Update app.py

app.py

@@ -983,7 +983,7 @@ with ui.navset_card_tab(id="tab"):
                     "Select Param Type:",
                     ["14", "31", "70", "160"],
                     multiple=True,
-                    selected=None
+                    selected=["14"]
                 )
             ui.input_selectize(
                     "model_type",
@@ -1009,15 +1009,15 @@ with ui.navset_card_tab(id="tab"):
                 for param_type in param_types:
                     for loss_type in loss_types:
                         for model_type in model_types:
-                            y = df[df['param_type'] ==param_type & df['loss_type'] == loss_type & df['model_type'] == model_type]['loss'].dropna().astype('float', errors = 'ignore').dropna().values
+                            y = df[df['param_type'] ==param_type && df['loss_type'] == loss_type && df['model_type'] == model_type]['loss'].dropna().astype('float', errors = 'ignore').dropna().values
                             f = interp1d(np.linspace(0, 1, len(y)), y)
                             loss_rates.append(f(x))
-                            labels.append(param_type +'_'+loss_type +'_'+model_type)
+                            labels.append(str(param_type) +'_'+loss_type +'_'+model_type)
                 fig, ax = plt.subplots()
                 for i, loss_rate in enumerate(loss_rates):
                     ax.plot(x, loss_rate, label=labels[i])
                 ax.legend()
-                ax.set_title(f'Loss rates for a {type} parameter model across context windows')
+          #      ax.set_title(f'Loss rates for a parameter model across context windows')
                 ax.set_xlabel('Training steps')
                 ax.set_ylabel('Loss rate')
                 return fig