app.py

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'

import gradio as gr
import tensorflow as tf
from PIL import Image
import numpy as np

# Modellpfad
model_path = "pokemon_classifier_model.keras"

# Modell laden
model = tf.keras.models.load_model(model_path)

# Klassenlabels (Passe diese entsprechend deinem Modell an)
labels = ['Abra', 'Cloyster', 'Dodrio']

# Vorhersagefunktion
def predict(image):
    try:
        # Bildvorverarbeitung
        image = image.resize((150, 150))  # Bild auf 150x150 skalieren
        image = np.array(image) / 255.0
        image = np.expand_dims(image, axis=0)
        
        # Vorhersage
        predictions = model.predict(image)
        confidences = {labels[i]: float(predictions[0][i]) for i in range(len(labels))}
        
        return confidences
    except Exception as e:
        return str(e)  # Fehlernachricht zurückgeben

# Gradio-Interface erstellen
iface = gr.Interface(
    fn=predict, 
    inputs=gr.Image(type="pil"),  # Bild als PIL-Objekt
    outputs=gr.Label(),
    description="Pokémon Classifier"
)

if __name__ == "__main__":
    iface.launch()