adding explanatory text

app.py

@@ -44,7 +44,7 @@ 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 data generated from [DiffusionBiasExplorer](https://huggingface.co/spaces/society-ethics/DiffusionBiasExplorer).")
+    gr.Markdown("## This Space lets you explore the clusters based on the data generated from [DiffusionBiasExplorer](https://huggingface.co/spaces/society-ethics/DiffusionBiasExplorer).")
     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, label="How many clusters do you want to make from the data?")
 
@@ -61,6 +61,9 @@ with gr.Blocks(title=TITLE) as demo:
                 b = gr.Plot(label="Gender label makeup of cluster")
             with gr.Column(scale=2):
                 d = gr.Plot(label="Ethnicity label makeup of cluster")
+    gr.Markdown(f"The 'Model makeup' plot corresponds to the number of images from the cluster that come from each of the TTI systems that we are comparing: Dall-E 2, Stable Diffusion v.1.4. and Stable Diffusion v.2.")
+    gr.Markdown(f"The 'Gender label makeup' plot corresponds to the number of images from each of the genders used in the prompts: male, female, non-binary and unmarked ('person')")
+    gr.Markdown(f"The 'Ethnicity label makeup' plot corresponds to the number of images from each of the 18 ethnicities used in the prompts. A blank value means unmarked ethnicity")
     demo.load(fn=show_cluster, inputs=[cluster_id, num_clusters], outputs=[a,b,c,d, gallery])
     num_clusters.change(fn=show_cluster, inputs=[cluster_id, num_clusters], outputs=[a,b,c,d, gallery])
     cluster_id.change(fn=show_cluster, inputs=[cluster_id, num_clusters], outputs=[a,b,c,d, gallery])