add app.py

app.py

@@ -0,0 +1,62 @@
+
+import torch
+from torch import nn
+from torchvision import datasets
+from torchvision.transforms import ToTensor
+
+
+# Define model
+class NeuralNetwork(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.flatten = nn.Flatten()
+        self.linear_relu_stack = nn.Sequential(
+            nn.Linear(28*28, 512),
+            nn.ReLU(),
+            nn.Linear(512, 512),
+            nn.ReLU(),
+            nn.Linear(512, 10)
+        )
+
+    def forward(self, x):
+        x = self.flatten(x)
+        logits = self.linear_relu_stack(x)
+        return logits
+
+
+model = NeuralNetwork()
+model.load_state_dict(torch.load("model_mnist_mlp.pth"))
+
+model.eval()
+
+import gradio as gr
+from torchvision import transforms
+
+def predict(image):
+    tsr_image = transforms.ToTensor()(image)
+
+    with torch.no_grad():
+        pred = model(tsr_image)
+        prob = torch.nn.functional.softmax(pred[0], dim=0)
+
+    confidences = {i: float(prob[i]) for i in range(10)} 
+
+    return confidences
+
+
+with gr.Blocks(css=".gradio-container {background:lightyellow;color:red;}", title="テスト"
+               ) as demo:
+    gr.HTML('<div style="font-size:12pt; text-align:center; color:yellow;"MNIST 分類器</div>')
+
+    with gr.Row(): 
+        input_image = gr.Image(label="画像入力", type="pil", image_mode="L", shape=(28, 28), invert_colors=True)
+
+        output_label=gr.Label(label="予測確率", num_top_classes=5)
+
+    send_btn = gr.Button("予測する")
+    send_btn.click(fn=predict, inputs=input_image, outputs=output_label)
+
+# demo.queue(concurrency_count=3)
+demo.launch()
+
+### EOF ###