import keras
from keras.src.utils import load_img
from keras.src.applications.densenet import preprocess_input
import numpy as np

pokedex = keras.saving.load_model("pokedex.keras")

image = load_img('image.png', target_size=(224, 224))

x = keras.utils.img_to_array(image)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

preds = pokedex.predict(x)

# Important: You must ensure that the Pokémon are ordered alphabetically, as the model was trained using this sequence. You can obtain the .txt file from the following link: https://huggingface.co/spaces/RogerKoala/Pokedex/blob/main/Pokemons.txt

with open('Pokemons.txt', 'r') as f:
  class_labels = f.read().splitlines()

top_indices = preds[0].argsort()[-3:][::-1] 

for i in top_indices:
    print(f"{class_labels[i]}: {preds[0][i]*100:.2f}%")