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app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+
+# Load the model
+model = tf.keras.models.load_model("my_mnist_model.keras")
+
+def predict_digit(image):
+    # Convert to grayscale if needed
+    if image.ndim == 3:
+        image = image[..., 0]
+    # Resize and preprocess
+    image = np.array(image).astype("float32")
+    image = image.reshape(1, 28, 28)
+    image = image / 255.0
+    prediction = model.predict(image)
+    return str(np.argmax(prediction))
+
+iface = gr.Interface(
+    fn=predict_digit,
+    inputs=gr.Image(shape=(28, 28), image_mode='L', source="canvas", tool="editor"),
+    outputs=gr.Label(num_top_classes=1),
+    title="MNIST Digit Classifier",
+    description="Draw a digit (0-9) and the model will predict it."
+)
+
+if __name__ == "__main__":
+    iface.launch() 

requirements.txt

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+tensorflow
+gradio
+numpy