adding first section draft

app.py

@@ -1,16 +1,33 @@
 import gradio as gr
+from PIL import Image
+import os
 
+def get_identity_images(path="images/identities"):
+    ims = Image.open([os.path.join(path,im) for im in os.listdir(path)])
+    return ims
 
 with gr.Blocks() as demo:
     gr.Markdown("""
                 ## Stable Bias:  Analyzing Societal Representations in Diffusion Models
             """)
     gr.HTML('''
-     <p style="margin-bottom: 10px; font-size: 94%">This is the demo page for the "Stable Bias" paper, which aims to explore and quantify social biases in text-to-image systems.</p>
+     <p style="margin-bottom: 10px; font-size: 94%">This is the demo page for the "Stable Bias" paper, which aims to explore and quantify social biases in text-to-image systems. <br> This work was done by <a href='https://huggingface.co/sasha' style='text-decoration: underline;' target='_blank'> Alexandra Sasha Luccioni (Hugging Face) </a>, <a href='https://huggingface.co/cakiki' style='text-decoration: underline;' target='_blank'> Christopher Akiki (ScaDS.AI, Leipzig University)</a>, <a href='https://huggingface.co/meg' style='text-decoration: underline;' target='_blank'> Margaret Mitchell (Hugging Face) </a> and  <a href='https://huggingface.co/yjernite' style='text-decoration: underline;' target='_blank'> Yacine Jernite (Hugging Face) </a> .</p>
           ''')
+    
     gr.HTML('''
-     <p style="margin-bottom: 10px; font-size: 94%">This work was done by <a href='https://huggingface.co/sasha' style='text-decoration: underline;' target='_blank'> Alexandra Sasha Luccioni (Hugging Face) </a>, <a href='https://huggingface.co/cakiki' style='text-decoration: underline;' target='_blank'> Christopher Akiki (ScaDS.AI, Leipzig University)</a>, <a href='https://huggingface.co/meg' style='text-decoration: underline;' target='_blank'> Margaret Mitchell (Hugging Face) </a> and  <a href='https://huggingface.co/yjernite' style='text-decoration: underline;' target='_blank'> Yacine Jernite (Hugging Face) </a> .</p>
-          ''')
-  
+     <p style="margin-bottom: 14px; font-size: 100%">  As AI-enabled Text-to-Image systems are becoming increasingly used, characterizing the social biases they exhibit is a necessary first step to lowering their risk of discriminatory outcomes. <br> We propose a new method for exploring and quantifying social biases in these kinds of systems by directly comparing collections of generated images designed to showcase a system’s variation across social attributes — gender and ethnicity — and target attributes for bias evaluation — professions and gender-coded adjectives. <br> We compare three models: Stable Diffusion v.1.4, Stable Diffusion v.2., and Dall-E 2, and present some of our key findings below:</p>
+          ''')    
+
+    with gr.Accordion("Identity group results (ethnicity and gender)", open=False):
+         gr.HTML('''
+         <p style="margin-bottom: 14px; font-size: 100%">  One of the approaches that we adopted in our work is hierarchical clustering of the images generated by the text-to-image systems in response to prompts that include identity terms with regards to ethnicity and gender. <br> We computed 3 different numbers of clusters (12, 24 and 48) and created an <a href='https://huggingface.co/spaces/society-ethics/DiffusionFaceClustering' style='text-decoration: underline;' target='_blank'> Identity Representation Demo </a> that allows for the exploration of the different clusters and their contents. <br>  </p>
+              ''')    
+         with gr.Row():
+            impath = "images/identities"
+            identity_gallery = gr.Gallery([os.path.join(impath,im) for im in os.listdir(impath)],
+            label="Identity cluster images images", show_label=False, elem_id="gallery"
+        ).style(grid=3, height="auto")
+
+
                      
-demo.launch(debug=True)
+demo.launch(debug=True)