add decorator to functions

app.py

@@ -45,37 +45,38 @@ aihub_deplot_model_path='./deplot_k.pt'
 t5_model_path = './ke_t5.pt'
 
 # Load first model ko-deplot
-processor1 = Pix2StructProcessor.from_pretrained('nuua/ko-deplot')
-model1 = Pix2StructForConditionalGeneration.from_pretrained('nuua/ko-deplot')
 @spaces.GPU(enable_queue=True)
-model1.load_state_dict(torch.load(ko_deplot_model_path, map_location=device))
-@spaces.GPU(enable_queue=True)
-model1.to(device)
+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=device))
+    model1.to(device)
+    return processor1,model1
+
+processor1,model1=load_model1()
 
 # Load second model aihub-deplot
-processor2 = AutoProcessor.from_pretrained("ybelkada/pix2struct-base")
-model2 = Pix2StructForConditionalGeneration.from_pretrained("ybelkada/pix2struct-base")
 @spaces.GPU(enable_queue=True)
-model2.load_state_dict(torch.load(aihub_deplot_model_path, map_location=device))
+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=device))
+    model2.to(device)
+    return processor2,model2
 
+processor2,model2=load_model2()
 
-tokenizer = T5Tokenizer.from_pretrained("KETI-AIR/ke-t5-base")
-t5_model = T5ForConditionalGeneration.from_pretrained("KETI-AIR/ke-t5-base")
-@spaces.GPU(enable_queue=True)
-t5_model.load_state_dict(torch.load(t5_model_path, map_location=device))
-
-@spaces.GPU(enable_queue=True)
-model2.to(device)
-@spaces.GPU(enable_queue=True)
-t5_model.to(device)
 
 #Load third model unichart
-unichart_model_path = "./unichart"
-model3 =  VisionEncoderDecoderModel.from_pretrained(unichart_model_path)
-processor3 = DonutProcessor.from_pretrained(unichart_model_path)
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 @spaces.GPU(enable_queue=True)
-model3.to(device)  
+def load_model3():
+    unichart_model_path = "./unichart"
+    model3 =  VisionEncoderDecoderModel.from_pretrained(unichart_model_path)
+    processor3 = DonutProcessor.from_pretrained(unichart_model_path)
+    model3.to(device)  
+    return processor3,model3
+
+processor3,model3=load_model3()
 
 #ko-deplot 추론함수
 # Function to format output