I am not able to display correct prediction of an array containing 4 folder of images

from PIL import Image

import cv2

import time

def load_and_preprocess_image(img_path, target_size=(112,112)):

try:

    img = Image.open(img_path)

    img = img.resize(target_size)

    img_array = np.array(img)

    img_array = img_array / 255.0  # Normalize to [0, 1] range

    return img_array

except Exception as e:

    print(f"Error loading image {img_path}: {e}")

    return None

def load_images_from_folders(folders, target_size):

images = []

folder_images = []

for folder in folders:

    if not os.listdir(folder):

      raise ValueError(f"Folder {folder} is empty.")

    for img_name in os.listdir(folder):

        img_path = os.path.join(folder, img_name)

        if img_name.lower().endswith(( '.jpg', )):

            img_array = load_and_preprocess_image(img_path, target_size)

            if img_array is not None:

                folder_images.append(img_array)

    images.append(np.concatenate(folder_images, axis=0))

return images 

def main(Model_Enhancer, folders, target_size=(112, 112)):

images = load_images_from_folders(folders, target_size)

num_images = min(len(images[0]), len(images[1]), len(images[2]), len(images[3]))

Input_Sample1 = tf.keras.Input(shape=(112, 112, 3), name='Input_Sample1')

Input_Sample2 = tf.keras.Input(shape=(112, 112, 3), name='Input_Sample2')

Input_Sample3 = tf.keras.Input(shape=(112, 112, 3), name='Input_Sample3')

Input_Sample4 = tf.keras.Input(shape=(112, 112, 3), name='Input_Sample4')

Input_Sample1 = np.concatenate(images[0][:num_images], axis=0)

Input_Sample1 = Input_Sample1.reshape((-1, 112, 112, 3))

Input_Sample2 = np.concatenate(images[1][:num_images], axis=0)

Input_Sample2 = Input_Sample2.reshape((-1, 112, 112, 3))

Input_Sample3 = np.concatenate(images[2][:num_images], axis=0)

Input_Sample3 = Input_Sample3.reshape((-1, 112, 112, 3))

Input_Sample4 = np.concatenate(images[2][:num_images], axis=0)

Input_Sample4 = Input_Sample4.reshape((-1, 112, 112, 3))







predictions = Model_Enhancer.predict([Input_Sample1,Input_Sample2,Input_Sample3,Input_Sample4])



for i, prediction in enumerate(predictions):

    output_file = os.path.join('/content/drive/MyDrive/Dataset', f'output_{[i]}.jpg')

    normalized_prediction = (prediction - prediction.min()) / (prediction.max() - prediction.min())

    plt.imshow(normalized_prediction)

    plt.title(f"Prediction: {np.argmax(prediction)}")

    plt.axis('off')

    plt.show()

    prediction_img = Image.fromarray((normalized_prediction * 255).astype(np.uint8))

    prediction_img.save(output_file)

    print(f'Output image saved as {output_file}')

if name == “main“:

input_shape = (112, 112, 3)

Output =merge(Input_Sample1,Input_Sample2,Input_Sample3,Input_Sample4)

Model_Enhancer = tf.keras.Model(inputs=[Input_Sample1,Input_Sample2,Input_Sample3,Input_Sample4], outputs=Output)

folders = [

   "/content/drive/MyDrive/Dataset/input_wb_train",

   "/content/drive/MyDrive/Dataset/input_gc_train",

   "/content/drive/MyDrive/Dataset/input_ce_train",

   "/content/drive/MyDrive/Dataset/input_train"

]  # Change these to your image folder paths

main(Model_Enhancer, folders,target_size=(112, 112))

Help me with this code or give me the correct to get correct prediction of images

I was expecting a correcting prediction of images, though, images are displayed still those are not the desired images

Enhanced output image
Here it is showing

KeyError Traceback (most recent call last)
/usr/local/lib/python3.10/dist-packages/PIL/Image.py in fromarray(obj, mode)
3079 try:
-> 3080 mode, rawmode = _fromarray_typemap[typekey]
3081 except KeyError as e:

KeyError: ((1, 1, 112, 3), ‘|u1’)

The above exception was the direct cause of the following exception:

TypeError Traceback (most recent call last)
2 frames
/usr/local/lib/python3.10/dist-packages/PIL/Image.py in fromarray(obj, mode)
3081 except KeyError as e:
3082 msg = “Cannot handle this data type: %s, %s” % typekey
-> 3083 raise TypeError(msg) from e
3084 else:
3085 rawmode = mode

TypeError: Cannot handle this data type: (1, 1, 112, 3), |u1