Add application file

core.py

@@ -1,3 +1,5 @@
+import pdb
+
 import torch
 import torchvision.transforms as transforms
 from PIL import Image
@@ -20,8 +22,26 @@ device = torch.device("cpu")
 print("use cpu")
 
 # model_ckpt_path = "./models/resnet18.pth"
-model_ckpt_path = "https://huggingface.co/M4869/beauty_prediction_fpb5k/blob/main/resnet18.pth"
+# model_ckpt_path = "https://huggingface.co/M4869/beauty_prediction_fpb5k/blob/main/resnet18.pth"
+
+# model_ckpt_path = "M4869/beauty_prediction_fpb5k"
+# model = torch.hub.load("huggingface/transformers", model_ckpt_path)
+
+
+from huggingface_hub import hf_hub_download
+import joblib
+
+REPO_ID = "M4869"
+FILENAME = "beauty_prediction_fpb5k"
+
+# tmp = joblib.load(
+#     hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
+# )
+
+# 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
 
+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)
 load_model(torch.load(model_ckpt_path, encoding='latin1'), net)
 net.eval()