import numpy as np
from tensorflow.keras.preprocessing.image import img_to_array

IMG_SIZE = 128
# Removed 'trash' from the list
CLASS_NAMES = ['cardboard', 'glass', 'metal', 'paper', 'plastic']

def preprocess_image(img):
    img = img.resize((IMG_SIZE, IMG_SIZE))
    img = img_to_array(img)
    img = img / 255.0
    img = np.expand_dims(img, axis=0)  # Add batch dimension
    return img

def predict_image(model, image):
    processed = preprocess_image(image)
    prediction = model.predict(processed)
    class_idx = np.argmax(prediction)
    confidence = float(np.max(prediction))
    label = CLASS_NAMES[class_idx]
    return label, confidence, class_idx, processed