Deploy MNIST CNN Gradio app

app.py

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+
+import gradio as gr
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision import transforms
+from PIL import Image
+
+class MNISTNet(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(1, 32, 3, 1)
+        self.conv2 = nn.Conv2d(32, 64, 3, 1)
+        self.dropout1 = nn.Dropout(0.25)
+        self.dropout2 = nn.Dropout(0.5)
+        self.fc1 = nn.Linear(9216, 128)
+        self.fc2 = nn.Linear(128, 10)
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = F.max_pool2d(x, 2)
+        x = self.dropout1(x)
+        x = torch.flatten(x, 1)
+        x = F.relu(self.fc1(x))
+        x = self.dropout2(x)
+        x = self.fc2(x)
+        return x
+
+model = MNISTNet()
+model.load_state_dict(torch.hub.load_state_dict_from_url('https://huggingface.co/Gaimundo/mnist-nn/resolve/main/mnist_cnn.pt', map_location='cpu'))
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Grayscale(),
+    transforms.Resize((28,28)),
+    transforms.ToTensor(),
+    transforms.Normalize((0.1307,), (0.3081,))
+])
+
+def predict(image):
+    image = transform(image).unsqueeze(0)
+    with torch.no_grad():
+        output = model(image)
+        probs = torch.softmax(output, dim=1)[0]
+    return {str(i): float(probs[i]) for i in range(10)}
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", shape=(28,28), image_mode="L", label="Draw a digit"),
+    outputs=gr.Label(num_top_classes=10),
+    title="MNIST Digit Classifier (PyTorch)"
+)
+
+iface.launch()

requirements.txt

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+torch
+torchvision
+gradio