minor updates

app.py

@@ -82,7 +82,26 @@ def get_activations(intermediate_model, image: list,
     
     return output, in_image, activation_1, activation_2
 
-def plot_activations(activation_1, activation_2, origin='lower'):
+def plot_input(input_image: list, origin='lower'):
+    
+    ##### make the figure for the input image #####
+    plt.rcParams['xtick.labelsize'] = ticks
+    plt.rcParams['ytick.labelsize'] = ticks
+    
+    input_fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(19, 8))
+    
+    im0 = ax.imshow(input_image, cmap=cmap,
+                     origin=origin)    
+    
+    divider = make_axes_locatable(ax)
+    cax = divider.append_axes('right', size='5%', pad=0.05)
+    input_fig.colorbar(im0, cax=cax, orientation='vertical')
+        
+    ax.set_title('Input', fontsize=titles)
+    
+    return input_fig
+
+def plot_activations(activation_1: list, activation_2: list, origin='lower'):
     
     
      ##### Make the activation figure ######
@@ -161,6 +180,9 @@ def predict_and_analyze(model_name, num_channels, dim, image):
     
     origin = 'lower'
     
+    # plot input image
+    input_fig = plot_input(input_image, origin=origin)
+    
     # plot mean subtracted activations
     fig1 = plot_activations(activation_1, activation_2, origin=origin)
     
@@ -170,22 +192,6 @@ def predict_and_analyze(model_name, num_channels, dim, image):
     activation_2 = normalize_array(activation_2)
     fig2 = plot_activations(activation_1, activation_2, origin=origin)
     
-    
-    ##### make the figure for the input image #####
-    plt.rcParams['xtick.labelsize'] = ticks
-    plt.rcParams['ytick.labelsize'] = ticks
-    
-    input_fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(19, 8))
-    
-    im0 = ax.imshow(input_image, cmap=cmap,
-                     origin=origin)    
-    
-    divider = make_axes_locatable(ax)
-    cax = divider.append_axes('right', size='5%', pad=0.05)
-    input_fig.colorbar(im0, cax=cax, orientation='vertical')
-        
-    ax.set_title('Input', fontsize=titles)
-    
     print("Sending to Hugging Face")
     
     return output, input_fig, fig1, fig2