from huggingface_hub import from_pretrained_keras
from PIL import Image
import keras
import numpy as np
import os
import tensorflow as tf

model = keras.saving.load_model("model/model.keras")

def predict(image_path):
    image = Image.fromarray(image_path)
    image = image.convert('L')
    image = image.resize((150, 150))  # Resize to match your model's input size
    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