model -> system

app.py

@@ -100,11 +100,11 @@ with gr.Blocks(title=TITLE) as demo:
     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.")
+        "### This demo showcases patterns in the images generated from different prompts input to Stable Diffusion and Dalle-2 TTI systems.")
     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>""")
+        """<span style="color:red" font-size:smaller>⚠️ DISCLAIMER: the images displayed by this tool were generated by text-to-image systems 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?")
 
@@ -120,7 +120,7 @@ with gr.Blocks(title=TITLE) as demo:
             a = gr.Text(label="Number of images")
     with gr.Row():
         with gr.Column(scale=1):
-            c = gr.Plot(label="How many images from each model?")
+            c = gr.Plot(label="How many images from each system?")
             c_desc = gr.HTML(label="")
         with gr.Column(scale=1):
             b = gr.Plot(label="How many gender terms are represented?")
@@ -129,7 +129,7 @@ with gr.Blocks(title=TITLE) as demo:
             d = gr.Plot(label="Which ethnicity terms are present?")
 
     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.")
+        f"The 'System 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(
         'The Gender plot shows the number of images based on the input prompts that used the words man, woman, non-binary person, and unmarked, which we label "person".')
     gr.Markdown(