remove extraneous model selection

app.py

@@ -14,14 +14,22 @@ TITLE = "Diffusion Professions Cluster Explorer"
 professions_dset = load_from_disk("professions")
 professions_df = professions_dset.to_pandas()
 
+
 def get_image(model, fname):
-    return professions_dset.select(professions_df[(professions_df["image_path"]==fname) & (professions_df["model"]==model)].index)["image"][0]
+    return professions_dset.select(
+        professions_df[
+            (professions_df["image_path"] == fname) & (professions_df["model"] == model)
+        ].index
+    )["image"][0]
+
 
 clusters_dicts = dict(
     (num_cl, json.load(open(f"clusters/professions_to_clusters_{num_cl}.json")))
     for num_cl in [12, 24, 48]
 )
 
+cluster_summaries_by_size = json.load(open("clusters/cluster_summaries_by_size.json"))
+
 prompts = pd.read_csv("promptsadjectives.csv")
 professions = list(sorted([p.lower() for p in prompts["Occupation-Noun"].tolist()]))
 models = {
@@ -60,6 +68,17 @@ def describe_cluster(num_clusters, block="label"):
 
 
 def make_profession_plot(num_clusters, prof_name):
+    sorted_cl_scores = [
+        (k, v)
+        for k, v in sorted(
+            clusters_dicts[num_clusters]["All"][prof_name][
+                "cluster_proportions"
+            ].items(),
+            key=lambda x: x[1],
+            reverse=True,
+        )
+        if v > 0
+    ]
     pre_pandas = dict(
         [
             (
@@ -71,14 +90,7 @@ def make_profession_plot(num_clusters, prof_name):
                             "cluster_proportions"
                         ][k],
                     )
-                    for k, v in sorted(
-                        clusters_dicts[num_clusters]["All"][prof_name][
-                            "cluster_proportions"
-                        ].items(),
-                        key=lambda x: x[1],
-                        reverse=True,
-                    )
-                    if v > 0
+                    for k, _ in sorted_cl_scores
                 ),
             )
             for mod_name in models
@@ -86,7 +98,9 @@ def make_profession_plot(num_clusters, prof_name):
     )
     df = pd.DataFrame.from_dict(pre_pandas)
     prof_plot = df.plot(kind="bar", barmode="group")
-    return prof_plot
+    return prof_plot, gr.update(
+        choices=[k for k, _ in sorted_cl_scores], value=sorted_cl_scores[0][0]
+    )
 
 
 def make_profession_table(num_clusters, prof_names, mod_name, max_cols=8):
@@ -145,12 +159,12 @@ def make_profession_table(num_clusters, prof_names, mod_name, max_cols=8):
     )
 
 
-def show_examplars(num_clusters, prof_name, mod_name, cl_id):
-    examplars_dict = clusters_dicts[num_clusters][df_models[mod_name]][prof_name][
+def show_examplars(num_clusters, prof_name, cl_id):
+    examplars_dict = clusters_dicts[num_clusters]["All"][prof_name][
         "cluster_examplars"
     ][str(cl_id)]
     l = list(chain(*[examplars_dict[k] for k in examplars_dict]))
-    return [get_image(model,fname) for _,model,fname in l]
+    return [get_image(model, fname) for _, model, fname in l]
 
 
 with gr.Blocks(title=TITLE) as demo:
@@ -222,17 +236,6 @@ with gr.Blocks(title=TITLE) as demo:
                 gr.Markdown(
                     "You can show examples of profession images assigned to each cluster:"
                 )
-                model_choices_focus = gr.Dropdown(
-                    [
-                        "All Models",
-                        "Stable Diffusion 1.4",
-                        "Stable Diffusion 2",
-                        "Dall-E 2",
-                    ],
-                    value="All Models",
-                    label="Select generation model:",
-                    interactive=True,
-                )
                 cluster_id_focus = gr.Dropdown(
                     choices=[i for i in range(num_clusters_focus.value)],
                     value=0,
@@ -245,38 +248,36 @@ with gr.Blocks(title=TITLE) as demo:
                 demo.load(
                     make_profession_plot,
                     [num_clusters_focus, profession_choice_focus],
-                    plot,
+                    [plot, cluster_id_focus],
                     queue=False,
                 )
                 for var in [num_clusters_focus, profession_choice_focus]:
                     var.change(
                         make_profession_plot,
                         [num_clusters_focus, profession_choice_focus],
-                        plot,
+                        [plot, cluster_id_focus],
                         queue=False,
                     )
         with gr.Row():
-            examplars_plot = (
-                gr.Gallery().style(grid=9, height="auto")
-            )
+            examplars_plot = gr.Gallery(
+                label="Profession images assigned to the selected cluster."
+            ).style(grid=5, height="auto")
             demo.load(
                 show_examplars,
                 [
                     num_clusters_focus,
                     profession_choice_focus,
-                    model_choices_focus,
                     cluster_id_focus,
                 ],
                 examplars_plot,
                 queue=False,
             )
-            for var in [model_choices_focus, cluster_id_focus]:
+            for var in [cluster_id_focus]:
                 var.change(
                     show_examplars,
                     [
                         num_clusters_focus,
                         profession_choice_focus,
-                        model_choices_focus,
                         cluster_id_focus,
                     ],
                     examplars_plot,