tweaking some words

README.md

@@ -11,7 +11,7 @@ license: mit
 short_description: An analysis of LFS files on the Hub.
 ---
 
-Running this locally is easiest with Poetry installed.
+Running this locally is easiest with [Poetry installed](https://python-poetry.org/).
 
 Clone the repository and run:
 

app.py

@@ -4,8 +4,16 @@ from plotly import graph_objects as go
 import plotly.io as pio
 import plotly.express as px
 
+# @TODO: Add a custom template to the plotly figure
+"""
+pio.templates["custom"] = go.layout.Template()
+pio.templates["custom"].layout = dict(
+    plot_bgcolor="#bde5ec", paper_bgcolor="#bbd5da"
+)
+
 # Set the default theme to "plotly_dark"
-pio.templates.default = "plotly_dark"
+pio.templates.default = "custom"
+"""
 
 
 def process_dataset():
@@ -222,7 +230,7 @@ def plot_total_sum(by_type_arr):
 
     # Update layout
     fig.update_layout(
-        title="Top 20 File Extensions by Total Size",
+        title="Top 20 File Extensions by Total Size (in PBs)",
         xaxis_title="File Extension",
         yaxis_title="Total Size (PBs)",
         yaxis=dict(tickformat=".2f"),  # Format y-axis labels to 2 decimal places
@@ -292,7 +300,7 @@ def area_plot_by_extension_month(_df):
     fig = px.area(_df, x="date", y="total_size", color="extension")
     # Update layout
     fig.update_layout(
-        title="File Extension Cumulative Growth (in PBs) Over Time",
+        title="File Extension Monthly Additions (in PBs) Over Time",
         xaxis_title="Date",
         yaxis_title="Size (PBs)",
         legend_title="Type",
@@ -353,7 +361,7 @@ with gr.Blocks() as demo:
     # Add top level heading and introduction text
     gr.Markdown("# Git LFS Usage Across the Hub")
     gr.Markdown(
-        "The Hugging Face Hub has just crossed 1,000,000 models - but where is all that data stored? The short answer is Git LFS. This analysis dives into the LFS storage on the Hub, breaking down the data by repository type, file extension, and growth over time."
+        "The Hugging Face Hub has just crossed 1,000,000 models - but where is all that data stored? Most of it is stored in Git LFS. This analysis dives into the LFS storage on the Hub, breaking down the data by repository type, file extension, and growth over time. The data is based on a snapshot of the Hub's LFS storage, starting in March 2022 and ending September 20th, 2024 (meaning the data is incomplete for September 2024). Right now, this is a one-time analysis, but as we do our work we hope to revisit and update the underlying data to provide more insights."
     )
 
     gr.Markdown(
@@ -362,15 +370,20 @@ with gr.Blocks() as demo:
     gr.HTML(div_px(25))
     # Cumulative growth analysis
     gr.Markdown("## Repository Growth")
-    with gr.Row():
-        gr.Plot(fig)
+    gr.Markdown(
+        "The plot below shows the growth of Git LFS storage on the Hub over the past two years. The solid lines represent the cumulative growth of models, spaces, and datasets, while the dashed lines represent the growth with file-level deduplication."
+    )
+    gr.Plot(fig)
 
     gr.HTML(div_px(5))
     # @TODO Talk to Allison about variant="panel"
     with gr.Row():
         with gr.Column(scale=1):
             gr.Markdown(
-                "This table shows the total number of files, cumulative size of those files across all repositories on the Hub, and the potential file-level dedupe savings. To put this in context, the last [Common Crawl](https://commoncrawl.org/) download was [451 TBs](https://github.com/commoncrawl/cc-crawl-statistics/blob/master/stats/crawler/CC-MAIN-2024-38.json#L31). The Spaces repositories alone outpaces that! Meanwhile, between Datasets and Model repos, the Hub stores **64 Common Crawls** 🤯. Current estimates put total deduplication savings at approximately 3.24 PBs (7.2 Common Crawls)!"
+                "In this table, we can see what the final picture looks like as of September 20th, 2024, along with the potential file-level deduplication savings."
+            )
+            gr.Markdown(
+                "To put this in context, the last [Common Crawl](https://commoncrawl.org/) download was [451 TBs](https://github.com/commoncrawl/cc-crawl-statistics/blob/master/stats/crawler/CC-MAIN-2024-38.json#L31). The Spaces repositories alone outpaces that! Meanwhile, between Datasets and Model repos, the Hub stores **64 Common Crawls** 🤯. Current estimates put file deduplication savings at approximately 3.24 PBs (7.2 Common Crawls)!"
             )
         with gr.Column(scale=3):
             # Convert the total size to petabytes and format to two decimal places
@@ -384,14 +397,14 @@ with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column(scale=1):
             gr.Markdown(
-                "The cumulative growth of models, spaces, and datasets over time can be seen in the adjacent chart. Beside that is a view of the total change, from the previous month to the current one, of LFS files stored on the hub over 2024. We're averaging nearly **2.3 PBs uploaded to LFS per month!**"
+                "The month-to-month growth of models, spaces, can be seen in the adjacent table. In 2024, the Hub has averaged nearly **2.3 PBs uploaded to LFS per month!** By the same token, the monthly file deduplication savings are nearly 225TBs. "
             )
 
             gr.Markdown(
-                "By the same token, the monthly file deduplication savings are nearly 225TBs. Borrowing from the [Common Crawl](https://commoncrawl.org/) analogy, that's about half a crawl saved each month!"
+                "Borrowing from the Common Crawl analogy, that's about *5 crawls* uploaded every month, with an _easy savings of half a crawl every month_ by deduplicating at the file-level!"
             )
         with gr.Column(scale=3):
-            gr.Dataframe(last_10_months, height=250)
+            gr.Dataframe(last_10_months)
 
     gr.HTML(div_px(25))
     # File Extension analysis
@@ -445,7 +458,7 @@ with gr.Blocks() as demo:
 
     gr.HTML(div_px(5))
     gr.Markdown(
-        "To dig a little deeper, the following dropdown allows you to filter the area chart by file extension."
+        "To dig a little deeper, the following dropdown allows you to filter the area chart by file extension. Because we're dealing with individual file extensions, the data is presented in terabytes (TBs)."
     )
 
     # build a dropdown using the unique values in the extension column