App

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+MODEL_PATH = "mnist_cnn.h5"
+
+model = tf.keras.models.load_model(MODEL_PATH)
+
+def preprocess(image):
+    image = image.convert("L").resize((28, 28))
+    img = np.array(image).astype("float32") / 255.0
+    img = np.expand_dims(img, axis=-1)
+    img = np.expand_dims(img, axis=0)
+    return img
+
+def predict(image):
+    img = preprocess(image)
+    preds = model.predict(img)[0]
+    return {str(i): float(preds[i]) for i in range(10)}
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Draw or upload a digit"),
+    outputs=gr.Label(num_top_classes=3),
+    title="MNIST Handwritten Digit Classifier",
+    description="CNN trained on MNIST to recognize handwritten digits (0–9)."
+)
+
+demo.launch()

mnist_cnn.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7f2bc89651d7db9086cb35c55b54f27b32fe42de5985dd3384f7795839a0914b
+size 4368752

requirements.txt

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+tensorflow>=2.11
+numpy
+pillow
+gradio