app.py

import torch
import gradio as gr
from PIL import Image
import qrcode
from pathlib import Path
from multiprocessing import cpu_count
import requests
import io
import os
from PIL import Image
import spaces
import numpy as np
import cv2
from pyzxing import BarCodeReader
from PIL import ImageOps, ImageEnhance, ImageFilter
from huggingface_hub import hf_hub_download, snapshot_download
from PIL import ImageEnhance

from diffusers import (
    StableDiffusionPipeline,
    StableDiffusionControlNetImg2ImgPipeline,
    StableDiffusionControlNetPipeline,
    ControlNetModel,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    DEISMultistepScheduler,
    HeunDiscreteScheduler,
    EulerDiscreteScheduler,
)

from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

USERNAME = os.getenv("USERNAME")
PASSWORD = os.getenv("PASSWORD")

qrcode_generator = qrcode.QRCode(
    version=1,
    error_correction=qrcode.ERROR_CORRECT_H,
    box_size=10,
    border=4,
)


# Define available models
CONTROLNET_MODELS = {
    "QR Code Monster": "monster-labs/control_v1p_sd15_qrcode_monster/v2/",
    "QR Code": "DionTimmer/controlnet_qrcode-control_v1p_sd15",
    # Add more ControlNet models here
}

DIFFUSION_MODELS = {
    "GhostMix": "digiplay/GhostMixV1.2VAE",
    "Stable v1.5": "Jiali/stable-diffusion-1.5",
    # Add more diffusion models here
}

# Global variables to store loaded models
loaded_controlnet = None
loaded_pipe = None

def load_models_on_launch():
    global loaded_controlnet, loaded_pipe
    print("Loading models on launch...")
    
    # Download the main repository
    main_repo_path = snapshot_download("monster-labs/control_v1p_sd15_qrcode_monster")
    
    # Construct the path to the subfolder
    controlnet_path = os.path.join(main_repo_path, "v2")
    
    loaded_controlnet = ControlNetModel.from_pretrained(
        controlnet_path,
        torch_dtype=torch.float16
    ).to("cuda")

    diffusion_path = snapshot_download(DIFFUSION_MODELS["GhostMix"])
    loaded_pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
        diffusion_path,
        controlnet=loaded_controlnet,
        torch_dtype=torch.float16,
        safety_checker=None,
    ).to("cuda")
    print("Models loaded successfully!")

# Modify the load_models function to use global variables
def load_models(controlnet_model, diffusion_model):
    global loaded_controlnet, loaded_pipe
    if loaded_controlnet is None or loaded_pipe is None:
        load_models_on_launch()
    return loaded_pipe

# Add new functions for image adjustments
def adjust_image(image, brightness, contrast, saturation):
    if image is None:
        return None
    
    img = Image.fromarray(image) if isinstance(image, np.ndarray) else image
    
    if brightness != 1:
        img = ImageEnhance.Brightness(img).enhance(brightness)
    if contrast != 1:
        img = ImageEnhance.Contrast(img).enhance(contrast)
    if saturation != 1:
        img = ImageEnhance.Color(img).enhance(saturation)
    
    return np.array(img)

def resize_for_condition_image(input_image: Image.Image, resolution: int):
    input_image = input_image.convert("RGB")
    W, H = input_image.size
    k = float(resolution) / min(H, W)
    H *= k
    W *= k
    H = int(round(H / 64.0)) * 64
    W = int(round(W / 64.0)) * 64
    img = input_image.resize((W, H), resample=Image.LANCZOS)
    return img


SAMPLER_MAP = {
    "DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True, algorithm_type="sde-dpmsolver++"),
    "DPM++ Karras": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True),
    "Heun": lambda config: HeunDiscreteScheduler.from_config(config),
    "Euler": lambda config: EulerDiscreteScheduler.from_config(config),
    "DDIM": lambda config: DDIMScheduler.from_config(config),
    "DEIS": lambda config: DEISMultistepScheduler.from_config(config),
}

def scan_qr_code(image):
    # Convert gradio image to PIL Image if necessary
    if isinstance(image, np.ndarray):
        image = Image.fromarray(image)
    
    # Convert to grayscale
    gray_image = image.convert('L')
    
    # Convert to numpy array
    np_image = np.array(gray_image)
    
    # Method 1: Using qrcode library
    try:
        qr = qrcode.QRCode()
        qr.add_data('')
        qr.decode(gray_image)
        return qr.data.decode('utf-8')
    except Exception:
        pass
    
