device agnostic code taken out

app.py

@@ -10,9 +10,6 @@ from typing import Tuple, Dict
 # Setup class names
 class_names = ["real", "spoof"]
 
-# Setup device-agnostic code
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
 ### 2. Model and transforms preparation ###
 
 # Create EffNetB2 model
@@ -38,7 +35,7 @@ def predict(img):# -> Tuple[Dict, float]:
     start_time = timer()
     
     # Transform the target image and add a batch dimension
-    img = data_transform(img).unsqueeze(0).to(device)
+    img = data_transform(img).unsqueeze(0)
     
     # Put model into evaluation mode and turn on inference mode
     vggface2.eval()

model.py

@@ -19,7 +19,7 @@ def create_vggface2_model(num_classes:int=2,
         transforms (torchvision.transforms): vggface2 image transforms.
     """
     # load the saved model
-    model_pred = InceptionResnetV1(pretrained='vggface2' , classify = True , num_classes = 2).to(device)
+    model_pred = InceptionResnetV1(pretrained='vggface2' , classify = True , num_classes = 2)
     layer_list = list(model_pred.children())[-5:] # all final layers
     model_pred = nn.Sequential(*list(model_pred.children())[:-5])
 
@@ -43,8 +43,6 @@ def create_vggface2_model(num_classes:int=2,
                          out_features=2, # same number of output units as our number of classes
                          bias=True))
 
-    model_pred = model_pred.to(device)
-
     # Write transform for image
     data_transform = transforms.Compose([
         # Resize the images to 64x64 --> RECOMENDATION FROM TRAINING FROM FACENET --> 160x160