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import gradio as gr |
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import torch |
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import numpy as np |
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import matplotlib |
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matplotlib.use("Agg") |
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import matplotlib.pyplot as plt |
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from PIL import Image |
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import collections |
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import numpy as np |
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import pandas as pd |
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import io |
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from saac.prompt_generation.prompts import generate_prompts,generate_occupations,generate_traits |
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from saac.prompt_generation.prompt_utils import score_prompt |
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from saac.image_analysis.process import process_image_pil |
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from saac.evaluation.eval_utils import generate_countplot, lumia_violinplot, process_analysis, generate_histplot,rgb_intensity |
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from datasets import load_dataset |
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from diffusers import DiffusionPipeline, PNDMScheduler |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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STABLE_MODELS = ["runwayml/stable-diffusion-v1-5", "Midjourney"] |
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scheduler = PNDMScheduler.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="scheduler", prediction_type="v_prediction") |
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pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", scheduler=scheduler) |
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pipe = pipe.to(device) |
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tokenizer = pipe.tokenizer |
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text_encoder = pipe.text_encoder |
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GENDERS = ["male", "female"] |
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ETHNICITIES = ["black", "white", "asian"] |
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LOOKS = list(generate_traits()['tag']) |
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JOBS = list(generate_occupations()['tag']) |
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RENDERPREFIX = "a high quality photo of a" |
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def echoToken(token): |
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res = getMostSimilar(tokenizer, text_encoder, token) |
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return ",".join(res) |
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def getEmbeddingForToken(tokenizer, token): |
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token_ids = tokenizer.encode(token)[1:-1] |
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if len(token_ids) != 1: |
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print(len(token_ids)) |
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raise |
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token_id = token_ids[0] |
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return token_id, text_encoder.get_input_embeddings().weight.data[token_id].unsqueeze(0) |
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def getMostSimilar(tokenizer, text_encoder, token, numResults=50): |
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internal_embs = text_encoder.text_model.embeddings.token_embedding.weight |
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tID, tok = getEmbeddingForToken(tokenizer, token) |
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cos = torch.nn.CosineSimilarity(dim=1, eps=1e-6) |
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scores = cos(internal_embs.to("cpu").to(torch.float32), tok.to("cpu").to(torch.float32)) |
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sorted_scores, sorted_ids = torch.sort(scores, descending=True) |
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best_ids = sorted_ids[0:numResults].detach().numpy() |
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best_scores = sorted_scores[0:numResults].detach().numpy() |
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res = [] |
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for best_id, best_score in zip(best_ids, best_scores): |
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res.append("[" + tokenizer.decode(best_id) + "," + str(best_score) + "]") |
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return res[1:] |
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def computeTermSimilarity(tokenizer, text_encoder, termA, termB): |
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inputs = tokenizer([termA, termB], padding=True, return_tensors="pt").to("cpu") |
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outputs = text_encoder(**inputs) |
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cos = torch.nn.CosineSimilarity(dim=-1, eps=1e-6) |
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val = cos(outputs.pooler_output[0], outputs.pooler_output[1]).item() |
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return float(val) |
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def computeJob(tokenizer, text_encoder, job): |
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res = {} |
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neutralPrompt = " ".join([RENDERPREFIX, job]) |
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titleText = neutralPrompt |
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for gender in GENDERS: |
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for ethnicity in ETHNICITIES: |
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prompt = " ".join([RENDERPREFIX, ethnicity, gender, job]) |
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val = computeTermSimilarity(tokenizer, text_encoder, prompt, neutralPrompt) |
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res[prompt] = val |
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return titleText, sorted(res.items(), reverse=True) |
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def computeLook(tokenizer, text_encoder, look): |
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res = {} |
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titleText = " ".join([RENDERPREFIX, |
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look, |
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"[", |
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"|".join(GENDERS), |
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"]"]) |
