Add application file

core.py

@@ -19,11 +19,10 @@ def load_model(pretrained_dict, new):
 device = torch.device("cpu")
 print("use cpu")
 
-# ans = hf_hub_download(repo_id="google/pegasus-xsum", filename="config.json")
-# /home/my/.cache/huggingface/hub/models--google--pegasus-xsum/snapshots/8d8ffc158a3bee9fbb03afacdfc347c823c5ec8b/config.json
+net = Nets.ResNet(block=Nets.BasicBlock, layers=[2, 2, 2, 2], num_classes=1).to(device)
 
 model_ckpt_path = hf_hub_download(repo_id="M4869/beauty_prediction_fpb5k", filename="resnet18.pth")
-net = Nets.ResNet(block=Nets.BasicBlock, layers=[2, 2, 2, 2], num_classes=1).to(device)
+# /home/my/.cache/huggingface/hub/models--google--pegasus-xsum/snapshots/8d8ffc158a3bee9fbb03afacdfc347c823c5ec8b/config.json
 load_model(torch.load(model_ckpt_path, encoding='latin1', map_location=torch.device('cpu')), net)
 net.eval()
 
@@ -40,4 +39,4 @@ def fun(img_path):
     with torch.no_grad():
         img = img.unsqueeze(0).to(device)
         output = net(img).squeeze(1).cpu().numpy()[0]
-        return output
+        return output * 20