revert changes

app.py

@@ -8,7 +8,7 @@ from diffusers import (
     UNet2DConditionModel,
     StableDiffusion3Pipeline
 )
-from transformers import BlipProcessor, BlipForConditionalGeneration, pipeline
+from transformers import BlipProcessor, BlipForConditionalGeneration
 from pathlib import Path
 from safetensors.torch import load_file
 from huggingface_hub import hf_hub_download
@@ -86,44 +86,6 @@ def genderplot(genlist):
         ax.add_patch(plt.Rectangle((0, 0), 1, 1, color=word_colors[i]))
     return fig
 
-def age_detector(image):
-    pipe = pipeline('image-classification', model="dima806/faces_age_detection", device=0)
-    result = pipe(image)
-    print(result)
-    filtered_labels = [item['label'] for item in result if item['score'] > 0.85]
-    print(filtered_labels)
-    return filtered_labels
-
-def ageplot(agelist):
-    order = ["YOUNG", "MIDDLE", "OLD"]
-    words = sorted(agelist, key=lambda x: order.index(x))
-    colors = {"YOUNG": "lightblue", "MIDDLE": "blue", "OLD": "darkblue", "Unsure": "lightgrey"}
-    word_colors = [colors[word] for word in words]
-    fig, axes = plt.subplots(2, 5, figsize=(5,5))
-    plt.subplots_adjust(hspace=0.1, wspace=0.1)
-    for i, ax in enumerate(axes.flat):
-        ax.set_axis_off()
-        ax.add_patch(plt.Rectangle((0, 0), 1, 1, color=word_colors[i]))
-    return fig
-
-def is_nsfw(image):
-    classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection")
-    result = classifier(image)
-    filtered_labels = [item['label'] for item in result if item['score'] > 0.85]
-    return filtered_labels[0]
-
-def nsfwplot(nsfwlist):
-    order = ["normal", "nsfw"]
-    words = sorted(nsfwlist, key=lambda x: order.index(x))
-    colors = {"normal": "lightred", "nsfw": "darkred", "Unsure": "lightgrey"}
-    word_colors = [colors[word] for word in words]
-    fig, axes = plt.subplots(2, 5, figsize=(5,5))
-    plt.subplots_adjust(hspace=0.1, wspace=0.1)
-    for i, ax in enumerate(axes.flat):
-        ax.set_axis_off()
-        ax.add_patch(plt.Rectangle((0, 0), 1, 1, color=word_colors[i]))
-    return fig
-
 def skintoneplot(hex_codes):
     hex_codes = [code for code in hex_codes if code is not None]
     rgb_values = [hex2color(hex_code) for hex_code in hex_codes]
@@ -146,13 +108,9 @@ def generate_images_plots(prompt, model_name):
     images = [getimgen(prompt, model_name) for _ in range(10)]
     genders = []
     skintones = []
-    ages = []
-    nsfws = []
     for image, i in zip(images, range(10)):
         prompt_prefix = "photo of a "
         caption = blip_caption_image(image, prefix=prompt_prefix)
-        age = age_detector(image)
-        nsfw = is_nsfw(image)
         image.save(f"{foldername}/image_{i}.png")
         try:
             skintoneres = stone.process(f"{foldername}/image_{i}.png", return_report_image=False)
@@ -161,46 +119,31 @@ def generate_images_plots(prompt, model_name):
         except:
             skintones.append(None)
         genders.append(genderfromcaption(caption))
-        ages.append(age)
-        nsfws.append(nsfw)
-    return images, skintoneplot(skintones), genderplot(genders), ageplot(ages), nsfwplot(nsfws)
+    return images, skintoneplot(skintones), genderplot(genders)
 
