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import gradio as gr |
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import numpy as np |
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from PIL import Image, ImageOps |
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import tensorflow as tf |
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model = tf.keras.models.load_model("cnn_model.h5") |
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def predict(image_array): |
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try: |
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if isinstance(image_array, dict) and 'composite' in image_array: |
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image_array = image_array['composite'] |
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if image_array is None or np.sum(image_array) == 0: |
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return "Please draw a digit." |
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image = Image.fromarray(image_array.astype("uint8"), mode="L") |
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image = image.resize((28, 28)) |
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image = image.point(lambda x: 0 if x < 128 else 255, 'L') |
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image = image.resize((28, 28)) |
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image_array = np.array(image).astype("float32") / 255.0 |
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image_array = image_array.reshape(1, 28, 28, 1) |
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logits = model.predict(image_array) |
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prediction = int(np.argmax(logits)) |
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confidence = float(tf.nn.softmax(logits)[0][prediction]) |
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return f"Digit: {prediction} (confidence: {confidence:.2%})" |
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except Exception as err: |
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return f"Runtime error: {str(err)}" |
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gr.Interface( |
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fn=predict, |
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inputs=gr.Sketchpad(image_mode="L", canvas_size=(200, 200), type="numpy"), |
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outputs="text", |
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title="Digit Classifier" |
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).launch() |
