So I was thinking of making or monetizing the background remover that I created. So I created this Python script that checks a folder and then takes all images in the folder and then removes the background, right, and then adds a, well, basically makes the image into the format that Zando requires. So now I'm trying to create a, you know, full app that can say something like, you know, guaranteed to improve, to reduce rejection from, you know, company based on our app. So people would just put their image that they're trying to, you know, use for selling on Zando. But then, yeah, that is one part of the app. It can do that, right. And also another part is, you know, creating it for content creators, YouTubers, so they can use it for creating thumbnails, basically. So just to, yeah, just to, yeah, just to remove the background, basically. And then, yeah, maybe later on we will add a couple of more improvements. But I want to build this app. I'm not really sure about the tech spec. I heard about Remix. So I was thinking of, you know, doing something with, um, maybe not Remix, maybe just Next.js using the app router. And then using ShadCN, you know. And I also want some app flow that is unique, as well as a design that is, you know, compelling that will make people, you know, want to stand aside or something, you know. Like the app flow is just unique, how, you know, you're placing, you know, the pictures to remove the background for the different sides of the app, you know. But I guess we can start with one initial plan. You can suggest which one we start with and then build on that. For the section regarding Zando, you know, I'm planning to, you know, incorporate other companies, like take a lot of image guidelines for, yeah, for generating the images for it. That will be later on for that section. But for now, I'll just use the Zando guideline, which I already have in Python code, you know. So, yeah. I might also going to need the spec, right? I was thinking SuperBase. and then using vercel serverless function. So, yeah. I'm planning to monetize this in South Africa. I'll obviously need, also, a plan on, you know, the amount that should be generally used, you know, for the monetization, like how much is it going to be for the pictures and all that stuff. I need those details, as well. include the app flow. below is the code for background removing  and formatting for zando guidelines . the code is optimised for cpu but there is a gpu version, I am trying to build everything for free though so if you have a recommendation that will be great too
"""
import os
from PIL import Image
import io
from transparent_background import Remover

def remove_background_transparent(input_path):
    # Initialize the Remover
    remover = Remover()
    
    # Open the image
    img = Image.open(input_path).convert('RGB')
    
    # Process the image
    out = remover.process(img)
    
    return out

def resize_and_compress_image(img, output_path, max_size=200*1024):
    # Ensure image is in RGBA mode to preserve transparency
    img = img.convert('RGBA')
    
    # Calculate new dimensions while maintaining aspect ratio
    aspect_ratio = img.width / img.height
    if aspect_ratio > 1:
        new_width = 2000
        new_height = int(2000 / aspect_ratio)
    else:
        new_height = 2000
        new_width = int(2000 * aspect_ratio)
    
    # Resize the image
    img = img.resize((new_width, new_height), Image.LANCZOS)
    
    # Create a new grey background image of 2000x2000
    background = Image.new('RGB', (2000, 2000), (240, 240, 240))
    
    # Paste the resized image onto the center of the grey background
    offset = ((2000 - new_width) // 2, (2000 - new_height) // 2)
    background.paste(img, offset, img)
    
    # Start with high quality
    quality = 95
    
    while True:
        # Save the image to a buffer
        buffer = io.BytesIO()
        background.save(buffer, format="JPEG", quality=quality, dpi=(300, 300), optimize=True)
        
        # Check the file size
        file_size = buffer.tell()
        
        if file_size <= max_size:
            # If the file size is smaller than or equal to max_size, save and exit
            with open(output_path, 'wb') as f:
                f.write(buffer.getvalue())
            print(f"Resized image saved. Size: {file_size / 1024:.2f} KB")
            break
        elif quality > 1:
            # If file size is too large, decrease quality and try again
            quality -= 5  # Decrease by 5 to speed up the process
            if quality < 1:
                quality = 1
        else:
            # If we've reached minimum quality and still over max_size,
            # save as is (it will be larger than 200KB but lowest quality)
            with open(output_path, 'wb') as f:
                f.write(buffer.getvalue())
            print(f"Resized image saved at min quality. Size: {file_size / 1024:.2f} KB")
            break

def process_images(input_folder, output_folder):
    if not os.path.exists(output_folder):
        os.makedirs(output_folder)
    
    for filename in os.listdir(input_folder):
        if filename.lower().endswith(('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp')):
            input_path = os.path.join(input_folder, filename)
            output_filename = f"processed_transparent_{os.path.splitext(filename)[0]}.jpg"
            output_path = os.path.join(output_folder, output_filename)
            
            # Remove background using transparent-background
            img_no_bg = remove_background_transparent(input_path)
            
            # Resize and compress
            resize_and_compress_image(img_no_bg, output_path, max_size=200*1024)
            print(f"Processed: {filename}")

if __name__ == "__main__":
    # Input the path to your folder here
    input_folder = r"C:\Users\daniela\Documents\takealot-image-extract\testza"
    
    # Output folder will be created in the same directory as the input folder
    output_folder = os.path.join(os.path.dirname(input_folder), "processed_testza_transparent")
    
    process_images(input_folder, output_folder)
    
    print(f"All images processed. Output saved in: {output_folder}")
"""