Model predicting image in bulking

bulk_model_testing.py

import os
from keras.models import load_model
from PIL import Image, ImageOps
import numpy as np

# Load the model
model = load_model("keras_Model.h5", compile=False)

# Load the labels
class_names = open("labels.txt", "r").readlines()

# Define the image folder
image_folder = "/Users/ericjulianto/Downloads/FINAL_DATASET_BARANG/test/Asli"

# Loop through all images in the folder and make predictions
for filename in os.listdir(image_folder):
    if filename.endswith(".jpg") or filename.endswith(".png"):  # Filter only image files
        # Read the image
        image_path = os.path.join(image_folder, filename)
        image = Image.open(image_path).convert("RGB")

        # Resize and normalize the image
        size = (224, 224)
        image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
        image_array = np.asarray(image)
        normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1

        # Load the image into the array
        data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
        data[0] = normalized_image_array

        # Predict with the model
        prediction = model.predict(data)
        index = np.argmax(prediction)
        class_name = class_names[index]
        confidence_score = prediction[0][index]

        # Print the prediction result
        print("File:", filename)
        print("Class:", class_name)
        print("Confidence Score:", confidence_score)