diff --git a/lightrise.py b/lightrise.py
new file mode 100644
index 0000000..16d1554
--- /dev/null
+++ b/lightrise.py
@@ -0,0 +1,49 @@
+# coding=utf-8
+import os
+import torch
+import numpy as np
+from PIL import Image
+from torchvision import transforms
+import argparse
+from models.modeling import VisionTransformer, CONFIGS
+import time
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--pretrained_model", type=str, default="../module/ieemoo-ai-isempty/model/new/ieemooempty_vitlight_checkpoint.pth", help="load pretrained model") #使用自定义VIT
+args = parser.parse_args()
+
+#args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+args.device = torch.device("cpu")
+
+# 准备模型
+model = torch.load(args.pretrained_model,map_location=torch.device('cpu')) #自己预训练模型
+model.to(args.device)
+model.eval()
+
+num_classes = 2
+cls_dict = {0: "noemp", 1: "yesemp"}
+
+test_transform = transforms.Compose([transforms.Resize((600, 600), Image.BILINEAR),
+                                     transforms.ToTensor(),
+                                     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
+
+
+#复杂场景小模型测试单张图片
+def riseempty(imgdata):
+    x = test_transform(imgdata)
+    part_logits = model(x.unsqueeze(0).to(args.device))
+    probs = torch.nn.Softmax(dim=-1)(part_logits)
+    top2 = torch.argsort(probs, dim=-1, descending=True)
+    riseclas_ids = top2[0][0]
+    #print("cur_img result: class id: %d, score: %0.3f" % (riseclas_ids, probs[0, riseclas_ids].item()))
+    riseresult={}
+    riseresult["success"] = "true"
+    riseresult["rst_cls"] = int(riseclas_ids)
+    
+    return riseresult
+
+if __name__ == "__main__":
+    riseresult = riseempty("light.jpg")
+    print(riseresult)
+
+