Update app.py

app.py

@@ -14,16 +14,21 @@ class model:
     def __init__(self):
         self.model = None
         self.model_name = None
+        self.model_dict = {}
 
     def gradio_demo(self, model_name, sequence_input, nucleus_image, protein_image):
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         
         if self.model_name != model_name:
             self.model_name = model_name
-            model_ckpt_path = hf_hub_download(repo_id=f"HuangLab/{model_name}", filename="model.ckpt")
-            model_config_path = hf_hub_download(repo_id=f"HuangLab/{model_name}", filename="config.yaml")
-            hf_hub_download(repo_id=f"HuangLab/{model_name}", filename="nucleus_vqgan.yaml")
-            hf_hub_download(repo_id=f"HuangLab/{model_name}", filename="threshold_vqgan.yaml")
+            if self.model_name not in self.model_dict.keys():
+                model_ckpt_path = hf_hub_download(repo_id=f"HuangLab/{model_name}", filename="model.ckpt")
+                model_config_path = hf_hub_download(repo_id=f"HuangLab/{model_name}", filename="config.yaml")
+                hf_hub_download(repo_id=f"HuangLab/{model_name}", filename="nucleus_vqgan.yaml")
+                hf_hub_download(repo_id=f"HuangLab/{model_name}", filename="threshold_vqgan.yaml")
+                self.model_dict.update({self.model_name:[model_ckpt_path, model_config_path]})
+            else:
+                model_ckpt_path, model_config_path = self.model_dict[self.model_name]
             
             
             # Load model config and set ckpt_path if not provided in config