-with gr.Blocks(title="Bias detection in SinteticoXL Models") as demo:
-    gr.Markdown("# Bias detection in SinteticoXL Models")
+with gr.Blocks(title="Skin Tone and Gender bias in Text-to-Image Generation Models") as demo:
+    gr.Markdown("# Skin Tone and Gender bias in Text to Image Models")
     gr.Markdown('''
-In this demo, we explore the potential biases in text-to-image models by generating multiple images based on user prompts and analyzing the gender, skin tone, and age of the generated subjects as well as the potential for NSFW content. Here's how the analysis works:
+In this demo, we explore the potential biases in text-to-image models by generating multiple images based on user prompts and analyzing the gender and skin tone of the generated subjects. Here's how the analysis works:
 
 1. **Image Generation**: For each prompt, 10 images are generated using the selected model.
 2. **Gender Detection**: The [BLIP caption generator](https://huggingface.co/Salesforce/blip-image-captioning-large) is used to elicit gender markers by identifying words like "man," "boy," "woman," and "girl" in the captions.
 3. **Skin Tone Classification**: The [skin-tone-classifier library](https://github.com/ChenglongMa/SkinToneClassifier) is used to extract the skin tones of the generated subjects.
-4. **Age Detection**: The [Faces Age Detection model](https://huggingface.co/dima806/faces_age_detection) is used to identify the age of the generated subjects.
-5. **NSFW Detection**: The [Falconsai/nsfw_image_detection](https://huggingface.co/Falconsai/nsfw_image_detection) model is used to identify whether the generated images are NSFW (not safe for work).
 
 ## Models
 
 - Sintetico XL: a merged model with my favorite aesthetics
 - Sintetico XL Prude: a SFW version that aims to remove unwanted nudity and sexual content.
 
-
-''')
-    with gr.Accordion("Open for More Information!", open=False):
-        gr.Markdown('''
-This space was clone from [JournalistsonHF/text-to-image-bias](https://huggingface.co/spaces/JournalistsonHF/text-to-image-bias).
-
-👉 It's also in line with "Stable Bias" work by Hugging Face's ML & Society team: https://huggingface.co/spaces/society-ethics/StableBias     
-
-This demo provides an insightful look into how current text-to-image models handle sensitive attributes, shedding light on areas for improvement and further study. 
-[Here is an article](https://medium.com/@evijit/analysis-of-ai-generated-images-of-indian-people-for-colorism-and-sexism-b80ff946759f) showing how this space can be used to perform such analyses, using colorism and sexism in India as an example.          
-
 #### Visualization
 
 We create visual grids to represent the data:
 
 - **Skin Tone Grids**: Skin tones are plotted as exact hex codes rather than using the Fitzpatrick scale, which can be [problematic and limiting for darker skin tones](https://arxiv.org/pdf/2309.05148).
 - **Gender Grids**: Light green denotes men, dark green denotes women, and grey denotes cases where the BLIP caption did not specify a binary gender.
-- **Age Grids**: Light blue denotes people between 18 and 30, blue denotes people between 30 and 50, and dark blue denotes people older than 50.
-- **NSFW Grids**: Light red denotes SFW images, and dark red denotes NSFW images.
 
+This demo provides an insightful look into how current text-to-image models handle sensitive attributes, shedding light on areas for improvement and further study. 
+[Here is an article](https://medium.com/@evijit/analysis-of-ai-generated-images-of-indian-people-for-colorism-and-sexism-b80ff946759f) showing how this space can be used to perform such analyses, using colorism and sexism in India as an example.
 ''')
     model_dropdown = gr.Dropdown(
         label="Choose a model", 
@@ -224,9 +167,6 @@ We create visual grids to represent the data:
     with gr.Row(equal_height=True):
         skinplot = gr.Plot(label="Skin Tone")
         genplot = gr.Plot(label="Gender")
-    with gr.Row(equal_height=True):
-        ageplot = gr.Plot(label="Age")
-        nsfwplot = gr.Plot(label="NSFW")
-    btn.click(generate_images_plots, inputs=[prompt, model_dropdown], outputs=[gallery, skinplot, genplot, ageplot, nsfwplot])
+    btn.click(generate_images_plots, inputs=[prompt, model_dropdown], outputs=[gallery, skinplot, genplot])
 
 demo.launch(debug=True)