import gradio as gr
import numpy as np
from PIL import Image

def dot_effect(input_image):
    """
    Convert input image to dotted effect similar to the example
    
    Args:
        input_image (ndarray): Input image
    
    Returns:
        ndarray: Image with dotted effect
    """
    # Convert input to grayscale
    gray_image = np.mean(input_image, axis=2)
    
    # Normalize pixel values
    normalized = (gray_image - gray_image.min()) / (gray_image.max() - gray_image.min())
    
    # Create dot mask
    dot_size = 5  # Adjust dot size as needed
    height, width = gray_image.shape
    dot_mask = np.zeros((height, width, 3), dtype=np.uint8)
    
    for y in range(0, height, dot_size):
        for x in range(0, width, dot_size):
            if normalized[y, x] > 0.3:  # Threshold for dot appearance
                radius = int(dot_size * normalized[y, x])
                center_x, center_y = x + dot_size//2, y + dot_size//2
                
                for dy in range(-radius, radius):
                    for dx in range(-radius, radius):
                        if dx*dx + dy*dy <= radius*radius:
                            px, py = center_x + dx, center_y + dy
                            if 0 <= px < width and 0 <= py < height:
                                dot_mask[py, px] = [255, 255, 255]
    
    return dot_mask

# Create Gradio interface
iface = gr.Interface(
    fn=dot_effect,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Image(type="numpy"),
    title="ChatGPT Ad Maker",
    description="Transform images into dotted effect"
)

# Launch the app
iface.launch()