chore: adding spinner

app.py

@@ -25,25 +25,27 @@ st.image(image, caption="Original Image")
 st.title("Attention Heat Maps")
 
 # Load the DINO model
-dino = from_pretrained_keras("probing-vits/vit-dino-base16")
+with st.spinner('Processing the heat maps. Wait for it 🔥...'):
+	dino = from_pretrained_keras("probing-vits/vit-dino-base16")
 
-# Get the attention scores
-_, attention_score_dict = dino.predict(preprocessed_image)
+	# Get the attention scores
+	_, attention_score_dict = dino.predict(preprocessed_image)
 
-# De-normalize the image for visual clarity. 
-in1k_mean = tf.constant([0.485 * 255, 0.456 * 255, 0.406 * 255])  
-in1k_std = tf.constant([0.229 * 255, 0.224 * 255, 0.225 * 255])  
-preprocessed_img_orig = (preprocessed_image * in1k_std) + in1k_mean
-preprocessed_img_orig = preprocessed_img_orig / 255.
-preprocessed_img_orig = tf.clip_by_value(preprocessed_img_orig, 0.0, 1.0).numpy()
+	# De-normalize the image for visual clarity. 
+	in1k_mean = tf.constant([0.485 * 255, 0.456 * 255, 0.406 * 255])  
+	in1k_std = tf.constant([0.229 * 255, 0.224 * 255, 0.225 * 255])  
+	preprocessed_img_orig = (preprocessed_image * in1k_std) + in1k_mean
+	preprocessed_img_orig = preprocessed_img_orig / 255.
+	preprocessed_img_orig = tf.clip_by_value(preprocessed_img_orig, 0.0, 1.0).numpy()
 
-attentions = utils.attention_heatmap(
-	attention_score_dict=attention_score_dict,
-	image=preprocessed_img_orig
-)
+	attentions = utils.attention_heatmap(
+		attention_score_dict=attention_score_dict,
+		image=preprocessed_img_orig
+	)
+
+	utils.plot(attentions=attentions, image=preprocessed_img_orig)
 
-utils.plot(attentions=attentions, image=preprocessed_img_orig)
+	# Show the attention maps
+	image = Image.open("heat_map.png")
 
-# Show the attention maps
-image = Image.open("heat_map.png")
 st.image(image, caption="Attention Heat Maps")