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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
# Lade das trainierte Modell
model_path = "Fruits_fruits.keras"
model = tf.keras.models.load_model(model_path)
# Definiere die Vorhersagefunktion
def predict_fruit(image):
# Bildvorverarbeitung
image = Image.fromarray(image.astype('uint8')) # Konvertiere numpy-Array zu PIL-Bild
image = image.resize((150, 150)) # Ändere die Bildgröße auf 150x150
image = np.array(image)
image = np.expand_dims(image, axis=0) # Füge Batch-Dimension hinzu
image = image / 255.0 # Normiere die Pixelwerte auf den Bereich [0, 1]
# Vorhersage
prediction = model.predict(image)
# Softmax anwenden, um Wahrscheinlichkeiten für jede Klasse zu erhalten
probabilities = tf.nn.softmax(prediction)
# Klassenbezeichnungen für die Früchte
fruit_classes = ['Apple', 'Lemon', 'Strawberry']
probabilities_dict = {fruit_class: round(float(probability), 2) for fruit_class, probability in zip(fruit_classes, probabilities[0])}
return probabilities_dict
# Erstelle die Gradio-Schnittstelle
input_image = gr.inputs.Image(shape=(150, 150), label="Upload a Fruit Image")
output_label = gr.outputs.Label(num_top_classes=3, label="Prediction")
iface = gr.Interface(
fn=predict_fruit,
inputs=input_image,
outputs=output_label,
live=True,
title="Fruit Classification",
description="Upload an image of an Apple, Lemon, or Strawberry to get the predicted class. The model uses a CNN trained on the Fruits-360 dataset.",
examples=["images/01.jpg", "images/02.jpg", "images/03.jpg"],
theme="dark"
)
iface.launch()
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