SingleStore April Challenge: Haiku ASCII

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!pip install matplotlib --quiet

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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.colors import LinearSegmentedColormap
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import matplotlib.image as mpimg  # Import for loading the logo image
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import requests  # Import requests to download the logo
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from PIL import Image  # Import PIL to handle the image
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from io import BytesIO  # Import BytesIO to handle the image as a byte stream
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# Seasonal Transition Haiku Generator
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def generate_seasonal_transition_haiku():
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    seasons = ["Winter", "Spring", "Summer", "Fall"]
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    transitions = {
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        ("Winter", "Spring"): [
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            "Data thaws with grace,",  # 5 syllables
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            "Queries swiftly bloom, unfurl,",  # 7 syllables
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            "Insight crisp and clear."  # 5 syllables
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        ],
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        ("Spring", "Summer"): [
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            "Insights grow in light,",  # 5 syllables
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            "Data scales the bright, warm sun,",  # 7 syllables
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            "Wisdom on full show."  # 5 syllables
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        ],
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        ("Summer", "Fall"): [
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            "Leaves of data turn,",  # 5 syllables
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            "Decisions ripe and well-earned,",  # 7 syllables
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            "Change whispers anew."  # 5 syllables
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        ],
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        ("Fall", "Winter"): [
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            "Data's frost sets in,",  # 5 syllables
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            "Vector cycles glean insights,",  # 7 syllables
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            "Silence holds the keys."  # 5 syllables
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        ],
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    }
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    # Randomly select a transition
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    start_season = np.random.choice(seasons)
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    end_season = seasons[(seasons.index(start_season) + 1) % len(seasons)]
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    haiku_lines = transitions[(start_season, end_season)]
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    return haiku_lines, start_season, end_season
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# Visualization with Gradient
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def visualize_seasonal_transition(haiku, start_season, end_season):
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    fig, ax = plt.subplots(figsize=(10, 6))
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    ax.axis('off')
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    # Seasonal Colors
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    colors = {
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        "Winter": "#ffffff",
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        "Spring": "#4caf50",
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        "Summer": "#ffa726",
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        "Fall": "#fb8c00"
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    }
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    # Create Gradient
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    cmap = LinearSegmentedColormap.from_list("season_transition", [colors[start_season], colors[end_season]])
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    gradient = np.linspace(0, 1, 256)
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    gradient = np.vstack((gradient, gradient))
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    ax.imshow(gradient, aspect='auto', cmap=cmap, extent=[0, 10, 0, 10])
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    # Display Haiku
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    ax.text(5, 5, "\n".join(haiku), ha='center', va='center', fontsize=14, color="black")
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    # Download the logo
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    logo_url = "https://raw.githubusercontent.com/singlestore-labs/spaces-notebooks/master/common/images/singlestore-logo-vertical.png"
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    response = requests.get(logo_url)
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    logo_img = Image.open(BytesIO(response.content))
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    # Convert the Image object to a NumPy array and display it
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    logo = np.array(logo_img)
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    ax_logo = fig.add_axes([0.75, -0.05, 0.15, 0.15])  # Adjust these values to position and scale the logo
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    ax_logo.imshow(logo)
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    ax_logo.axis('off')  # Hide the axis of the logo subplot
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    plt.show()
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# Generate and Visualize
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haiku, start_season, end_season = generate_seasonal_transition_haiku()
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visualize_seasonal_transition(haiku, start_season, end_season)

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!pip install Pillow requests --quiet

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import requests
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from PIL import Image
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from io import BytesIO
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# Define the ASCII characters that will be used to replace the pixels.
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ASCII_CHARS = ["@", "#", "S", "%", "?", "*", "+", ";", ":", ",", "."]
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def resize_image(image, new_width=100):
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    width, height = image.size
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    ratio = height / width / 1.65  # Correct for aspect ratio
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    new_height = int(new_width * ratio)
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    resized_image = image.resize((new_width, new_height))
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    return resized_image
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def grayify(image):
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    grayscale_image = image.convert("L")
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    return grayscale_image
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def pixels_to_ascii(image):
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    pixels = image.getdata()
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    ascii_str = ''
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    for pixel in pixels:
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        ascii_str += ASCII_CHARS[pixel // 25]  # Map the pixel value to ASCII_CHARS
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    return ascii_str
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def process_image_from_url(image_url, new_width=100):
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    # Fetch the image from the URL
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    response = requests.get(image_url)
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    if response.status_code == 200:
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        # Open the image from the bytes in response content
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        image = Image.open(BytesIO(response.content))
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        # Process the image
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        image = resize_image(image, new_width)
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        image = grayify(image)
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        ascii_str = pixels_to_ascii(image)
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        # Format the ASCII string so that each line has `new_width` characters.
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        img_width = image.width
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        ascii_str_len = len(ascii_str)
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        ascii_img = ""
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        for i in range(0, ascii_str_len, img_width):
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            ascii_img += ascii_str[i:i+img_width] + "\n"
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        # Print the ASCII art
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        print(ascii_img)
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    else:
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        print("Failed to retrieve the image from the URL")
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# Example usage with a public image URL
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image_url = 'https://raw.githubusercontent.com/singlestore-labs/spaces-notebooks/master/common/images/singlestore-banner.png'  # Replace with your image's URL
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process_image_from_url(image_url, 100)