Create app.py

app.py

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+import gradio as gr
+import numpy as np 
+from PIL import Image, ImageOps
+import tensorflow as tf
+
+#Load the model 
+model = tf.keras.models.load_model("cnn_model.h5")
+
+def predict(image_array):
+    if image_array is None or np.sum(image_array) == 0:
+        return "Please draw a digit."
+
+    image = Image.fromarray(image_array.astype("uint8"), mode="L")
+    image = ImageOps.invert(image).resize((28, 28))
+
+    image_array = np.array(image).astype("float32") / 255.0
+    image_array = image_array.reshape(1, 28, 28, 1)
+
+    logits = model.predict(image_array)
+    prediction = np.argmax(logits)
+    confidence = tf.nn.softmax(logits)[0][prediction].numpy()
+
+    return f"Digit: {prediction} (confidence: {confidence:.2%})"
+
+gr.Interface(
+    fn=predict,
+    inputs=gr.Sketchpad(image_mode="L", canvas_size=(200, 200)),
+    outputs="text",
+    title="Digit Classifier",
+).launch()