Upload 5 files

Model.py

@@ -0,0 +1,20 @@
+import torch
+
+class MNIST(torch.nn.Module):
+    def __init__(self):
+        super(MNIST, self).__init__()
+        self.conv = torch.nn.Sequential(torch.nn.Conv2d(1, 32, 3, 1, 1),
+                                        torch.nn.ReLU(),
+                                        torch.nn.Conv2d(32, 64, 3, 1, 1),
+                                        torch.nn.ReLU(),
+                                        torch.nn.MaxPool2d(2, 2))
+        self.dense = torch.nn.Sequential(torch.nn.Linear(14 * 14 * 64, 1024),
+                                         torch.nn.ReLU(),
+                                         torch.nn.Dropout(p=0.2),
+                                         torch.nn.Linear(1024, 10))
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = x.view(-1, 14 * 14 * 64)
+        x = self.dense(x)
+        return x

app.py

@@ -0,0 +1,20 @@
+import gradio as gr
+import cv2
+import torch
+from torchvision import datasets, transforms
+from Model import MNIST
+
+def mnist(image):
+	img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+	transf = transforms.ToTensor()
+	img_tensor = torch.unsqueeze(transf(img), dim=0)
+	res = model(img_tensor)
+	res = res.detach().numpy()
+	return "the result is: " + str(res.argmax())
+
+if __name__ == "__main__":
+	device = torch.device('cpu')
+	model = MNIST().to(device)
+	model.load_state_dict(torch.load('mnist.pkl' , map_location=device))
+	myapp = gr.Interface(fn=mnist, inputs=gr.Image(shape=(28,28)), outputs="text",title="手写数字识别", description="请点击上传图片或选择下方样例",examples=['5.png','2.png','7.png'])
+	myapp.launch()