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for gender in GENDERS: |
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neutralPromptGender = " ".join([RENDERPREFIX, look, gender]) |
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for ethnicity in ETHNICITIES: |
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prompt = " ".join([RENDERPREFIX, look, ethnicity, gender]) |
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val = computeTermSimilarity(tokenizer, text_encoder, prompt, neutralPromptGender) |
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res[prompt] = val |
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return titleText, sorted(res.items(), reverse=True) |
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def fig2img(fig): |
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"""Convert a Matplotlib figure to a PIL Image and return it""" |
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buf = io.BytesIO() |
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fig.savefig(buf) |
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buf.seek(0) |
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img = Image.open(buf) |
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return img |
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def computePlot(title, results, scaleXAxis=True): |
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x = list(map(lambda x:x[0], results)) |
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y = list(map(lambda x:x[1], results)) |
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fig, ax = plt.subplots(1, 1, figsize=(10, 5)) |
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y_pos = np.arange(len(x)) |
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hbars = ax.barh(y_pos, y, left=0, align='center') |
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ax.set_yticks(y_pos, labels=x) |
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ax.invert_yaxis() |
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ax.set_xlabel('Cosine similarity - take care to note compressed X-axis') |
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ax.set_title('Similarity to "' + title + '"') |
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ax.bar_label(hbars, fmt='%.3f') |
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minR = np.min(y) |
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maxR = np.max(y) |
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diffR = maxR-minR |
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if scaleXAxis: |
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ax.set_xlim(left=minR-0.1*diffR, right=maxR+0.1*diffR) |
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else: |
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ax.set_xlim(left=0.0, right=1.0) |
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plt.tight_layout() |
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plt.close() |
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return fig2img(fig) |
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def computeJobBias(job): |
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title, results = computeJob(tokenizer, text_encoder, job) |
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return computePlot(title, results) |
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def computeLookBias(look): |
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title, results = computeLook(tokenizer, text_encoder, look) |
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return computePlot(title, results) |
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def trait_graph(trait,hist=True): |
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tda_res, occ_result = process_analysis() |
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fig = None |
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if not hist: |
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fig = generate_countplot(tda_res, 'tda_sentiment_val', 'gender_detected_val', |
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title='Gender Count by Trait Sentiment', |
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xlabel='Trait Sentiment', |
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ylabel='Count', |
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legend_title='Gender') |
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else: |
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df = tda_res |
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df['tda_sentiment_val'] = pd.Categorical(df['tda_sentiment_val'], |
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['very negative', 'negative', 'neutral', 'positive', 'very positive']) |
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fig = generate_histplot(tda_res, 'tda_sentiment_val', 'gender_detected_val', |
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title='Gender Distribution by Trait Sentiment', |
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xlabel='Trait Sentiment', |
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ylabel='Count', ) |
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fig2 = lumia_violinplot(df = tda_res, |
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x_col = 'tda_compound', |
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rgb_col = 'skincolor', |
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n_bins = 21, |
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widths_val = 0.05, |
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points_val = 100, |
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x_label = 'TDA Sentiment', |
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y_label = 'Skincolor Intensity', |
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title = 'Skin Color Intensity, Binned by TDA Sentiment',) |
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return fig2img(fig),fig2img(fig2) |
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def occ_graph(model): |
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tda_res, occ_result = process_analysis() |
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fig = generate_histplot(occ_result, 'a_median', 'gender_detected_val', |
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title='Gender Distribution by Median Annual Salary', |
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xlabel= 'Median Annual Salary', |
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ylabel= 'Count',) |
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fig2 = lumia_violinplot(df=occ_result, x_col='a_median', |
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rgb_col='skincolor', |
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n_bins=21, |
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widths_val=7500.0, |
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points_val=100, |
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x_label='Median Salary', |
