More test images, tweak description

app.py

@@ -41,7 +41,7 @@ def create_plots( output_tensor, title, fontsize=24 ):
     img_list = [ output_tensor[0][n].detach().numpy() for n in range( output_tensor.shape[1] ) ]
     fig = plt.figure()
     M, N = get_best_layout( len( img_list ), img_list[0].shape )
-    ax = fig.subplots(  M, N, gridspec_kw={ 'wspace':0.01, 'hspace':0.01 } )
+    ax = fig.subplots(  M, N )
     for N0, img in enumerate( img_list ):
         m = N0//N
         n = N0%N
@@ -82,9 +82,13 @@ model = learn.model.eval()
 description='''
 A simple 4-way classifier that categorizes images as 'snake', 'bird', 'otter' or 'forest'. 
 Refined from an initial ResNet18 model downloaded from HuggingFace.
+The test images given here are chosen to demonstrate the effect of lack of training data on the classification outcome. 
 
-**DISCLAIMER**: the images here are merely for demonstration purposes. I don't own any of them 
-and I'm not making money from them.  
+The actual classification for each test image actually takes a very short time; the delay in predicing results here is due to 
+the extra step of platting the intermediate activation maps and inferred features in `matplotlib`. 
+
+
+**DISCLAIMER**: the images here are merely for demonstration purposes. I don't own any of them and I'm not making money from them.  
 '''
 
 # with gr.Blocks() as layout: