Update app.py

app.py

@@ -1036,6 +1036,64 @@ with ui.navset_card_tab(id="tab"):
             mpl.rcParams.update(mpl.rcParamsDefault)
             fig = plot_loss_rates_model(df, input.param_type(),input.loss_type(),input.model_type())
             return fig
+    with ui.nav_panel("Scaling Laws"):
+        ui.page_opts(fillable=True)
+        ui.panel_title("Params & Losses")
+    
+        with ui.card():
+
+            ui.input_selectize(
+                    "model_type",
+                    "Select Model Type:",
+                    ["pythia", "denseformer", "evo"],
+                    multiple=True,
+                    selected=['evo','denseformer']
+                )
+            ui.input_selectize(
+                    "loss_type",
+                    "Select Loss Type:",
+                    ["compliment", "cross_entropy", "headless", "2d", "2d_representation_MSEPlusCE"],
+                    multiple=False,
+                    selected="cross_entropy"
+                )
+        def plot_loss_rates_model_scale(df, loss_type, model_types):
+            df = df[df['loss_type'] == loss_type]
+            # interplot each column to be same number of points
+            params = []
+            loss_rates = []
+            labels = []
+            for model_type in model_types:
+                df_new = df[df['model_type']]
+                losses = []
+                params_model = []
+                for paramy in df_new['num_params'].unique():
+                        loss = df_new[df_new['num_params']==paramy].min()
+                        par = int(paramy)
+                        losses.append(loss)
+                        params_model.append(par)
+                loss_rates.append(losses)
+                params.append(params_model)
+                labels.append(model_type)
+        
+            fig, ax = plt.subplots()
+        
+            for i, loss_rate in enumerate(loss_rates):
+                ax.plot(x=params[i], y=loss_rate, label=labels[i])
+        
+            ax.legend()
+            ax.set_xlabel('Params')
+            ax.set_ylabel('Loss')
+        
+            return fig
+        
+        import matplotlib as mpl
+        @render.plot()
+        def plot_loss_rates_model_scale():
+            fig = None
+            df = pd.read_csv('training_data_5.csv')
+            mpl.rcParams.update(mpl.rcParamsDefault)
+            fig = plot_loss_rates_model(df,input.loss_type(),input.model_type())
+            return fig
         # @output
         # @render.plot
         # def plot_training_loss():