filter gradio repos

app.py

@@ -19,14 +19,18 @@ assert HF_TOKEN
 def yield_models(exclude_users: Optional[Set[str]] = None):
     """Yields models from the hub optionally excluding users."""
     for model in iter(list_models(full=True)):
-        if exclude_users and model.author in exclude_users:
+        if (
+            exclude_users is not None
+            and model.author is not None
+            and model.author in exclude_users
+        ):
             continue
         yield "model", model
 
 
 def yield_spaces(exclude_users: Optional[Set[str]] = None):
     for space in iter(list_spaces(full=True)):
-        if exclude_users and space.author in exclude_users:
+        if exclude_users is not None and space.author and space.author in exclude_users:
             continue
         yield "space", space
 
@@ -84,7 +88,7 @@ def get_top_k_notebooks_by_repo_type(type: str = "space", k: int = 50):
 
 
 def update_stats():
-    df = pl.LazyFrame(yield_notebooks_counts())
+    df = pl.LazyFrame(yield_notebooks_counts(exclude_users="gradio"))
 
     df = (
         df.with_columns(pl.col("repo_id").str.split_exact("/", 1))
@@ -142,12 +146,12 @@ with gr.Blocks() as demo:
         .sort("date")
         .to_pandas()
     )
- 
+
     gr.Markdown("Top Repos by likes with notebooks")
     # k = gr.Slider(10, 100, 10,step=5, label="k",interactive=True)
     # repo_type = gr.Dropdown(["space", "model"], value="space", label="repo_type")
-    gr.DataFrame(get_top_k_notebooks_by_repo_type("space", 10)[['repo_id','likes']])
-    gr.DataFrame(get_top_k_notebooks_by_repo_type("model", 10)[['repo_id','likes']])
+    gr.DataFrame(get_top_k_notebooks_by_repo_type("space", 10)[["repo_id", "likes"]])
+    gr.DataFrame(get_top_k_notebooks_by_repo_type("model", 10)[["repo_id", "likes"]])
     # repo_type.update(get_top_k_notebooks_by_repo_type, [repo_type, k],[df])
     gr.Markdown("## Notebooks on the Hub raw data")
     gr.DataFrame(pandas_df)