removing hardcoded aspects

app.py

@@ -24,12 +24,16 @@ def get_image_data(image_name):
     if (image_name == "imagenet_diego.png"): 
         image = get_dataset_by_name("imagenet_diego", get_label=False)
         model_and_data = process_imagenet_get_model(image)
+    elif (image_name == "mnist_9.png"): 
+        image = get_dataset_by_name(image_name, get_label=False)
+        model_and_data = process_mnist_get_model(image)
+    
 
     return image, model_and_data
 
 
 def segmentation_generation(image_name, c_width, n_top, n_gif_imgs):
-    print("GRADIO INPUTS:", image_name, c_width, n_top, n_gif_imgs)
+    print("Inputs Received:", image_name, c_width, n_top, n_gif_imgs)
 
     cred_width = c_width
     n_top_segs = n_top

bayes/models.py

@@ -39,7 +39,6 @@ def get_xtrain(segs):
 
 def process_imagenet_get_model(data):
     """Gets wrapped imagenet model."""
-    print("DATA RECEIVED FROM APP:\n", data)
     # Get the vgg16 model, used in the experiments
     model = models.vgg16(pretrained=True)
     model.eval()

image_posterior.py

@@ -64,10 +64,10 @@ def create_gif(explanation_blr, img_name, segments, image, n_images=20, n_max=5)
             plt.imshow(c_image, alpha=0.3)
             paths.append(os.path.join(tmpdirname, f"{i}.png"))
             plt.savefig(paths[-1])
-        print("CREATING GIF NOW")
+    
         # Save to gif
         # https://stackoverflow.com/questions/61716066/creating-an-animation-out-of-matplotlib-pngs
-        # print(f"Saving gif to {save_loc}")
+        print(f"Saving gif to {img_name}_explanation.gif")
 
         ims = [imageio.imread(f) for f in paths]
         imageio.mimwrite(f'{img_name}_explanation.gif', ims)