add app.py

app.py

@@ -0,0 +1,76 @@
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torchvision.transforms import ToTensor
+
+# Define model
+class ConvNet(nn.Module):
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=5)
+        self.conv2 = nn.Conv2d(32, 32, kernel_size=5)
+        self.conv3 = nn.Conv2d(32,64, kernel_size=5)
+        self.fc1 = nn.Linear(3*3*64, 256)
+        self.fc2 = nn.Linear(256, 10)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        #x = F.dropout(x, p=0.5, training=self.training)
+        x = F.relu(F.max_pool2d(self.conv2(x), 2))
+        x = F.dropout(x, p=0.5, training=self.training)
+        x = F.relu(F.max_pool2d(self.conv3(x),2))
+        x = F.dropout(x, p=0.5, training=self.training)
+        x = x.view(-1,3*3*64 )
+        x = F.relu(self.fc1(x))
+        x = F.dropout(x, training=self.training)
+        logits = self.fc2(x)
+        return logits
+
+
+model = ConvNet()
+model.load_state_dict(
+    torch.load("weights/mnist_convnet_model.pth",
+    map_location=torch.device('cpu'))
+    )
+
+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="MNIST 分類器"
+               ) as demo:
+    gr.HTML('<div style="font-size:12pt; text-align:center; color:yellow;"MNIST 分類器</div>')
+
+    with gr.Row():
+        with gr.Tab("キャンバス"):
+            input_image1 = gr.Image(label="画像入力", source="canvas", type="pil", image_mode="L", shape=(28,28), invert_colors=True)    
+            send_btn1 = gr.Button("推論する")
+
+        with gr.Tab("画像ファイル"):
+            input_image2 = gr.Image(label="画像入力", type="pil", image_mode="L", shape=(28, 28), invert_colors=True)
+            send_btn2 = gr.Button("推論する")
+            gr.Examples(['examples/sample2.png', 'examples/sample4.png'], inputs=input_image2)
+
+        output_label=gr.Label(label="推論確率", num_top_classes=3)
+
+    send_btn1.click(fn=predict, inputs=input_image1, outputs=output_label)
+    send_btn2.click(fn=predict, inputs=input_image2, outputs=output_label)
+
+# demo.queue(concurrency_count=3)
+demo.launch()
+
+
+### EOF ###