Reorder models

app.py

@@ -144,6 +144,26 @@ def generate_images_plots(prompt, model_name):
 
 with gr.Blocks(title="Skin Tone and Gender bias in Text to Image 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 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 is used to detect gender by identifying words like "man," "boy," "woman," and "girl" in the captions.
+3. **Skin Tone Classification**: The skin-tone-classifier library is used to extract the skin tones of the generated subjects.
+
+
+#### 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.
+- **Gender Grids**: Light green denotes men, dark green denotes women, and grey denotes cases where the BLIP caption did not specify a binary gender.
+
+---
+
+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", 
         choices=[
@@ -171,25 +191,4 @@ with gr.Blocks(title="Skin Tone and Gender bias in Text to Image Models") as dem
         genplot = gr.Plot(label="Gender")
     btn.click(generate_images_plots, inputs=[prompt, model_dropdown], outputs=[gallery, skinplot, genplot])
 
-    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:
-
-1. **Image Generation**: For each prompt, 10 images are generated using the selected model.
-2. **Gender Detection**: The BLIP caption generator is used to detect gender by identifying words like "man," "boy," "woman," and "girl" in the captions.
-3. **Skin Tone Classification**: The skin-tone-classifier library is used to extract the skin tones of the generated subjects.
-
-
-#### 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.
-- **Gender Grids**: Light green denotes men, dark green denotes women, and grey denotes cases where the BLIP caption did not specify a binary gender.
-
----
-
-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.
-''')
-
 demo.launch(debug=True)