add application file

app.py

@@ -0,0 +1,40 @@
+import torch
+# from neural_network import neural_network
+from neural_network_CNN import neural_network
+from preprocess_data import pic_process
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+import gradio as gr
+from PIL import Image
+
+# net=neural_network(784,512,10)
+net=neural_network()
+model_path=r"D:\PythonProject\Mnist_study\model\Mnist_model.pth"
+output_path=r"D:\PythonProject\Mnist_study\img\output.jpg"
+
+
+def net_predict(image):
+    images = pic_process(image)[0]
+    images_array = pic_process(image)[1]
+    # 载入训练好的神经网络模型
+    try:
+        net.load_state_dict(torch.load(model_path))
+    except:
+        print("fail to load model")
+    # labels=Variable(labels)
+    outputs=net.forward(images) #前向传播
+    _,predicts=torch.max(outputs.data,1) #输出概率最大的索引值
+    # 显示图片
+    # fig = plt.figure()
+    plt.imshow(images_array,cmap=plt.cm.binary)
+    plt.title(label='predicted number:'+str(np.array(predicts)[0]))
+    plt.savefig(output_path)
+    # plt.show()
+    return output_path
+
+interface = gr.Interface(fn=net_predict, inputs=gr.inputs.Image(source="canvas",type="pil",image_mode="L"),outputs=gr.outputs.Image(type="file"))
+interface.launch(share=True)
+
+