having a hard time filtering the data'

app.py

@@ -29,6 +29,12 @@ models = {
     "DallE": "Dall-E 2",
 }
 
+df_models = {
+    "All Models": "All",
+    "Stable Diffusion 1.4" : "SD_14",
+    "Stable Diffusion 2": "SD_2",
+    "Dall-E 2": "DallE",
+}
 
 def make_profession_plot(num_clusters, prof_name):
     pre_pandas = dict(
@@ -59,33 +65,12 @@ def make_profession_plot(num_clusters, prof_name):
     prof_plot = df.plot(kind="bar", barmode="group")
     return prof_plot
 
-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
+def make_profession_table(num_clusters, prof_names, mod_names):
+    cl_dct = clusters_by_size[num_clusters]
+    cl_df = pd.DataFrame.from_dict(cl_dct)
+    print(mod_names)
+    mod_df = pd.DataFrame(cl_df[df_models[mod_names]])
+    return mod_df
 
 
 with gr.Blocks() as demo:
@@ -101,7 +86,7 @@ with gr.Blocks() as demo:
                     value=12,
                     label="How many clusters do you want to use to represent identities?",
                 )
-                model_choices = gr.Dropdown(list(models.values()), value='All Models', label="Which models do you want to compare?", multiselect=True, interactive= True)
+                model_choices = gr.Dropdown(["All Models", "Stable Diffusion 1.4", "Stable Diffusion 2", "Dall-E 2"], value="All Models", label="Which models do you want to compare?", 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("")
@@ -114,9 +99,9 @@ with gr.Blocks() as demo:
                 table = gr.DataFrame(
                     label="Profession assignment per cluster"
                 )
-                profession_choices_1.change(
+                num_clusters.change(
                     make_profession_table,
-                    [num_clusters, profession_choices_1],
+                    [num_clusters, profession_choices_1,model_choices],
                     table,
                     queue=False,
                 )
@@ -139,12 +124,12 @@ with gr.Blocks() as demo:
                 plot = gr.Plot(
                     label=f"Makeup of the cluster assignments for profession {profession_choice}"
                 )
-                profession_choice.change(
-                    make_profession_plot,
-                    [num_clusters, profession_choice],
-                    plot,
-                    queue=False,
-                )
+                #profession_choice.change(
+                #    make_profession_plot,
+                #    [num_clusters, profession_choice],
+                #    plot,
+                #    queue=False,
+               # )
         with gr.Row():
             gr.Markdown("TODO: show examplars for cluster")