app.py

import gradio as gr
import tensorflow as tf
from tensorflow.keras.preprocessing import image
import numpy as np

# Modell laden mit Fehlerbehandlung
try:
    model = tf.keras.models.load_model('pokemon_classifier_model.keras')
except Exception as e:
    print(f"Fehler beim Laden des Modells: {e}")

# Klassenlabels
class_names = ['Bisasam', 'Schiggy', 'Glumanda']

# Vorhersagefunktion
def predict(img):
    try:
        img = img.resize((224, 224))
        img_array = np.array(img)
        img_array = np.expand_dims(img_array, axis=0)
        img_array = tf.keras.applications.vgg16.preprocess_input(img_array)

        predictions = model.predict(img_array)
        score = tf.nn.softmax(predictions[0])

        return {class_names[i]: float(score[i]) for i in range(3)}
    except Exception as e:
        return {"Fehler": str(e)}

# Gradio Interface erstellen
image_input = gr.Image(type='pil')
label_output = gr.Label(num_top_classes=3)

gr.Interface(fn=predict, inputs=image_input, outputs=label_output, 
             title="Pokémon Classifier", 
             description="Laden Sie ein Bild hoch, um das Pokémon zu klassifizieren.").launch()