update description

app.py

@@ -164,15 +164,15 @@ with gr.Blocks(title="Skin Tone and Gender bias in Text to Image Models") as dem
 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.
+2. **Gender Detection**: The [BLIP caption generator](https://huggingface.co/Salesforce/blip-image-captioning-large) 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](https://github.com/ChenglongMa/SkinToneClassifier) 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.
+- **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.
 
 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.