有没有好心的大老看一下

import os
import cv2
import numpy as np
from tensorflow import keras

def preprocess_image(image_path):
    # Load image
    image = cv2.imread(image_path)
    # Resize image
    resized_image = cv2.resize(image, (224, 224))
    # Convert color channels from BGR to RGB
    rgb_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)
    # Normalize image data
    normalized_image = rgb_image / 255.0
    return normalized_image

def predict_images(image_list, model_path):
    # Load model
    model = keras.models.load_model(model_path)

    # Create an empty list to store the predictions
    predictions = []

    try:
        for image_path in image_list:
            # Preprocess the image
            processed_image = preprocess_image(image_path)
            # Add an extra dimension to match the input shape of the model
            input_image = np.expand_dims(processed_image, axis=0)
            # Predict image class probabilities
            prediction = model.predict(input_image)
            # Get the predicted class label
            predicted_label = np.argmax(prediction, axis=1)
            predictions.append(predicted_label)

        return predictions

    except ValueError:
        return "ValueError occurred during prediction."

# Set the path to the image folder and the model file
image_folder = "path/to/image/folder"
model_file = "path/to/model.h5"

# Get a list of image file paths from the image folder
image_list = [os.path.join(image_folder, file) for file in os.listdir(image_folder) if file.endswith(".jpg")]

# Call the predict_images function
result = predict_images(image_list, model_file)