import os
import sys

os.environ["KERAS_BACKEND"] = "tensorflow"

import numpy as np
import keras
from keras.applications.mobilenet_v2 import (
    MobileNetV2,
    decode_predictions,
    preprocess_input,
)

image_path = sys.argv[1] if len(sys.argv) > 1 else "sample.jpg"

model = MobileNetV2(weights="imagenet")
image = keras.utils.load_img(
    image_path,
    color_mode="rgb",
    target_size=(224, 224),
)
image_array = keras.utils.img_to_array(image)
image_batch = np.expand_dims(image_array, axis=0)
model_input = preprocess_input(image_batch.copy())

predictions = model.predict(model_input, verbose=0)

print(f"model input shape: {model_input.shape}")
print(f"prediction shape: {predictions.shape}")

for rank, (_, label, score) in enumerate(
    decode_predictions(predictions, top=3)[0],
    start=1,
):
    print(f"{rank}. {label}: {score:.3f}")