Fleshing out descriptions

app.py

@@ -17,15 +17,26 @@ clusters_by_size = {
     48: clusters_48,
 }
 
+def to_string(label):
+    if label == "SD_2":
+        label = "Stable Diffusion 2"
+    elif label == "SD_14":
+        label = "Stable Diffusion 14"
+    elif label == "DallE":
+        label = "Dall-E 2"
+    return label
+
 def describe_cluster(cl_dict, block="label"):
     labels_values = sorted(cl_dict.items(), key=operator.itemgetter(1))
     labels_values.reverse()
     total = float(sum(cl_dict.values()))
     lv_prcnt = list((item[0], round(item[1] * 100/total, 0)) for item in labels_values)
-    description_string = "The most represented %s is %s, making up about %d%% of the cluster.\n" % (block, lv_prcnt[0][0], lv_prcnt[0][1])
-    description_string += "This is followed by: "
-    for label_value_tuple in lv_prcnt[1:]:
-        description_string += "\n%s: %d%%" % label_value_tuple
+    top_label = lv_prcnt[0][0]
+    description_string = "<span>The most represented %s is <b>%s</b>, making up about %d%% of the cluster.</span>" % (block, to_string(lv_prcnt[0][0]), lv_prcnt[0][1])
+    description_string += "<p>This is followed by: "
+    for lv in lv_prcnt[1:]:
+        description_string += "<BR/><b>%s:</b> %d%%" % (to_string(lv[0]), lv[1])
+    description_string += "</p>"
     return description_string
 
 def show_cluster(cl_id, num_clusters):
@@ -60,9 +71,11 @@ def show_cluster(cl_id, num_clusters):
 
 with gr.Blocks(title=TITLE) as demo:
     gr.Markdown(f"# {TITLE}")
-    gr.Markdown("## This Space lets you explore the clusters based on the data generated from [DiffusionBiasExplorer](https://huggingface.co/spaces/society-ethics/DiffusionBiasExplorer).")
+    gr.Markdown("## Explore the data generated from [DiffusionBiasExplorer](https://huggingface.co/spaces/society-ethics/DiffusionBiasExplorer)!")
+    gr.Markdown("### This demo showcases patterns in the images generated from different prompts input to Stable Diffusion and Dalle-2 diffusion models.")
+    gr.Markdown("### Below, see results on how the images from different prompts cluster together.")
     gr.HTML("""<span style="color:red" font-size:smaller>⚠️ DISCLAIMER: the images displayed by this tool were generated by text-to-image models and may depict offensive stereotypes or contain explicit content.</span>""")
-    num_clusters = gr.Radio([12,24,48], value=12, labels="How many clusters do you want to make from the data?")
+    num_clusters = gr.Radio([12,24,48], value=12, label="How many clusters do you want to make from the data?")
 
     
     with gr.Row():
@@ -74,10 +87,10 @@ with gr.Blocks(title=TITLE) as demo:
     with gr.Row():
             with gr.Column(scale=1):
                 c = gr.Plot(label="How many images from each model?")
-                c_desc = gr.Text(label="")
+                c_desc = gr.HTML(label="")
             with gr.Column(scale=1):
                 b = gr.Plot(label="How many genders are represented?")
-                b_desc = gr.Text(label="")
+                b_desc = gr.HTML(label="")
             with gr.Column(scale=2):
                 d = gr.Plot(label="Which ethnicities are present?")