Update app.py

app.py

@@ -999,30 +999,34 @@ with ui.navset_card_tab(id="tab"):
                     multiple=True,
                     selected=["compliment", "cross_entropy", "headless"]
                 )
-            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 = []
-                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'].astype('float').values
-                            print(y)
+        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 = []
+            labels = []
+        
+            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'].astype('float').values
+                        print(y)
+                        
+                        if len(y) > 0:
                             f = interp1d(np.linspace(0, 1, len(y)), y)
                             loss_rates.append(f(x))
-                            labels.append(str(param_type) +'_'+loss_type +'_'+model_type)
-                fig, ax = plt.subplots()
-                print(loss_rates)
-                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 parameter model across context windows')
-                ax.set_xlabel('Training steps')
-                ax.set_ylabel('Loss rate')
-                return fig
+                            labels.append(str(param_type) + '_' + loss_type + '_' + model_type)
+        
+            fig, ax = plt.subplots()
+            print(loss_rates)
+        
+            for i, loss_rate in enumerate(loss_rates):
+                ax.plot(x, loss_rate, label=labels[i])
+        
+            ax.legend()
+            ax.set_xlabel('Training steps')
+            ax.set_ylabel('Loss rate')
+        
+            return fig
         
         import matplotlib as mpl
         @render.plot()