predict.py

import tensorflow as tf
import numpy as np
from PIL import Image
import time

# Load the saved model
model = tf.keras.models.load_model('C:/Users/asus/PycharmProjects/TA/ResNet50V2_Dense512_KFold10_TA_60.model')

# Preprocess the input image
def preprocess_image(image_path):
    img = Image.open(image_path)
    img = img.resize(224, 224)  # Resize the image to match the input size of the model
    img = np.array(img) / 255.0 # Normalize the pixel values to the range of [0, 1]
    img = np.expand_dims(img, axis=0)  # Add a batch dimension
    return img

# Define the class labels
class_labels = ['Glioma', 'Meningioma', 'Normal', 'Pituitary']  # Replace with your actual class labels

# Predict the brain tumor for a new image
# image_path = 'C:/Users/asus/Downloads/Testing/glioma/Te-gl_0010.jpg'
# image_path = 'C:/Users/asus/Downloads/Testing/meningioma/Te-me_0010.jpg'
# image_path = 'C:/Users/asus/Downloads/Testing/normal/Te-no_0010.jpg'
image_path = 'C:/Users/asus/Downloads/Testing/pituitary/Te-pi_0010.jpg'
preprocessed_image = preprocess_image(image_path)

# Measure the prediction time
start_time = time.time()
predictions = model.predict(preprocessed_image)
end_time = time.time()
prediction_time = end_time - start_time

predicted_class_index = np.argmax(predictions)
predicted_class_label = class_labels[predicted_class_index]
confidence = predictions[0][predicted_class_index]

print("Predicted class:", predicted_class_label)
print("Confidence:", confidence)
print("Prediction time:", prediction_time, "seconds")