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y_label='Skincolor Intensity', |
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title='Skin Color Intensity, Binned by Median Salary') |
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return fig2img(fig),fig2img(fig2) |
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def occ_example(occ): |
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prompt = f"a {occ} photorealistic" |
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return example_analysis(prompt) |
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def adj_example(adj): |
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prompt = f"a {adj} person photorealistic" |
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return example_analysis(prompt) |
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def example_analysis(prompt): |
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pil_img = pipe(prompt).images[0] |
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df = process_image_pil(pil_img,prompt) |
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rgb_tup = df["skin color"][0] |
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def clamp(x): |
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return max(0, min(int(x), 255)) |
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def hex_from_tup(in_tup): |
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return "#{0:02x}{1:02x}{2:02x}".format(clamp(in_tup[0]), clamp(in_tup[1]), clamp(in_tup[2])) |
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rgb_hex = hex_from_tup(rgb_tup) |
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intensity_val = rgb_intensity(rgb_tup) |
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intense_hex = str(hex(int(intensity_val))) |
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intense_hex = f"#{intense_hex}{intense_hex}{intense_hex}" |
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gender_w = float(df["gender.Woman"][0]) |
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gender_m = float(df["gender.Man"][0]) |
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gender_str = f"Male ({gender_m})" if gender_m>gender_w else f"Female({gender_w}" |
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return pil_img,gender_str,rgb_hex,intense_hex |
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if __name__=='__main__': |
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disclaimerString = "" |
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jobInterface = gr.Interface(fn=occ_example, |
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inputs=[gr.Dropdown(JOBS, label="occupation")], |
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outputs=['image','text','colorpicker','colorpicker'], |
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description="Referencing a specific profession comes loaded with associations of gender and ethnicity." |
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" Text to image models provide an opportunity to explicitly specify an underrepresented group, but first we must understand our default behavior.", |
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title="How occupation affects txt2img gender and skin color representation", |
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article = "To view how mentioning a particular occupation affects the gender and skin colors in faces of text to image generators, select a job." |
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" Promotional materials, advertising, and even criminal sketches which do not explicitly specify a gender or ethnicity term will tend towards the displayed distributions.") |
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affectInterface = gr.Interface(fn=trait_graph, |
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inputs=[gr.Dropdown(LOOKS, label="trait")], |
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outputs=['image','image'], |
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description="Certain adjectives can reinforce harmful stereotypes associated with gender roles and ethnic backgrounds." |
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"Text to image models provide an opportunity to understand how prompting a particular human expression could be triggering," |
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" or why an uncommon combination might provide important examples to minorities without default representation.", |
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title="How word sentiment affects txt2img gender and skin color representation", |
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article = "To view how characterizing a person with a positive, negative, or neutral term influences the gender and skin color composition of AI-generated faces, select a direction.") |
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jobInterfaceManual = gr.Interface(fn=score_prompt, |
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inputs=[gr.inputs.Textbox()], |
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outputs='text', |
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description="Analyze prompt", |
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title="Understand which prompts require further engineering to represent equally genders and skin colors", |
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article = "Try modifying a trait or occupational prompt to produce a result in the minority representation!") |
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toolInterface = gr.Interface(fn=lambda t: trait_graph(t,hist=False),inputs=[gr.Dropdown(STABLE_MODELS,label="text-to-image model")],outputs='image', |
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title="How different models fare in gender and skin color representation across a variety of prompts", |
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description="The training set, vocabulary, pre and post processing of generative AI tools doesn't treat everyone equally. " |
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"Within a 95% margin of statistical error, the following tests expose bias in gender and skin color.", |
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article="To learn more about this process, <a href=\"http://github.com/TRSS-Research/SAAC.git\"/> Visit the repo</a>" |
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) |
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gr.TabbedInterface( |
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[jobInterface, affectInterface, jobInterfaceManual,toolInterface], |
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["Occupational Bias", "Adjectival Bias", "Prompt analysis",'FACIA model auditing'], |
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title = "Text-to-Image Bias Explorer" |
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).launch() |
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