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app.py

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+# app.py
+import torch
+import torch.nn.functional as F
+import gradio as gr
+import numpy as np
+from PIL import Image
+from model import CNN
+
+# Load model
+model = CNN()
+model.load_state_dict(torch.load("pytorch_model.bin", map_location="cpu")) 
+model.eval()
+
+# Inference function
+def predict_digit(image):
+    image = image.convert("L").resize((28, 28))        # Convert to grayscale
+    image = np.array(image) / 255.0                     # Normalize
+    image = torch.tensor(image).unsqueeze(0).unsqueeze(0).float()  # (1, 1, 28, 28)
+    with torch.no_grad():
+        logits = model(image)
+        probs = F.softmax(logits, dim=1).numpy().flatten()
+        predicted = np.argmax(probs)
+    return {str(i): float(probs[i]) for i in range(10)}
+
+# Gradio UI
+interface = gr.Interface(
+    fn=predict_digit,
+    inputs=gr.Image(type="pil", tool="editor", label="Draw or Upload a Digit"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Handwritten Digit Classifier",
+    description="Draw a digit or upload an image of a handwritten digit."
+)
+
+if __name__ == "__main__":
+    interface.launch()

model.py

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+# model.py
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class CNN(nn.Module):
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)       # (1, 28, 28) -> (32, 26, 26)
+        self.pool1 = nn.MaxPool2d(2, 2)                    # (32, 26, 26) -> (32, 13, 13)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)      # (32, 13, 13) -> (64, 11, 11)
+        self.pool2 = nn.MaxPool2d(2, 2)                    # (64, 11, 11) -> (64, 5, 5)
+        self.fc1 = nn.Linear(64 * 5 * 5, 64)
+        self.fc2 = nn.Linear(64, 10)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = self.pool1(x)
+        x = F.relu(self.conv2(x))
+        x = self.pool2(x)
+        x = x.view(-1, 64 * 5 * 5)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x