Update app.py

app.py

@@ -1000,27 +1000,27 @@ with ui.navset_card_tab(id="tab"):
                     selected='compliment'
                 )
             def plot_loss_rates(df, param_types, loss_types, model_types): 
-            # interplot each column to be same number of points
-            x = np.linspace(0, 1, 1000)
-            loss_rates = []
-            labels = []
-            #drop the column step
-            df = df.drop(columns=['Step'])
-            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
-                        f = interp1d(np.linspace(0, 1, len(y)), y)
-                        loss_rates.append(f(x))
-                        labels.append(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_xlabel('Training steps')
-            ax.set_ylabel('Loss rate')
-            return fig
+                # interplot each column to be same number of points
+                x = np.linspace(0, 1, 1000)
+                loss_rates = []
+                labels = []
+                #drop the column step
+                df = df.drop(columns=['Step'])
+                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
+                            f = interp1d(np.linspace(0, 1, len(y)), y)
+                            loss_rates.append(f(x))
+                            labels.append(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_xlabel('Training steps')
+                ax.set_ylabel('Loss rate')
+                return fig
         
         import matplotlib as mpl
         @render.plot()