Update app.py

app.py

@@ -2,14 +2,10 @@ import os
 import re
 import time
 import spaces
-import torch
 import requests
-from transformers import AutoModelForCausalLM, AutoTokenizer
 import gradio as gr
-from huggingface_hub import HfApi, ModelFilter, list_models
+from huggingface_hub import HfApi, list_models
 from threading import Thread
-import math
-import base64
 
 HF_TOKEN = os.environ.get("HF_TOKEN", None)
 
@@ -168,14 +164,18 @@ def generate_leaderboard_data(num_models=100, model_type="all"):
     """Generate leaderboard data by analyzing model cards"""
     api = HfApi(token=HF_TOKEN)
     
-    # Define filter based on model type
+    # Define filter parameters based on model type
     if model_type == "llm":
-        model_filter = ModelFilter(task="text-generation")
+        # 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
+        )
     else:
-        model_filter = None
-    
-    # List models
-    models = list_models(filter=model_filter, limit=num_models * 5)  # Get more models than needed to account for ones without READMEs
+        # Get all models
+        models = list_models(
+            limit=num_models * 5  # Get more models than needed to account for ones without READMEs
+        )
     
     leaderboard_data = []
     count = 0
@@ -207,6 +207,9 @@ def generate_leaderboard_data(num_models=100, model_type="all"):
         })
         
         count += 1
+        
+        # Status update
+        print(f"Processed {count}/{num_models} models")
     
     # Sort by combined rating in descending order
     leaderboard_data.sort(key=lambda x: x["combined_rating"], reverse=True)
@@ -305,9 +308,15 @@ def create_leaderboard_html(leaderboard_data):
 @spaces.GPU()
 def update_leaderboard(num_models, model_type):
     """Update leaderboard with new data"""
-    leaderboard_data = generate_leaderboard_data(num_models, model_type)
-    leaderboard_html = create_leaderboard_html(leaderboard_data)
-    return leaderboard_html
+    # Show loading message
+    yield "Loading models and analyzing Schizo ratings... This may take a few minutes."
+    
+    try:
+        leaderboard_data = generate_leaderboard_data(num_models, 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>"
 
 with gr.Blocks(css=CSS, theme="soft") as demo:
     gr.HTML(TITLE)
@@ -339,5 +348,12 @@ with gr.Blocks(css=CSS, theme="soft") as demo:
         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()