model.py

from keras.models import load_model  # TensorFlow is required for Keras to work
from PIL import Image, ImageOps  # Install pillow instead of PIL
import numpy as np


def get_class(model_path, labels_path, image_path):
    np.set_printoptions(suppress=True)
    model = load_model(model_path, compile=False)
    class_names = open(labels_path, "r").readlines()
    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
    
    image = Image.open(image_path).convert("RGB")
    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

    data[0] = normalized_image_array
    prediction = model.predict(data)
    index = np.argmax(prediction)
    class_name = class_names[index]
    confidence_score = prediction[0][index]
    print("Class:", class_name[2:], end="")
    print("Confidence Score:", confidence_score)
    
    return class_name[2:]