Cell 27

image = 'images/train/fear/2.jpg'
print("original image is of fear")
img = ef(image)
pred = model.predict(img)
pred_label = label[pred.argmax()]
print("model prediction is ",pred_label)
plt.imshow(img.reshape(48,48),cmap='gray')

What the code could have been:

import os
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.preprocessing.image import load_img

def load_image(image_path):
    """
    Load an image from the given path.
    
    Args:
    image_path (str): Path to the image file.
    
    Returns:
    PIL Image: Loaded image.
    """
    return load_img(image_path, target_size=(48, 48))

def preprocess_image(image):
    """
    Resize and convert image to grayscale.
    
    Args:
    image (PIL Image): Image to preprocess.
    
    Returns:
    numpy array: Preprocessed image.
    """
    return np.array(image)[:, :, 0]

def plot_image(image):
    """
    Plot the given image.
    
    Args:
    image (numpy array): Image to plot.
    """
    plt.imshow(image, cmap='gray')
    plt.show()

def main():
    # Load image
    image_path = 'images/train/fear/2.jpg'
    original_label = "fear"
    print(f"Original image is of {original_label}")

    # Load and preprocess image
    image = load_image(image_path)
    img = preprocess_image(image)

    # Make predictions
    model = tf.keras.models.load_model('path/to/model.h5')
    pred = model.predict(img)
    pred_label = 'anger' if pred.argmax() == 0 else 'fear'  # Replace with actual label
    print(f"Model prediction is {pred_label}")

    # Plot image
    plot_image(img)

if __name__ == "__main__":
    main()