Update app.py

app.py

@@ -174,14 +174,14 @@ def ageplot(agelist):
 
 def is_nsfw(image):
     """
-    A function that checks if the input image is not safe for work (NSFW) by classifying it using 
+    A function that checks if the input image is not for all audiences (NFAA) by classifying it using 
     an image classification pipeline and returning the label with the highest score.
     
     Args:
         image: The input image to be classified.
         
     Returns:
-        str: The label of the NSFW category with the highest score.
+        str: The label of the NFAA category with the highest score.
     """
     classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection")
     result = classifier(image)
@@ -190,7 +190,7 @@ def is_nsfw(image):
 
 def nsfwplot(nsfwlist):
     """
-    Generates a plot of NSFW categories based on a list of NSFW labels.
+    Generates a plot of NFAA categories based on a list of NFAA labels.
 
     Args:
         nsfwlist (list): A list of NSFW labels ("normal" or "nsfw").
@@ -201,9 +201,9 @@ def nsfwplot(nsfwlist):
     Raises:
         None
 
-    This function takes a list of NSFW labels and generates a plot with a grid of 2 rows and 5 columns. 
+    This function takes a list of NFAA labels and generates a plot with a grid of 2 rows and 5 columns. 
     Each label is sorted based on a predefined order and assigned a color. The plot is then created using matplotlib, 
-    with each cell representing an NSFW label. The color of each cell is determined by the corresponding label's color. 
+    with each cell representing an NFAA label. The color of each cell is determined by the corresponding label's color. 
     The function returns the generated figure object.
     """
     order = ["normal", "nsfw"]
@@ -243,16 +243,16 @@ def generate_images_plots(prompt, model_name):
         nsfws.append(nsfw)
     return images, skintoneplot(skintones), genderplot(genders), ageplot(ages), nsfwplot(nsfws)
 
-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")
+with gr.Blocks(title="Demographic bias in Text-to-Image Generation Models") as demo:
+    gr.Markdown("# Demographic 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 and skin tone of the generated subjects. 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, skin tone, age, and potential sexual nature 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).
+5. **NFAA Detection**: The [Falconsai/nsfw_image_detection](https://huggingface.co/Falconsai/nsfw_image_detection) model is used to identify whether the generated images are NFAA (not for all audiences).
 
 #### Visualization
 
@@ -261,7 +261,7 @@ 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.
+- **NFAA Grids**: Light red denotes FAA images, and dark red denotes NFAA 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.
@@ -294,7 +294,7 @@ This demo provides an insightful look into how current text-to-image models hand
         genplot = gr.Plot(label="Gender")
     with gr.Row(equal_height=True):
         agesplot = gr.Plot(label="Age")
-        nsfwsplot = gr.Plot(label="NSFW")
+        nsfwsplot = gr.Plot(label="NFAA")
     btn.click(generate_images_plots, inputs=[prompt, model_dropdown], outputs=[gallery, skinplot, genplot, agesplot, nsfwsplot])
 
 demo.launch(debug=True)