remove extraneous stuff

app.py

@@ -7,15 +7,6 @@ pd.options.plotting.backend = "plotly"
 
 
 TITLE = "Diffusion Faces Cluster Explorer"
-clusters_12 = pd.read_json("clusters/professions_to_clusters_12.json")
-clusters_24 = pd.read_json("clusters/professions_to_clusters_24.json")
-clusters_48 = pd.read_json("clusters/professions_to_clusters_48.json")
-
-clusters_by_size = {
-    12: clusters_12,
-    24: clusters_24,
-    48: clusters_48,
-}
 
 clusters_dicts = dict(
     (num_cl, json.load(open(f"clusters/professions_to_clusters_{num_cl}.json")))
@@ -81,9 +72,6 @@ def make_profession_table(num_clusters, prof_names, mod_name, max_cols=8):
         )
         for prof_name in prof_names
     ]
-    from pprint import pprint
-
-    pprint(professions_list_clusters)
     totals = sorted(
         [
             (
@@ -121,7 +109,6 @@ def make_profession_table(num_clusters, prof_names, mod_name, max_cols=8):
         for prof_name, prof_clusters in professions_list_clusters
     ]
     clusters_df = pd.DataFrame.from_dict(prof_list_pre_pandas)
-    print("I'm fine")
     return (
         clusters_df.style.background_gradient(
             axis=None, vmin=0, vmax=100, cmap="YlGnBu"
@@ -131,35 +118,6 @@ def make_profession_table(num_clusters, prof_names, mod_name, max_cols=8):
     )
 
 
-def make_profession_table_df(num_clusters, prof_names, mod_name):
-    cl_df = clusters_by_size[num_clusters]
-    clusters_df = (
-        cl_df[df_models[mod_name]]
-        .apply(pd.Series)
-        .loc[prof_names]["cluster_proportions"]
-        .apply(pd.Series)
-        .reset_index()
-        .rename(columns={"index": "profession"})
-        .round(1)
-    )
-    labor_df = (
-        cl_df[df_models[mod_name]]
-        .apply(pd.Series)
-        .loc[prof_names]["labor_fm"]
-        .apply(pd.Series)
-        .rename(columns={0: "woman", 1: "male"})
-        .reset_index()
-        .rename(columns={"index": "profession"})
-        .round(1)
-    )
-    return (
-        clusters_df.style.background_gradient(cmap="YlGnBu").format(precision=1),
-        labor_df.style.background_gradient(cmap="coolwarm").to_html(),
-    )
-
-
-#    return clusters_df.style.background_gradient(axis=None, vmin=0, vmax=100, cmap="YlGnBu").format(precision=1), labor_df.style.background_gradient(cmap='coolwarm').to_html()
-
 
 with gr.Blocks() as demo:
     gr.Markdown("# 🤗 Diffusion Cluster Explorer")