Update app.py

app.py

@@ -51,6 +51,11 @@ tr:hover {
     max-height: 600px;
     overflow-y: auto;
 }
+.loading {
+    text-align: center;
+    font-size: 18px;
+    padding: 20px;
+}
 """
 
 # List of schizo words to check for
@@ -160,7 +165,7 @@ def fetch_model_readme(model_id):
         print(f"Error fetching README for {model_id}: {e}")
         return None
 
-def generate_leaderboard_data(num_models=100, model_type="all"):
+def generate_leaderboard_data(model_type="llm", max_models=500):
     """Generate leaderboard data by analyzing model cards"""
     api = HfApi(token=HF_TOKEN)
     
@@ -169,21 +174,18 @@ def generate_leaderboard_data(num_models=100, model_type="all"):
         # Filter to text-generation models using API parameters
         models = list_models(
             task="text-generation",
-            limit=num_models * 5  # Get more models than needed to account for ones without READMEs
+            limit=max_models  # Set a reasonable limit to avoid overwhelming the API
         )
     else:
         # Get all models
         models = list_models(
-            limit=num_models * 5  # Get more models than needed to account for ones without READMEs
+            limit=max_models  # Set a reasonable limit to avoid overwhelming the API
         )
     
     leaderboard_data = []
-    count = 0
+    processed_count = 0
     
     for model in models:
-        if count >= num_models:
-            break
-            
         model_id = model.id
         readme_content = fetch_model_readme(model_id)
         
@@ -206,10 +208,11 @@ def generate_leaderboard_data(num_models=100, model_type="all"):
             "markdown_count": ratings["markdown_count"]
         })
         
-        count += 1
+        processed_count += 1
         
         # Status update
-        print(f"Processed {count}/{num_models} models")
+        if processed_count % 10 == 0:
+            print(f"Processed {processed_count} models")
     
     # Sort by combined rating in descending order
     leaderboard_data.sort(key=lambda x: x["combined_rating"], reverse=True)
@@ -305,18 +308,30 @@ def create_leaderboard_html(leaderboard_data):
     
     return html
 
-@spaces.GPU()
-def update_leaderboard(num_models, model_type):
-    """Update leaderboard with new data"""
+def load_leaderboard(model_type):
+    """Load the leaderboard with models"""
     # Show loading message
-    yield "Loading models and analyzing Schizo ratings... This may take a few minutes."
+    yield '<div class="loading">Loading models and analyzing Schizo ratings... This may take a few minutes.</div>'
     
     try:
-        leaderboard_data = generate_leaderboard_data(num_models, model_type)
+        leaderboard_data = generate_leaderboard_data(model_type)
         leaderboard_html = create_leaderboard_html(leaderboard_data)
         yield leaderboard_html
     except Exception as e:
-        yield f"<p>Error generating leaderboard: {str(e)}</p>"
+        yield f'<div class="loading">Error generating leaderboard: {str(e)}</div>'
+
+# Background loading thread
+def background_loader(model_type, progress=None):
+    try:
+        leaderboard_data = generate_leaderboard_data(model_type)
+        leaderboard_html = create_leaderboard_html(leaderboard_data)
+        return leaderboard_html
+    except Exception as e:
+        return f'<div class="loading">Error generating leaderboard: {str(e)}</div>'
+
+@spaces.GPU()
+def init_leaderboard():
+    return '<div class="loading">Initializing leaderboard... Please wait while we analyze Hugging Face models.</div>'
 
 with gr.Blocks(css=CSS, theme="soft") as demo:
     gr.HTML(TITLE)
@@ -324,36 +339,28 @@ with gr.Blocks(css=CSS, theme="soft") as demo:
     
     with gr.Row():
         with gr.Column():
-            num_models_slider = gr.Slider(
-                minimum=10,
-                maximum=200,
-                step=10,
-                value=50,
-                label="Number of Models to Analyze",
-            )
-            
             model_type_dropdown = gr.Dropdown(
-                choices=["all", "llm"],
+                choices=["llm", "all"],
                 value="llm",
                 label="Model Type Filter",
             )
             
-            update_button = gr.Button("Update Leaderboard")
+            refresh_button = gr.Button("Refresh Leaderboard")
         
-    leaderboard_html = gr.HTML()
+    leaderboard_html = gr.HTML(value=init_leaderboard)
     
-    update_button.click(
-        fn=update_leaderboard,
-        inputs=[num_models_slider, model_type_dropdown],
+    # Load leaderboard on startup and when refresh button is clicked
+    demo.load(
+        fn=load_leaderboard,
+        inputs=[model_type_dropdown],
+        outputs=[leaderboard_html],
+    )
+    
+    refresh_button.click(
+        fn=load_leaderboard,
+        inputs=[model_type_dropdown],
         outputs=[leaderboard_html],
     )
-
-# Add a default leaderboard with a few example entries for initial display
-default_leaderboard = [
-    {"model_id": "example/model-1", "combined_rating": 150.0, "word_rating": 100.0, "wordiness_rating": 25.0, "visual_rating": 25.0},
-    {"model_id": "example/model-2", "combined_rating": 100.0, "word_rating": 80.0, "wordiness_rating": 10.0, "visual_rating": 10.0},
-    {"model_id": "example/model-3", "combined_rating": 75.0, "word_rating": 50.0, "wordiness_rating": 12.5, "visual_rating": 12.5}
-]
 
 if __name__ == "__main__":
     demo.launch()