    # Method 2: Using OpenCV
    try:
        qr_detector = cv2.QRCodeDetector()
        retval, decoded_info, points, straight_qrcode = qr_detector.detectAndDecodeMulti(np_image)
        if retval:
            return decoded_info[0]
    except Exception:
        pass
    
    # Method 3: Fallback to zxing-cpp
    try:
        reader = BarCodeReader()
        results = reader.decode(np_image)
        if results:
            return results[0].parsed
    except Exception:
        pass
    
    return None

def invert_image(image):
    if image is None:
        return None
    if isinstance(image, np.ndarray):
        return 255 - image
    elif isinstance(image, Image.Image):
        return ImageOps.invert(image.convert('RGB'))
    else:
        raise ValueError("Unsupported image type")

def invert_displayed_image(image):
    if image is None:
        return None
    inverted = invert_image(image)
    if isinstance(inverted, np.ndarray):
        return Image.fromarray(inverted)
    return inverted


@spaces.GPU()
def inference(
    qr_code_content: str,
    prompt: str,
    negative_prompt: str,
    guidance_scale: float = 15.0,  # Increased from 10.0 to 15.0
    controlnet_conditioning_scale: float = 1.5,  # Adjusted from 2.0 to 1.5
    strength: float = 0.6,  # Reduced from 0.8 to 0.6
    seed: int = -1,
    init_image: Image.Image | None = None,
    qrcode_image: Image.Image | None = None,
    use_qr_code_as_init_image = True,
    sampler = "DPM++ Karras SDE",  
    bg_color: str = "white",
    qr_color: str = "black",
    invert_final_image: bool = False,
    invert_init_image: bool = False,
    controlnet_model: str = "QR Code Monster",
    diffusion_model: str = "GhostMix",
    reference_image_strength: float = 0.6,
):
    try:
        progress = gr.Progress()
        # Load models based on user selection
        progress(0, desc="Downloading models...")
        pipe = load_models(controlnet_model, diffusion_model)
        progress(0.5, desc="Models downloaded, preparing for inference...")
        
        if prompt is None or prompt == "":
            raise gr.Error("Prompt is required")

        if qrcode_image is None and qr_code_content == "":
            raise gr.Error("QR Code Image or QR Code Content is required")

        pipe.scheduler = SAMPLER_MAP[sampler](pipe.scheduler.config)

        if seed == -1:
            seed = torch.seed()  # Generate a truly random seed
        generator = torch.manual_seed(seed)

        if qr_code_content != "" or qrcode_image.size == (1, 1):
            print("Generating QR Code from content")
            qr = qrcode.QRCode(
                version=1,
                error_correction=qrcode.constants.ERROR_CORRECT_H,
                box_size=10,
                border=4,
            )
            qr.add_data(qr_code_content)
            qr.make(fit=True)

            qrcode_image = qr.make_image(fill_color=qr_color, back_color=bg_color)
            qrcode_image = resize_for_condition_image(qrcode_image, 1024)
        else:
            print("Using QR Code Image")
            qrcode_image = resize_for_condition_image(qrcode_image, 1024)

        # Determine which image to use as init_image and control_image
        if use_qr_code_as_init_image:
            init_image = qrcode_image
            control_image = qrcode_image
        else:
            control_image = qrcode_image
            if init_image is None:
                # If no init_image provided, set strength to 1.0 to generate a new image
                strength = 1.0

        # Adjust strength if using an init_image
        if init_image is not None and not use_qr_code_as_init_image:
            # Map the 0-5 range to 0-1 range for the strength parameter
            mapped_strength = min(reference_image_strength / 5.0, 1.0)
            strength = 1.0 - mapped_strength  # Invert the strength for img2img
        elif use_qr_code_as_init_image:
            strength = min(strength, 0.6)  # Cap strength at 0.6 when using QR code as init_image

