Amazing-Python-Scripts

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import tensorflow as tf
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import tensorflow.keras.applications.mobilenet_v2 as mobilenet
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from tensorflow.keras.preprocessing import image
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from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions
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import numpy as np
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def classify_image(image_path):
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    # Load the pre-trained MobileNetV2 model
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    model = mobilenet.MobileNetV2(weights='imagenet')
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    # Load and preprocess the image
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    img = image.load_img(image_path, target_size=(224, 224))
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    img_array = image.img_to_array(img)
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    img_array = np.expand_dims(img_array, axis=0)
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    processed_img = preprocess_input(img_array)
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    # Perform image classification
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    predictions = model.predict(processed_img)
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    decoded_predictions = decode_predictions(predictions, top=3)[0]
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    return decoded_predictions
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# Example usage
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image_path = "image.jpg"
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predictions = classify_image(image_path)
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# Print the top 3 predictions
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for prediction in predictions:
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    print(f"{prediction[1]}: {prediction[2]*100:.2f}%")
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