pipeline.py

# load the model first and pass as an argument
model = VGG16()
model = Model(inputs = model.inputs, outputs = model.layers[-2].output)

def extract_features(file, model):
    # load the image as a 224x224 array
    img = load_img(file, target_size=(224,224))
    # convert from 'PIL.Image.Image' to numpy array
    img = np.array(img) 
    # reshape the data for the model reshape(num_of_samples, dim 1, dim 2, channels)
    reshaped_img = img.reshape(1,224,224,3) 
    # prepare image for model
    imgx = preprocess_input(reshaped_img)
    # get the feature vector
    features = model.predict(imgx, use_multiprocessing=True)
    return features