Suggested text updates

app.py

@@ -157,18 +157,15 @@ with gr.Blocks(theme="citrus", fill_width=False) as demo:
         """
     )
 
-    gr.HTML(
-        f"<div id='global' style='font-size:16px;color:var(--body-text-color)'><span style='background-color:#f59e0b;color:black;padding:2px'>{n_unique_countries}</span> countries developing, sending <span style='background-color:#f59e0b;color:black;padding:2px'>{sum_request_count:,}</span> upload requests, and pushing over <span style='background-color:#f59e0b;color:black;padding:2px'>{sum_object_size / 1e+12:.2f}TB</span> to the Hub in 24 hours.</div>"
-    )
-
     gr.Markdown(
-        "Use the slider below to view the data by hour. Select `Upload Size` to see total uploaded size in bytes, or `Requests` to show the cumulative number of requests from each country."
+        "The [Xet team's](https://huggingface.co/xet-team) backend uses a [content-addressable store (CAS)](https://en.wikipedia.org/wiki/Content-addressable_storage) for efficient deduplication and optimized data storage, making it ideal for Hugging Face Hub's scale. As we re-architect uploads and downloads on the Hub, we are inserting a CAS as the first stop for content distribution. To decide where to deploy our CAS [points of presence](https://docs.aws.amazon.com/whitepapers/latest/aws-fault-isolation-boundaries/points-of-presence.html), we analyzed a 24 hour window of global uploads to the Hub from October 11th, 2024."
     )
 
-    gr.Markdown(
-        "Xet-backed storage uses a [content-addressable store (CAS)](https://en.wikipedia.org/wiki/Content-addressable_storage) as an integral part of its architecture. This enables efficient deduplication and optimized data storage, making it ideal for our needs. As we re-architect uploads and downloads on the Hub, we are inserting a CAS as the first stop for content distribution. To see how uploads are routed to each CAS cluster in our architecture, use the drop-down menu to filter by AWS region. For more details, check out our accompanying blog post."
+    gr.HTML(
+        f"<div id='global' style='color:var(--body-text-color)'>We found <span style='background-color:#f59e0b;color:black;padding:2px'>{sum_request_count:,}</span> upload requests pushing over <span style='background-color:#f59e0b;color:black;padding:2px'>{sum_object_size / 1e+12:.2f} TB</span> from <span style='background-color:#f59e0b;color:black;padding:2px'>{n_unique_countries}</span> countries. Explore the data below by using the slider to view uploads by hour, the buttons to visualize by object size or number of requests, and the dropdown to see how uploads will be routed by AWS region in our new redesigned architecture.</div>"
     )
 
+
     with gr.Row():
         with gr.Group():
             with gr.Column(scale=1):
@@ -213,7 +210,7 @@ with gr.Blocks(theme="citrus", fill_width=False) as demo:
     )
 
     gr.Markdown(
-        "The Pareto chart below shows the top countries by upload size or request count, with a cumulative line indicating the percentage of total upload volume or requests represented by these countries. Like the map above, the values change as you filter by AWS region."
+        "The Pareto chart below shows the top countries by upload size or request count, with a cumulative line indicating the percentage of total upload volume or requests represented by these countries. This chart is filtered by AWS region selected above."
     )
 
     bar_chart = gr.Plot()