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import gradio as gr
import tensorflow as tf
from PIL import Image
import numpy as np
# Laden des Modells
model_path = "brain_classification.keras"
model = tf.keras.models.load_model(model_path)
# Klassenlabels
labels = ['glioma_tumor', 'meningioma_tumor', 'no_tumor', 'pituitary_tumor']
def predict_image(image):
image = Image.fromarray(image.astype('uint8'), 'RGB')
image = image.resize((224, 224))
image = np.array(image)
prediction = model.predict(np.expand_dims(image, axis=0))
confidences = {labels[i]: float(prediction[0][i]) for i in range(len(labels))}
return confidences
# Gradio interface
iface = gr.Interface(
fn=predict_image,
inputs=gr.Image(),
outputs=gr.Label(num_top_classes=4),
title="MRI Tumor Classifier",
description="Upload your MRI image and the model will predict, if there's a tumor present"
)
iface.launch(share=True)
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