changing titles

app.py

@@ -23,10 +23,10 @@ with gr.Blocks() as demo:
           ''')    
     
     gr.Markdown("""
-                ## Identity group results (ethnicity and gender)
+                ### Looking at Identity Groups
             """)
 
-    with gr.Accordion("Identity group results (ethnicity and gender)", open=False):
+    with gr.Accordion("Looking at Identity Groups(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. </p>
               ''')    
@@ -41,11 +41,11 @@ with gr.Blocks() as demo:
                  <p style="margin-bottom: 14px; font-size: 100%"> You can see that the models reflect many societal biases -- for instance representing Native Americans wearing traditional headdresses, non-binary people with stereotypical haircuts and glasses, and East Asian men with features that amplify ethnic stereotypes. <br> This is problematic because it reinforces existing cultural stereotypes and fails to represent the diversity that is present in all identity groups.</p>
               ''')    
     gr.Markdown("""
-                ## Bias Exploration
+                ### Exploring Biases
             """)
      
 
-    with gr.Accordion("Bias Exploration", open=False):
+    with gr.Accordion("Exploring Biases", open=False):
          gr.HTML('''
          <p style="margin-bottom: 14px; font-size: 100%">  We also explore the correlations between the professions that use used in our prompts and the different identity clusters that we identified. <br> Using both the <a href='https://huggingface.co/spaces/society-ethics/DiffusionClustering' style='text-decoration: underline;' target='_blank'> Diffusion Cluster Explorer </a> and the <a href='https://huggingface.co/spaces/society-ethics/DiffusionFaceClustering' style='text-decoration: underline;' target='_blank'> Identity Representation Demo </a>, we can see which clusters are most correlated with each profession and what identities are in these clusters.</p>
               ''')    
@@ -76,10 +76,10 @@ with gr.Blocks() as demo:
              cluster4 = gr.Image(Image.open("images/bias/Cluster2.png"), label = "Cluster 2 Image", show_label=False)  
 
     gr.Markdown("""
-                ## Comparing model generations
+                ### Comparing Model Generations
             """) 
     
-    with gr.Accordion("Comparing model generations", open=False):
+    with gr.Accordion("Comparing Model Generations", open=False):
          gr.HTML('''
          <p style="margin-bottom: 14px; font-size: 100%">  One of the goals of our study was allowing users to compare model generations across professions in an open-ended way, uncovering patterns and trends on their own. This is why we created the <a href='https://huggingface.co/spaces/society-ethics/DiffusionBiasExplorer' style='text-decoration: underline;' target='_blank'> Diffusion Bias Explorer </a> and the <a href='https://huggingface.co/spaces/society-ethics/Average_diffusion_faces' style='text-decoration: underline;' target='_blank'> Average Diffusion Faces </a> tools. We show some of their functionalities below:    </p> ''')    
          with gr.Row():           
@@ -100,9 +100,9 @@ with gr.Blocks() as demo:
                  <p style="margin-bottom: 14px; font-size: 100%"> Looking at the average faces for a given profession across multiple models can help see the dominant characteristics of that profession, as well as how much variation there is (based on how fuzzy the image is). <br> In the images shown here, we can see that representations of these professions significantly differ across the three models, while sharing common characteristics, e.g. <i> postal workers </i> all wear caps. <br> Also, the average faces of <i> hairdressers </i> seem more fuzzy than the other professions, indicating a higher diversity among the generations compared to other professions. <br> Look at the <a href='https://huggingface.co/spaces/society-ethics/Average_diffusion_faces' style='text-decoration: underline;' target='_blank'> Average Diffusion Faces </a> tool for more examples! </p>''')                                              
 
     gr.Markdown("""
-                ## Exploring the Pixel Space of Generated Images
+                ### Exploring the Pixel Space of Generated Images
             """) 
-    with gr.Accordion("Exploring the pixel space of generated images", open=False):
+    with gr.Accordion("Exploring the Pixel Space of Generated Images", open=False):
          gr.HTML('''
          <br>
          <p style="margin-bottom: 14px; font-size: 100%">  With thousands of generated images, we found it useful to provide ways to explore the data in a structured way that did not depend on any external dataset or model. We provide two such tools, one based on <b>colorfulness</b> and one based on a <b>bag-of-visual words</b> model computed using SIFT features.</p>