Update app.py

app.py

@@ -1005,7 +1005,7 @@ with ui.navset_card_tab(id="tab"):
                     selected=["compliment", "cross_entropy", "headless"]
                 )
             ui.input_slider("x_filter", "x_filter", 0, 1, 0.01)
-        def plot_loss_rates_model(df, param_types, loss_types, model_types, x_filter):
+        def plot_loss_rates_model(df, param_types, loss_types, model_types):
             # interplot each column to be same number of points
             x = np.linspace(0, 1, 1000)
             loss_rates = []
@@ -1026,16 +1026,14 @@ with ui.navset_card_tab(id="tab"):
           #  print(loss_rates)
         
             for i, loss_rate in enumerate(loss_rates):
-                df_madmad = pd.DataFrame({'x':x, 'loss':loss_rate})
+                # df_madmad = pd.DataFrame({'x':x, 'loss':loss_rate})
         
-                # df_madmad = df_madmad.sort_values(by='x')
-                df_madmad = df_madmad[df_madmad['x']>x_filter]
-                x = df_madmad['x'].to_list()
-                loss_rate = df_madmad['loss'].to_list()
-                try: 
-                    ax.plot(x, loss_rate, label=labels[i])
-                except: 
-                    return fig
+                # # df_madmad = df_madmad.sort_values(by='x')
+                # df_madmad = df_madmad[df_madmad['x']>x_filter]
+                # x = df_madmad['x'].to_list()
+                # loss_rate = df_madmad['loss'].to_list(
+                ax.plot(x, loss_rate, label=labels[i])
+       
         
             ax.legend()
             ax.set_xlabel('Training steps')
@@ -1048,8 +1046,9 @@ with ui.navset_card_tab(id="tab"):
         def plot_model_scaling():
             fig = None
             df = pd.read_csv('training_data_5.csv')
+            df = df[df['epoch_interp']>0.035]
             mpl.rcParams.update(mpl.rcParamsDefault)
-            fig = plot_loss_rates_model(df, input.param_type(),input.loss_type(),input.model_type(),input.x_filter() )
+            fig = plot_loss_rates_model(df, input.param_type(),input.loss_type(),input.model_type() )
             
             import tempfile
             fd, path = tempfile.mkstemp(suffix = '.svg')