Delete app.py

app.py

@@ -1,44 +0,0 @@
-import gradio as gr
-import tensorflow as tf
-from PIL import Image
-import numpy as np
-
-# Laden des vortrainierten Pokémon-Modells
-model_path = "kia_pokemon_keras_model.h5"
-model = tf.keras.models.load_model(model_path)
-
-# Labels für den Pokémon Classifier (angepasst an Ihre Klassen)
-labels = [
-    'Charmander', 'Bulbasaur', 'Squirtle'
-]
-
-def predict_pokemon(image):
-    # Bild konvertieren und Größe anpassen
-    image = Image.fromarray(image).resize((224, 224))
-    # Normalisieren und in ein numpy-Array umwandeln
-    image = np.array(image) / 255.0
-    # Reshape für das Modell (erwarte 3 Farbkanäle)
-    image = image.reshape(1, 224, 224, 3)
-
-    # Vorhersage treffen
-
-    predictions = model.predict(image)
-    prediction = np.argmax(predictions, axis=1)[0]
-    confidence = np.max(predictions)
-
-    # Vorbereiten der Ausgabe
-    result = f"Predicted Pokémon: {labels[prediction]} with confidence: {confidence:.2f}"
-    return result
-
-# Erstellen der Gradio-Oberfläche
-input_image = gr.Image(width=224, height=224, image_mode='RGB') 
-output_label = gr.Label()
-interface = gr.Interface(fn=predict_pokemon,
-                         inputs=input_image,
-                         outputs=output_label,
-                         examples=["images/bulbasaur.png", "images/charmander.png", "images/squirtle.png"], 
-                         title="Pokémon Classifier",
-                         description="Drag and drop an image or select an example below to predict the Pokémon.")
-
-# Interface starten
-interface.launch()