Update src/webui.py

src/webui.py

@@ -21,6 +21,35 @@ def get_current_models(models_dir):
     items_to_remove = ['hubert_base.pt', 'MODELS.txt', 'public_models.json', 'rmvpe.pt']
     return [item for item in models_list if item not in items_to_remove]
 
+def filter_records(query):
+    # Ensure the query is not empty
+    if not query:
+        return [["Error", "Error", "Error", "Error"]]
+    
+    response = requests.get(
+        "https://huggingface.co/api/models",
+        params={"search": query, "filter": "", "sort": "downloads", "direction": "-1", "limit": "20", "full": "True"},
+    )
+
+    if response.status_code == 200:
+        response_json = response.json()
+        models = []
+
+        for model in response_json:
+            if "rvc" in model["tags"] or "huggingartists" in model["tags"]:
+                for file in model.get("siblings", []):
+                    if file.get("rfilename", "").endswith(".pth"):
+                        models.append([
+                            model.get("id").split("/")[-1],
+                            model.get("author"),
+                            model.get("downloads"),
+                            f"https://huggingface.co/{model.get('id')}/resolve/main/{file.get('rfilename')}",
+                        ])
+
+        return models
+
+    else:
+        return [["Error", "Error", "Error", "Error"]]
 
 def update_models_list():
     models_l = get_current_models(rvc_models_dir)
@@ -174,25 +203,32 @@ if __name__ == '__main__':
         )
         gr.Markdown("Duplicate the old CPU space for use in private: [![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm-dark.svg)](https://huggingface.co/spaces/r3gm/AICoverGen_old_stable_cpu?duplicate=true)\n\n") 
 
-        with gr.Tab("Find Models"):
-            # Input field for model query
-            model_query = gr.Textbox(placeholder="Eg: Taylor Swift, Drake", max_lines=1, lines=1, label="Search Models")
-
-            # Dataframe to display the filtered models
-            model_display = gr.Dataframe(
-                headers=["name", "author", "url"],
-                datatype=["str", "str", "str"],
-                row_count=5,
-                col_count=(3, "fixed"),
-                label="Model Results"
+        with gr.Tab("Search"):
+            gr.Markdown("# Model Search")
+    
+            with gr.Column():
+                with gr.Row():
+                    query_input = gr.Textbox(
+                        label="Search Query",
+                        placeholder="Enter model name or keyword...",
+                        lines=1,
+                        min_width=320
+                    )
+            
+                with gr.Row():
+                    output_df = gr.Dataframe(
+                        headers=["name", "author", "downloads", "downloadURL"],
+                        datatype=["str", "str", "number", "str"],
+                        row_count=5,
+                        col_count=(4, "fixed"),
+                    )
+
+            query_input.submit(
+                fn=filter_records,
+                inputs=query_input,
+                outputs=output_df
             )
 
-            # Button to trigger the search
-            filter_btn = gr.Button("Search")
-
-            # Connect the button click to the filter_models function
-            filter_btn.click(fn=lambda query: filter_models([], query), inputs=model_query, outputs=model_display)
-
             
         with gr.Tab("Generate"):