In this page we have some examples of the https://github.com/mannyray/jekyll-expand tag plugin. Start with the one below… press the “Show more” followed by the “Show less” button to see the style and transitions of the buttons.

Here is some text followed by other markdown features like a photo. Sometimes you only want to show the content to the user that chooses to see it while keeping the overall content of the page slim.

This plugin is also really good for long pieces of code - you want your user to be able to copy paste and use the code, but not take away from the concise way you present your content:

import face_recognition
import os
import uuid
from PIL import Image

# --- Configuration ---
# Folder with original images
input_folder = 'input_images'
# Where to save cropped faces      
faces_folder = 'extracted_faces'
# Where to save embeddings (as .npy files) 
embeddings_folder = 'embeddings'   

# Create output folders if they don't exist
os.makedirs(faces_folder, exist_ok=True)
os.makedirs(embeddings_folder, exist_ok=True)

# Loop through all files in the input folder
for filename in os.listdir(input_folder):
    if not filename.lower()
        .endswith(('.png', '.jpg', '.jpeg')):
        continue  # Skip non-image files

    image_path = os.path.join(input_folder, filename)
    image = face_recognition.load_image_file(image_path)

    # Find face locations
    face_locations = face_recognition
        .face_locations(image)

    # Compute embeddings for each face
    face_encodings = face_recognition
        .face_encodings(image, face_locations)

    
    for i, (location, encoding) in 
        enumerate(zip(face_locations, face_encodings)):
        top, right, bottom, left = location

        # Crop the face from the original image
        face_image = Image.
            fromarray(image[top:bottom, left:right])
        face_filename = f"{uuid.uuid4()}.jpg"
        face_path = os.path
            .join(faces_folder, face_filename)
        face_image.save(face_path)

        # Save the face embedding as a .npy file
        embedding_filename = face_filename
            .replace('.jpg', '.npy')
        embedding_path = os.path
            .join(embeddings_folder, embedding_filename)

        # Save as numpy array
        import numpy as np
        np.save(embedding_path, encoding)

        print(f"Saved face and embedding:
            {face_filename}")

print("✅ Done extracting and computing embeddings.")

You can also change the preview height to show more/less content. This section below is identical to the one above it, but just has a smaller preview.

import face_recognition
import os
import uuid
from PIL import Image

# --- Configuration ---
# Folder with original images
input_folder = 'input_images'
# Where to save cropped faces      
faces_folder = 'extracted_faces'
# Where to save embeddings (as .npy files) 
embeddings_folder = 'embeddings'   

# Create output folders if they don't exist
os.makedirs(faces_folder, exist_ok=True)
os.makedirs(embeddings_folder, exist_ok=True)

# Loop through all files in the input folder
for filename in os.listdir(input_folder):
    if not filename.lower()
        .endswith(('.png', '.jpg', '.jpeg')):
        continue  # Skip non-image files

    image_path = os.path.join(input_folder, filename)
    image = face_recognition.load_image_file(image_path)

    # Find face locations
    face_locations = face_recognition
        .face_locations(image)

    # Compute embeddings for each face
    face_encodings = face_recognition
        .face_encodings(image, face_locations)

    
    for i, (location, encoding) in 
        enumerate(zip(face_locations, face_encodings)):
        top, right, bottom, left = location

        # Crop the face from the original image
        face_image = Image.
            fromarray(image[top:bottom, left:right])
        face_filename = f"{uuid.uuid4()}.jpg"
        face_path = os.path
            .join(faces_folder, face_filename)
        face_image.save(face_path)

        # Save the face embedding as a .npy file
        embedding_filename = face_filename
            .replace('.jpg', '.npy')
        embedding_path = os.path
            .join(embeddings_folder, embedding_filename)

        # Save as numpy array
        import numpy as np
        np.save(embedding_path, encoding)

        print(f"Saved face and embedding:
            {face_filename}")

print("✅ Done extracting and computing embeddings.")

Finally, you can also modify the button text such as below:

import face_recognition
import os
import uuid
from PIL import Image

# --- Configuration ---
# Folder with original images
input_folder = 'input_images'
# Where to save cropped faces      
faces_folder = 'extracted_faces'
# Where to save embeddings (as .npy files) 
embeddings_folder = 'embeddings'   

# Create output folders if they don't exist
os.makedirs(faces_folder, exist_ok=True)
os.makedirs(embeddings_folder, exist_ok=True)

# Loop through all files in the input folder
for filename in os.listdir(input_folder):
    if not filename.lower()
        .endswith(('.png', '.jpg', '.jpeg')):
        continue  # Skip non-image files

    image_path = os.path.join(input_folder, filename)
    image = face_recognition.load_image_file(image_path)

    # Find face locations
    face_locations = face_recognition
        .face_locations(image)

    # Compute embeddings for each face
    face_encodings = face_recognition
        .face_encodings(image, face_locations)

    
    for i, (location, encoding) in 
        enumerate(zip(face_locations, face_encodings)):
        top, right, bottom, left = location

        # Crop the face from the original image
        face_image = Image.
            fromarray(image[top:bottom, left:right])
        face_filename = f"{uuid.uuid4()}.jpg"
        face_path = os.path
            .join(faces_folder, face_filename)
        face_image.save(face_path)

        # Save the face embedding as a .npy file
        embedding_filename = face_filename
            .replace('.jpg', '.npy')
        embedding_path = os.path
            .join(embeddings_folder, embedding_filename)

        # Save as numpy array
        import numpy as np
        np.save(embedding_path, encoding)

        print(f"Saved face and embedding:
            {face_filename}")

print("✅ Done extracting and computing embeddings.")