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app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image, ImageOps
+
+# Load the model directly
+# reliable for Spaces where you upload the model file alongside the app
+model = tf.keras.models.load_model("mnist_model.keras")
+
+def predict_digit(image):
+    if image is None:
+        return None
+    
+    # 1. Convert to grayscale
+    image = image.convert('L')
+    
+    # 2. Resize to 28x28 to match training data
+    image = image.resize((28, 28))
+    
+    # 3. Invert colors (MNIST is white text on black background)
+    # Most user uploads are black text on white background (paper), so we usually need to invert
+    # We check mean pixel value; if > 127, it's likely a white background.
+    if np.mean(image) > 127:
+        image = ImageOps.invert(image)
+        
+    # 4. Convert to numpy array and normalize
+    image_array = np.array(image) / 255.0
+    
+    # 5. Flatten to shape (1, 784) as expected by the Dense input layer
+    image_array = image_array.reshape(1, 784)
+    
+    # Predict
+    prediction = model.predict(image_array)
+    
+    # Return dictionary for Gradio Label output
+    return {str(i): float(prediction[0][i]) for i in range(10)}
+
+iface = gr.Interface(
+    fn=predict_digit,
+    inputs=gr.Image(type="pil", label="Upload an Image"),
+    outputs=gr.Label(num_top_classes=3, label="Predictions"),
+    title="MNIST Digit Classifier",
+    description="Upload an image of a handwritten digit (0-9) to see the prediction. Works best with a single digit centered in the image."
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+tensorflow
+gradio
+numpy
+pillow