Update to model name heading

app.py

@@ -42,7 +42,7 @@ def load(repo):
         'chain',
         'sample',
         parameter,
-        'model',
+        'parameter_value',
         'value',
     ]
     dataset = load_dataset(repo)
@@ -60,7 +60,7 @@ def summarize(df, ci=0.95):
         interval = hdi(values, ci)
 
         agg = {
-            'model': i,
+            'parameter_value': i,
             'ability': values.median(),
             'uncertainty': interval.upper - interval.lower,
         }
@@ -68,7 +68,7 @@ def summarize(df, ci=0.95):
 
         return agg
 
-    groups = df.groupby('model', sort=False)
+    groups = df.groupby('parameter_value', sort=False)
     records = it.starmap(_aggregate, groups)
 
     return pd.DataFrame.from_records(records)
@@ -84,7 +84,7 @@ def rank(df, ascending, name='rank'):
     return df.reset_index(names=name)
 
 def compare(df, model_1, model_2):
-    mcol = 'model'
+    mcol = 'parameter_value'
     models = [
         model_1,
         model_2,
@@ -141,7 +141,7 @@ class RankPlotter(DataPlotter):
                   xmax=self.df['upper'],
                   alpha=0.5)
         ax.set_ylabel('')
-        ax.set_yticks(self.y, self.df['model'])
+        ax.set_yticks(self.y, self.df['parameter_value'])
 
 class ComparisonPlotter(DataPlotter):
     def __init__(self, df, model_1, model_2, ci=0.95):
@@ -219,8 +219,9 @@ with gr.Blocks() as demo:
 
                 ''')
             with gr.Column():
-                models = sorted(df['model'].unique(), key=lambda x: x.lower())
-                drops = ft.partial(gr.Dropdown, choices=models)
+                models = df['parameter_value'].unique()
+                choices = sorted(models, key=lambda x: x.lower())
+                drops = ft.partial(gr.Dropdown, choices=choices)
                 inputs = [ drops(label=f'Model {x}') for x in range(1, 3) ]
 
                 button = gr.Button(value='Compare!')