adapt zerogpu

app.py

@@ -212,6 +212,8 @@ def create_protenix_json(input_data: Dict) -> List[Dict]:
 #@torch.inference_mode()
 @spaces.GPU(duration=120)  # Specify a duration to avoid timeout
 def predict_structure(input_collector: dict):
+        #first initialize runner
+        runner = InferenceRunner(configs)
         """Handle both input types"""
         os.makedirs("./output", exist_ok=True)
         
@@ -282,9 +284,9 @@ configs = parse_configs(
     fill_required_with_null=True,
 )
 configs.load_checkpoint_path='./checkpoint.pt'
-download_infercence_cache(configs, model_version="v0.2.0")
+download_infercence_cache()
 configs.use_deepspeed_evo_attention=False
-runner = InferenceRunner(configs)
+
 add_watermark = gr.Checkbox(label="Add Watermark", value=True)
 add_watermark1 = gr.Checkbox(label="Add Watermark", value=True)
 
@@ -453,6 +455,8 @@ with gr.Blocks(title="FoldMark", css=custom_css) as demo:
 
     @spaces.GPU
     def is_watermarked(file):
+        #first initialize runner
+        runner = InferenceRunner(configs)
         # Generate a unique subdirectory and filename
         unique_id = str(uuid.uuid4().hex[:8])
         subdir = os.path.join('./output', unique_id)

runner/inference.py

@@ -201,7 +201,7 @@ class InferenceRunner(object):
         self.model.configs = new_configs
 
 
-def download_infercence_cache(configs: Any, model_version: str = "v0.2.0") -> None:
+def download_infercence_cache() -> None:
     code_directory = './'
 
     data_cache_dir = os.path.join(code_directory, "release_data/ccd_cache")