import gradio as gr
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import img_to_array, load_img
import numpy as np

# Modell laden
model = load_model('pokemon-model.keras')

def classify_image(image):
    image = image.resize((224, 224))  # passende Größe für das Modell
    image = img_to_array(image)  # Bild in Array umwandeln
    image = np.expand_dims(image, axis=0)  # Dimension hinzufügen
    image /= 255.0  # Normalisierung

    prediction = model.predict(image)  # Vorhersage vom Modell
    classes = ['Squirtle', 'Pikachu', 'Charizard', 'Butterfree']  # Klassen
    return {classes[i]: float(prediction[0][i]) for i in range(4)}  # Wahrscheinlichkeiten zurückgeben

iface = gr.Interface(
    classify_image,
    gr.inputs.Image(shape=(224, 224)),
    gr.outputs.Label(num_top_classes=4),
    title="Pokémon Classifier",
    description="Upload an image of a Pokémon and see the model classify it!"
)

iface.launch()