trying to make things work

app.py

@@ -30,7 +30,7 @@ models = {
 }
 
 
-def make_profession_table(num_clusters, prof_name):
+def make_profession_plot(num_clusters, prof_name):
     pre_pandas = dict(
         [
             (
@@ -59,10 +59,34 @@ def make_profession_table(num_clusters, prof_name):
     prof_plot = df.plot(kind="bar", barmode="group")
     return prof_plot
 
-def gen_df(num_clusters,model,profs):
-    if num_clusters == 12:
-        print(clusters_12.head())
-    
+def make_profession_table(num_clusters, prof_name):
+    pre_pandas = dict(
+        [
+            (
+                models[mod_name],
+                dict(
+                    (
+                        f"Cluster {k}",
+                        clusters_by_size[num_clusters][mod_name][prof_name][
+                            "cluster_proportions"
+                        ][k],
+                    )
+                    for k, v in sorted(
+                        clusters_by_size[num_clusters]["All"][prof_name][
+                            "cluster_proportions"
+                        ].items(),
+                        key=lambda x: x[1],
+                        reverse=True,
+                    )
+                    if v > 0
+                ),
+            )
+            for mod_name in models
+        ]
+    )
+    df = pd.DataFrame.from_dict(pre_pandas)
+    return df
+
 
 with gr.Blocks() as demo:
     gr.Markdown("# 🤗 Diffusion Cluster Explorer")
@@ -70,22 +94,32 @@ with gr.Blocks() as demo:
     with gr.Tab("Professions Overview"):
         gr.Markdown("TODO")
         with gr.Row():
-            with gr.Column():
-                gr.Markdown("Select your settings below:")
+            with gr.Column(scale=1):
+                gr.Markdown("Select the parameters here:")
                 num_clusters = gr.Radio(
                     [12, 24, 48],
                     value=12,
                     label="How many clusters do you want to use to represent identities?",
                 )
-            with gr.Column():
+                model_choices = gr.Dropdown(list(models.values()), value='All Models', label="Which models do you want to compare?", multiselect=True, interactive= True)
+                profession_choices_1 = gr.Dropdown(professions, value=["CEO", "social worker"], label= "Which professions do you want to compare?", multiselect=True, interactive=True)
+            with gr.Column(scale=3):
                 gr.Markdown("")
                 order = gr.Dropdown(
                     ["entropy", "cluster/sum of clusters"],
-                    value="entryopy",
+                    value="entropy",
                     label="Order rows by:",
                     interactive=True,
                 )
-    #        with gr.Row():
+                table = gr.DataFrame(
+                    label="Profession assignment per cluster"
+                )
+                profession_choices_1.change(
+                    make_profession_table,
+                    [num_clusters, profession_choices_1],
+                    table,
+                    queue=False,
+                )
 
     #        with gr.Accordion("Tag Frequencies", open=False):
 
@@ -106,7 +140,7 @@ with gr.Blocks() as demo:
                     label=f"Makeup of the cluster assignments for profession {profession_choice}"
                 )
                 profession_choice.change(
-                    make_profession_table,
+                    make_profession_plot,
                     [num_clusters, profession_choice],
                     plot,
                     queue=False,