Update app.py

app.py

@@ -12,19 +12,19 @@ os.system("wget https://github.com/liuxiaoyuyuyu/vanGogh-and-Other-Artist/blob/m
 #model = torch.hub.load('pytorch/vision:v0.9.0', 'mobilenet_v2', pretrained=False)
 #checkpoint = 'https://github.com/liuxiaoyuyuyu/vanGogh-and-Other-Artist/blob/main/model_weights_mobilenet_v2_valp1trainp2.pth'
 #model.load_state_dict(torch.hub.load_state_dict_from_url(checkpoint, progress=False))
-model = models.mobilenet_v2()
-num_ftrs = model.classifier[1].in_features
-model.classifier[1] = nn.Linear(num_ftrs, 6)
+model = models.vgg16()
+num_ftrs = model.classifier[6].in_features
+model.classifier[6] = nn.Linear(num_ftrs, 6)
 device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
 #model = model.to(device)
-model.load_state_dict(torch.load('model_weights_mobilenet_v2_valp1trainp2.pth',map_location=device))
+model.load_state_dict(torch.load('VGG16_weights_May28.pth',map_location=device))
 
 #torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
 
 
 def inference(input_image):
     preprocess = transforms.Compose([
-        transforms.Resize(256),
+        transforms.Resize(260),
         transforms.CenterCrop(224),
         transforms.ToTensor(),
         #transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),