, index], outputs=[image, gallery]) | |
demo.launch() | |