deploy

app.py

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+import gradio as gr
+from model import Net, predict
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+
+model = Net()
+model.load_state_dict(torch.load("mnist_model.pth", map_location=torch.device("cpu")))
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Grayscale(),  # Convert to grayscale if needed
+    transforms.Resize((28, 28)),  # Fix: pass size as a tuple
+    transforms.ToTensor()  # Convert to a PyTorch tensor
+])
+
+
+def predict_image(image):
+    input_tensors = transform(Image.fromarray(image)).unsqueeze(0)
+
+    result = predict(model,input_tensors)
+
+    return result
+
+
+app = gr.Interface(predict_image, gr.Image(), "text")
+
+
+app.launch()

mnist_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a49b6e2a632bf6efd53d49aab0ec533481c431d7404429b6a6147221c942d0f7
+size 1689904

model.py

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+import torch
+from torch import nn
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
+        self.relu = nn.ReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.flatten = nn.Flatten()
+        self.fc1 = nn.Linear(64 * 7 * 7, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+        x = self.conv2(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+        x = self.flatten(x)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        return x
+
+
+def predict(model, image):
+    model.eval()
+    with torch.no_grad():
+        output = model(image)
+
+    result = torch.argmax(output,dim=1).item()
+    return result