Update app.py

app.py

@@ -35,57 +35,62 @@ def generate_plots(min_slider_samples_range,max_slider_samples_range):
           oa_mse[i, j] = oa.error_norm(real_cov, scaling=False)
           oa_shrinkage[i, j] = oa.shrinkage_
 
-       
-  # plot MSE
-  plt.clf()
-  plt.subplot(2, 1, 1)
-  plt.errorbar(
-      slider_samples_range,
-      lw_mse.mean(1),
-      yerr=lw_mse.std(1),
-      label="Ledoit-Wolf",
-      color="navy",
-      lw=2,
-  )
-  plt.errorbar(
-      slider_samples_range,
-      oa_mse.mean(1),
-      yerr=oa_mse.std(1),
-      label="OAS",
-      color="darkorange",
-      lw=2,
-  )
-  plt.ylabel("Squared error")
-  plt.legend(loc="upper right")
-  plt.title("Comparison of covariance estimators")
-  plt.xlim(5, 31)
-
-  # plot shrinkage coefficient
-  plt.subplot(2, 1, 2)
-  plt.errorbar(
+
+
+ def plot_mse():
+     # plot MSE
+     plt.clf()
+     plt.subplot(2, 1, 1)
+     plt.errorbar(
+          slider_samples_range,
+          lw_mse.mean(1),
+          yerr=lw_mse.std(1),
+          label="Ledoit-Wolf",
+          color="navy",
+          lw=2,
+      )
+      plt.errorbar(
+          slider_samples_range,
+          oa_mse.mean(1),
+          yerr=oa_mse.std(1),
+          label="OAS",
+          color="darkorange",
+          lw=2,
+      )
+      plt.ylabel("Squared error")
+      plt.legend(loc="upper right")
+      plt.title("Comparison of covariance estimators")
+      plt.xlim(5, 31)
+      return plt  
+
+
+def plot_shrinkage():
+    # plot shrinkage coefficient
+    plt.subplot(2, 1, 2)
+    plt.errorbar(
       slider_samples_range,
       lw_shrinkage.mean(1),
       yerr=lw_shrinkage.std(1),
       label="Ledoit-Wolf",
       color="navy",
       lw=2,
-  )
-  plt.errorbar(
+      )
+    plt.errorbar(
       slider_samples_range,
       oa_shrinkage.mean(1),
       yerr=oa_shrinkage.std(1),
       label="OAS",
       color="darkorange",
       lw=2,
-  )
-  plt.xlabel("n_samples")
-  plt.ylabel("Shrinkage")
-  plt.legend(loc="lower right")
-  plt.ylim(plt.ylim()[0], 1.0 + (plt.ylim()[1] - plt.ylim()[0]) / 10.0)
-  plt.xlim(5, 31)
+      )
+    plt.xlabel("n_samples")
+    plt.ylabel("Shrinkage")
+    plt.legend(loc="lower right")
+    plt.ylim(plt.ylim()[0], 1.0 + (plt.ylim()[1] - plt.ylim()[0]) / 10.0)
+    plt.xlim(5, 31)
 
-  # plt.show()
-  return plt
+    # plt.show()
+    return plt
 
 
 
@@ -118,8 +123,7 @@ with gr.Blocks(title=title, theme=gr.themes.Default(font=[gr.themes.GoogleFont("
     
 
     r = 0.1
-    changed1 = False
-    changed2 = False
+
     real_cov = toeplitz(r ** np.arange(n_features))
     coloring_matrix = cholesky(real_cov)
     gr.Markdown(" **[Demo is based on sklearn docs](https://scikit-learn.org/stable/auto_examples/covariance/plot_lw_vs_oas.html)**")
@@ -128,12 +132,12 @@ with gr.Blocks(title=title, theme=gr.themes.Default(font=[gr.themes.GoogleFont("
     # output = gr.Textbox(label="Output Box")
     # greet_btn.click(fn=greet, inputs=name, outputs=output)
     gr.Label(value="Comparison of Covariance Estimators")
+    generate_plots()
     #if min_slider_samples_range:
-    while not (changed1 and changed2):
-        min_slider_samples_range.change(generate_plots, inputs=[min_slider_samples_range,max_slider_samples_range], outputs= gr.Plot() )
-        max_slider_samples_range.change(generate_plots, inputs=[min_slider_samples_range,max_slider_samples_range], outputs= gr.Plot() )
-        changed1 = True
-        changed2 = True
+
+    min_slider_samples_range.change(plot_mse, outputs= gr.Plot() )
+    max_slider_samples_range.change(plot_shrinkage, outputs= gr.Plot() )
+
 
 
     #elif max_slider_samples_range: