torch.device(cuda)

app.py

@@ -37,7 +37,6 @@ warnings.filterwarnings('ignore')
 MAX_PATCHES = 512
 # Load the models and processor
 #device = torch.device("cpu")
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Paths to the models
 ko_deplot_model_path = './deplot_model_ver_kor_24.7.25_refinetuning_epoch3.bin'
@@ -50,7 +49,7 @@ def load_model1():
     processor1 = Pix2StructProcessor.from_pretrained('nuua/ko-deplot')
     model1 = Pix2StructForConditionalGeneration.from_pretrained('nuua/ko-deplot')
     model1.load_state_dict(torch.load(ko_deplot_model_path, map_location="cpu"))
-    model1.to(device)
+    model1.to(torch.device("cuda"))
     return processor1,model1
 
 processor1,model1=load_model1()
@@ -61,7 +60,7 @@ def load_model2():
     processor2 = AutoProcessor.from_pretrained("ybelkada/pix2struct-base")
     model2 = Pix2StructForConditionalGeneration.from_pretrained("ybelkada/pix2struct-base")
     model2.load_state_dict(torch.load(aihub_deplot_model_path, map_location="cpu"))
-    model2.to(device)
+    model2.to(torch.device("cuda"))
     return processor2,model2
 
 processor2,model2=load_model2()
@@ -73,7 +72,7 @@ def load_model3():
     unichart_model_path = "./unichart"
     model3 =  VisionEncoderDecoderModel.from_pretrained(unichart_model_path)
     processor3 = DonutProcessor.from_pretrained(unichart_model_path)
-    model3.to(device)  
+    model3.to(torch.device("cuda"))  
     return processor3,model3
 
 processor3,model3=load_model3()