adding Meg's function

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import json
 import numpy as np
 import pandas as pd
+import operator
 
 pd.options.plotting.backend = "plotly"
 
@@ -30,6 +31,21 @@ df_models = {
 }
 
 
+def describe_cluster(cl_dict, block="label"):
+    labels_values = sorted(cl_dict.items(), key=operator.itemgetter(1))
+    labels_values.reverse()
+    total = float(sum(cl_dict.values()))
+    lv_prcnt = list(
+        (item[0], round(item[1] * 100 / total, 0)) for item in labels_values)
+    top_label = lv_prcnt[0][0]
+    description_string = "<span>The most represented %s is <b>%s</b>, making up about <b>%d%%</b> of the cluster.</span>" % (
+    to_string(block), to_string(top_label), lv_prcnt[0][1])
+    description_string += "<p>This is followed by: "
+    for lv in lv_prcnt[1:]:
+        description_string += "<BR/><b>%s:</b> %d%%" % (to_string(lv[0]), lv[1])
+    description_string += "</p>"
+    return description_string
+
 def make_profession_plot(num_clusters, prof_name):
     print("-------------")
     print(num_clusters, prof_name)
@@ -163,6 +179,7 @@ with gr.Blocks() as demo:
                         queue=False,
                     )
 
+
     #        with gr.Accordion("Tag Frequencies", open=False):
 
     with gr.Tab("Profession Focus"):