Update utils.py

utils.py

@@ -212,32 +212,32 @@ class MolecularPropertyPredictionModel():
         if adapter_name == self.adapter_name:
             return "keep"
         # switch adapter
-        try:
+        #try:
             #self.adapter_name = adapter_name
             #print(self.adapter_name, adapter_id)
             #self.lora_model = PeftModel.from_pretrained(self.base_model, adapter_id, token = os.environ.get("TOKEN"))
             #self.lora_model.to("cuda")
             #print(self.lora_model)
 
-            self.base_model.set_adapter(adapter_name)
-            self.base_model.eval()
+        self.base_model.set_adapter(adapter_name)
+        self.base_model.eval()
 
-            #if adapter_name not in self.apapter_scaler_path:
-            #    self.apapter_scaler_path[adapter_name] = hf_hub_download(adapter_id, filename="scaler.pkl", token = os.environ.get("TOKEN"))
-            if os.path.exists(self.apapter_scaler_path[adapter_name]):
-                self.scaler = pickle.load(open(self.apapter_scaler_path[adapter_name], "rb"))
-            else:
-                self.scaler = None
-            
-            self.adapter_name = adapter_name
-
-            return "switched"
-        except Exception as e:
-            print(e)
-            # handle error
-            return "error"
+        #if adapter_name not in self.apapter_scaler_path:
+        #    self.apapter_scaler_path[adapter_name] = hf_hub_download(adapter_id, filename="scaler.pkl", token = os.environ.get("TOKEN"))
+        if os.path.exists(self.apapter_scaler_path[adapter_name]):
+            self.scaler = pickle.load(open(self.apapter_scaler_path[adapter_name], "rb"))
+        else:
+            self.scaler = None
+        
+        self.adapter_name = adapter_name
+
+        return "switched"
+        #except Exception as e:
+        #    print(e)
+        #    # handle error
+        #    return "error"
     
-    @spaces.GPU(duration=20)
+    @spaces.GPU(duration=10)
     def predict(self, valid_df, task_type):
         test_dataset = Dataset.from_pandas(valid_df)
         # construct the dataloader