filter zero

app.py

@@ -8,8 +8,15 @@ from cachetools import cached, TTLCache
 
 @cached(TTLCache(maxsize=10, ttl=60 * 60 * 3))
 def get_all_models():
-    models = list(tqdm(iter(list_models(cardData=True, limit=None))))
-    return [model for model in models if model is not None]
+    models = list(
+        tqdm(
+            iter(list_models(cardData=True, limit=None, sort="downloads", direction=-1))
+        )
+    )
+    models = [model for model in models if model is not None]
+    return [
+        model for model in models if model.downloads > 1
+    ]  # filter out models with 0 downloads
 
 
 def has_base_model_info(model):
@@ -69,7 +76,10 @@ def return_models_for_base_model(base_model):
     # sort models by downloads
     models = sorted(models, key=lambda x: x.downloads, reverse=True)
     results = ""
-    results += f"## Models fine-tuned from [`{base_model}`](https://huggingface.co/{base_model}) \n\n"
+    results += (
+        "## Models fine-tuned from"
+        f" [`{base_model}`](https://huggingface.co/{base_model}) \n\n"
+    )
     results += f"`{base_model}` has {len(models)} children\n\n"
     total_download_number = sum(model.downloads for model in models)
     results += (
@@ -131,6 +141,7 @@ with gr.Blocks() as demo:
         You can also optionally filter by task to see rankings for a particular machine learning task.
         Don't forget to  ❤  if you like this space 🤗"""
     )
+
     gr.Markdown(produce_summary())
     gr.Markdown("## Find all models trained from a base model")
     base_model = gr.Dropdown(all_base_models, label="Base Model")