Update app.py

app.py

@@ -8,7 +8,83 @@ iface = gr.Interface.from_pipeline(
         pipe, 
         examples= [ [example] for example in images],
         description="Final project that labels flowers images into: Daisy, Dandelion, Rose, Sunflower, Tulip",
-        title="Vit"
+        title="Flower Classifier - Vit"
 )
 
 iface.launch()
+# import torch
+# import torchvision.transforms as transforms
+# from torchvision import models
+# from torch import nn
+# import torch.nn.functional as F
+# import gradio as gr
+# import json
+
+
+# # Get cpu or gpu device for training.
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+# print(f"Using {device} device")
+
+# model_path = './resnet18_flower.pth'
+# num_classes = 5
+# label_name_list = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']
+
+# # step1: 创建并加载模型
+# resnet18 = models.resnet18(pretrained=True)
+# num_ftrs = resnet18.fc.in_features
+# resnet18.fc = nn.Linear(num_ftrs, num_classes)
+# resnet18 = resnet18.to(device)
+
+# resnet18.load_state_dict(torch.load(model_path,map_location=torch.device('cpu')))
+
+# model = resnet18
+# # model = base_model_vgg13
+# model.eval()
+
+
+# # step2: 图片转换
+# train_transform = transforms.Compose([
+#                     # transforms.RandomRotation(5),
+#                     # transforms.RandomHorizontalFlip(),
+#                     transforms.Resize((224,224)),
+#                     transforms.ToTensor(),
+#                     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+#                     ])
+
+
+# def predict(inp):
+#   # 定义预处理变换
+#   transform = train_transform
+
+#   # 加载图片并进行预处理
+#   # image = Image.open(image_path)
+#   image = transform(inp).unsqueeze(0).to(device)
+
+#   # step3：使用模型进行预测
+#   with torch.no_grad():
+#       output = model(image)
+      
+#   # step4：数据后处理
+#   # 计算预测概率
+#   pred_score = nn.functional.softmax(output[0], dim=0)
+#   pred_score = pred_score.cpu().numpy()
+
+#   # 获取预测结果
+#   pred_index = torch.argmax(output, dim=1).item()
+#   pred_label = label_name_list[pred_index]
+  
+#   # 转为json字符串格式
+#   result_dict = {'pred_score':str(max(pred_score)),'pred_index':str(pred_index),'pred_label':pred_label }
+#   result_json = json.dumps(result_dict)
+
+#   return result_json
+
+
+# demo = gr.Interface(fn=predict, 
+#              inputs=gr.Image(type="pil"),
+#              outputs="text",
+#              examples=["./592px-Red_sunflower.jpg"],
+#              )
+             
+# # demo.launch(debug=True)
+# demo.launch()