Filter Dropdown Models Properly

app.py

@@ -1,17 +1,17 @@
 from typing import List
 import gradio as gr
-from transformers import AutoModel, AutoTokenizer
+# from transformers import AutoModel, AutoTokenizer
 from huggingface_hub import list_models
 
-def get_collection_models(collection_name: str) -> List[str]:
+def get_collection_models(tag: str) -> List[str]:
     """Get a list of models from a specific Hugging Face collection."""
-    models = list_models(author="de-Rodrigo",
-                         filter=f"collections:{collection_name}")
-    models_nofilter = list_models(author="de-Rodrigo")
-    print("")
-    print(models_nofilter)
-    print("")
-    model_names = [model.modelId for model in models]
+    models = list_models(author="de-Rodrigo")
+    
+    model_names = []
+    for model in models:
+        if tag in model.tags:
+            model_names.append(model.modelId)
+
     return model_names
 
 def load_model(model_name: str):
@@ -28,19 +28,18 @@ def process_input(text: str, model_name: str) -> str:
     return f"Processed output with {model_name}"
 
 # Create Gradio interface
-def create_interface(collection_name: str):
+def create_interface(tag: str):
     iface = gr.Interface(
         fn=process_input,
         inputs=[
             gr.Textbox(lines=2, placeholder="Enter some text", label="Input Text"),
-            gr.Dropdown(choices=get_collection_models(collection_name), label="Select Model")
+            gr.Dropdown(choices=get_collection_models(tag), label="Select Model")
         ],
         outputs=gr.Textbox(label="Model Output"),
-        title="Hugging Face Model Selector from Collection",
-        description=f"Select a model from the '{collection_name}'.")
+        title="Hugging Face Model Selector from Collection")
     return iface
 
 # Specify the name of your collection
-collection_name = "VrDU-Doctor"
-iface = create_interface(collection_name)
+filter_tag = "saliency-merit"
+iface = create_interface(filter_tag)
 iface.launch()