Update app.py

app.py

@@ -117,9 +117,9 @@ with gr.Blocks() as demo:
     gr.Markdown("""
     <center><h1> Product Recommendation using Image Similarity </h1></center>
     
-    <center>by <a href="https://www.tonyassi.com/" target="_blank">Tony Assi</a></center>
+    <center>by <a href="https://www.tonyassi.com/" target="_blank">Tony Assi</a></center><br>
     
-    <center> This is a demo of product recommendation using image similarity of user preferences. <a href="https://huggingface.co/blog/tonyassi/product-recommendation-using-image-similarity/" target="_blank">Read the article.</a></center> <br>
+    <center><i> This is a demo of product recommendation using image similarity of user preferences. </i><a href="https://huggingface.co/blog/tonyassi/product-recommendation-using-image-similarity/" target="_blank">Read the article.</a></center> <br>
 
 
     The the user selects their favorite product which then gets added to the user preference group. Each of the image embeddings in the user preference products get averaged into a preference embedding. Each round some products are displayed: 5 products most similar to user preference embedding and 5 random products. Embeddings are generated with [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224). The dataset used is [tonyassi/finesse1-embeddings](https://huggingface.co/datasets/tonyassi/finesse1-embeddings).