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app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+from torchvision import transforms, models
+from PIL import Image
+import numpy as np
+
+# Load model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model = models.resnet18()
+model.fc = nn.Sequential(
+    nn.Linear(512, 128), nn.ReLU(), nn.Dropout(0.3), nn.Linear(128, 10)
+)
+model.load_state_dict(torch.load("model.pth", map_location=device))
+model = model.to(device)
+model.eval()
+
+# Preprocessing
+transform = transforms.Compose(
+    [
+        transforms.Grayscale(num_output_channels=3),
+        transforms.Resize((32, 32)),
+        transforms.ToTensor(),
+        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+    ]
+)
+
+
+def predict_digit(image):
+    if image is None:
+        return {str(i): 0.0 for i in range(10)}
+
+    # Sketchpad returns a dict with "composite" key (RGBA numpy array)
+    # or directly a numpy array depending on Gradio version
+    if isinstance(image, dict):
+        image = image.get("composite", image.get("layers", [None])[0])
+
+    if image is None:
+        return {str(i): 0.0 for i in range(10)}
+
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(image.astype(np.uint8))
+
+    # Convert to grayscale
+    image = image.convert("L")
+    img_array = np.array(image)
+
+    # The canvas is white (255) with dark strokes.
+    # MNIST expects black background with white digit, so invert.
+    img_array = 255 - img_array
+
+    # Check if the canvas is essentially blank (all near-zero after inversion)
+    if img_array.max() < 10:
+        return {str(i): 0.0 for i in range(10)}
+
+    image = Image.fromarray(img_array)
+
+    img_tensor = transform(image).unsqueeze(0).to(device)
+
+    # Predict
+    with torch.no_grad():
+        output = model(img_tensor)
+        probabilities = torch.nn.functional.softmax(output, dim=1)[0]
+
+    confidences = {str(i): float(probabilities[i]) for i in range(10)}
+    return confidences
+
+
+# Create Gradio interface with sketchpad (drawable white canvas)
+interface = gr.Interface(
+    fn=predict_digit,
+    inputs=gr.Sketchpad(
+        label="Draw a digit (0–9)",
+        type="numpy",
+        canvas_size=(280, 280),
+        brush=gr.Brush(colors=["#000000"], color_mode="fixed", default_size=18),
+    ),
+    outputs=gr.Label(num_top_classes=10, label="Predictions"),
+    title="Handwritten Digit Recognizer",
+    description="Draw a digit (0–9) on the white canvas below and click Predict.",
+    submit_btn="Predict",
+    clear_btn="Clear Canvas",
+)
+
+if __name__ == "__main__":
+    interface.launch(share=True)