File size: 729 Bytes
d60e533 e0801ac d60e533 e0801ac 62eca9a 962d630 05aedf9 d60e533 d5cf581 c4011fa 6059147 d22fd20 672a9ff 837f07f a0f5a83 4766918 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | from PIL import Image
import keras
import numpy as np
import os
import tensorflow as tf
model = keras.saving.load_model("hf://sensei-ml/Brain_Tumors_Classificator_CNN_Keras_Model")
def predict(image_path):
image = Image.fromarray(image_path)
image = image.convert('L') # Use grayscale images
image = image.resize((150, 150)) # Resize to 150 x 150 px.
img_array = np.array(image) / 255.0 # Normalize pixel values
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
prediction = model.predict(img_array)
labels = ['Glioma', 'Meningioma', 'No tumor', 'Pituitary']
probabilities = {label: probability for label, probability in zip(labels, prediction[0])}
return probabilities |