Update app.py

app.py

@@ -4,7 +4,7 @@ import random
 import string
 import tempfile
 import time
-from concurrent.futures import ThreadPoolExecutor, as_completed
+from concurrent.futures import ThreadPoolExecutor
 from typing import Iterable, List
 
 import gradio as gr
@@ -13,8 +13,7 @@ import torch
 import yaml
 from gradio_logsview.logsview import Log, LogsView, LogsViewRunner
 from mergekit.config import MergeConfiguration
-from clean_community_org import garbage_collect_empty_models
-import spaces
+from huggingface_hub import spaces
 
 has_gpu = torch.cuda.is_available()
 
@@ -62,7 +61,6 @@ If you use it in your research, please cite the following paper:
   year={2024}
 }
 This Space is heavily inspired by LazyMergeKit by Maxime Labonne (see [Colab](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb)).
-
 """
 
 examples = [[str(f)] for f in pathlib.Path("examples").glob("*.yaml")]
@@ -133,7 +131,6 @@ def merge(yaml_config, hf_token, repo_name, profile_name):
         yield from runner.run_python(api.upload_folder, repo_id=repo_url.repo_id, folder_path=merged_path / "merge")
         yield runner.log(f"Model successfully uploaded to HF: {repo_url.repo_id}")
 
-
 with gr.Blocks() as demo:
     gr.Markdown(MARKDOWN_DESCRIPTION)
 
@@ -149,27 +146,11 @@ with gr.Blocks() as demo:
     gr.Examples(examples, fn=lambda s: (s,), run_on_click=True, label="Examples", inputs=[filename], outputs=[config])
     gr.Markdown(MARKDOWN_ARTICLE)
 
-    button.click(fn=merge, inputs=[config, token, repo_name, profile_name], outputs=[logs])
-
-
-def garbage_collect():
-    try:
-        garbage_collect_empty_models(token=COMMUNITY_HF_TOKEN)
-    except Exception as e:
-        print(f"Error running garbage collection: {e}")
-
-
-def schedule_garbage_collection(interval_seconds=0):
-    while True:
-        time.sleep(interval_seconds)
-        garbage_collect()
+    button.click(fn=merge, inputs=[config, token, repo_name, profile_name], outputs=[logs], queue=False)
 
 
 @spaces.GPU
 def launch():
-    pool = ThreadPoolExecutor(max_workers=1)
-    pool.submit(schedule_garbage_collection)
-
     demo.launch(share=True)