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Update app.py

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  1. app.py +3 -3
app.py CHANGED
@@ -116,12 +116,12 @@ def select_image(evt: gr.SelectData, gallery, preference_gallery):
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  with gr.Blocks() as demo:
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  gr.Markdown("""
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  <center><h1> Product Recommendation using Image Similarity </h1></center>
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-
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- <center>by <a href="https://huggingface.co/blog/tonyassi/product-recommendation-using-image-similarity/" target="_blank">Read the Article</a></center>
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  <center> This is a demo of product recommendation using image similarity of user preferences. </center> <br>
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- <center>by <a href="https://www.tonyassi.com/" target="_blank">Tony Assi</a></center>
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  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).
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  """)
 
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  with gr.Blocks() as demo:
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  gr.Markdown("""
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  <center><h1> Product Recommendation using Image Similarity </h1></center>
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+
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+ <center>by <a href="https://www.tonyassi.com/" target="_blank">Tony Assi</a></center>
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  <center> This is a demo of product recommendation using image similarity of user preferences. </center> <br>
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+ <center>by <a href="https://huggingface.co/blog/tonyassi/product-recommendation-using-image-similarity/" target="_blank">Read the Article</a></center> <br>
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  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).
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  """)