        # Invert init_image if requested
        if invert_init_image and init_image is not None:
            init_image = invert_image(init_image)

        final_image = None
        out = pipe(
            prompt=prompt,  # Use the full prompt
            negative_prompt=negative_prompt,  # Use the full negative prompt
            image=init_image,
            control_image=control_image,
            width=1024,
            height=1024,
            guidance_scale=float(guidance_scale),
            controlnet_conditioning_scale=float(controlnet_conditioning_scale),
            generator=generator,
            strength=float(strength),
            num_inference_steps=50,
        )
        final_image = out.images[0] if final_image is None else final_image

        if invert_final_image:
            final_image = invert_image(final_image)
        
        return final_image, seed
    except gr.Error as e:
        print(f"Gradio error in inference: {str(e)}")
        return Image.new('RGB', (1024, 1024), color='white'), -1
    except Exception as e:
        print(f"Unexpected error in inference: {str(e)}")
        return Image.new('RGB', (1024, 1024), color='white'), -1

def invert_init_image_display(image):
    if image is None:
        return None
    inverted = invert_image(image)
    if isinstance(inverted, np.ndarray):
        return Image.fromarray(inverted)
    return inverted

def adjust_color_balance(image, r, g, b):
    # Convert image to RGB if it's not already
    image = image.convert('RGB')
    
    # Split the image into its RGB channels
    r_channel, g_channel, b_channel = image.split()
    
    # Adjust each channel
    r_channel = r_channel.point(lambda i: i + (i * r))
    g_channel = g_channel.point(lambda i: i + (i * g))
    b_channel = b_channel.point(lambda i: i + (i * b))
    
    # Merge the channels back
    return Image.merge('RGB', (r_channel, g_channel, b_channel))

def apply_qr_overlay(image, original_qr, overlay, opacity):
    if not overlay or original_qr is None:
        return image
    
    # Resize original QR to match the generated image
    original_qr = original_qr.resize(image.size)
    
    # Create a new image blending the generated image and the QR code
    return Image.blend(image, original_qr, opacity)

def apply_edge_enhancement(image, strength):
    if strength == 0:
        return image
    
    # Apply edge enhancement
    enhanced = image.filter(ImageFilter.EDGE_ENHANCE)
    
    # Blend the original and enhanced images based on strength
    return Image.blend(image, enhanced, strength / 5.0)


css = """
h1, h2, h3, h4, h5, h6, p, li, ul, ol, a, .centered-image {
    text-align: center;
    display: block;
    margin-left: auto;
    margin-right: auto;
}
ul, ol {
    margin-left: auto;
    margin-right: auto;
    display: table;
}
.centered-image {
    max-width: 100%;
    height: auto;
}
"""

def login(username, password):
    if username == USERNAME and password == PASSWORD:
        return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(value="Login successful! You can now access the QR Code Art Generator tab.", visible=True)
    else:
        return gr.update(visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(value="Invalid username or password. Please try again.", visible=True)

# Add login elements to the Gradio interface
with gr.Blocks(theme='Hev832/Applio', css=css, fill_width=True, fill_height=True) as blocks:
    generated_images = gr.State([])

    with gr.Tab("Welcome"):
        with gr.Row():
            with gr.Column(scale=2):  
                gr.Markdown(
                    """
                    <img src="https://cdn-uploads.huggingface.co/production/uploads/64740cf7485a7c8e1bd51ac9/8lHjpId7-JDalHq1JByPE.webp" alt="Yamamoto Logo" class="centered-image">
                    
                    # 🎨 Yamamoto QR Code Art Generator
                
                    ## Transform Your QR Codes into Brand Masterpieces
                    This cutting-edge tool empowers our creative team to craft visually stunning,<br> 
                    on-brand QR codes that perfectly blend functionality with artistic expression.
                    ## 🚀 How It Works:
                    1. **Enter Your QR Code Content**: Start by inputting the URL or text for your QR code.
                    2. **Craft Your Prompt**: Describe the artistic style or theme you envision for your QR code.
                    3. **Fine-tune with Advanced Settings**: Adjust parameters to perfect your creation (see tips below).
                    4. **Generate and Iterate**: Click 'Run' to create your art, then refine as needed.
                    """
                )
            
            with gr.Column(scale=1):  
                with gr.Row():  
                    gr.Markdown(
                        """
                        Login below using the Yamamoto internal<br>
                        username and password to access the full app.<br>
                        
                        Once logged in, a new tab will appear named<br>
                        "QR Code Art Generator" allowing you to access.
                        """
                    )

                with gr.Row():
                    username = gr.Textbox(label="Username", placeholder="Enter your username", value="ugd")
                with gr.Row():
                    password = gr.Textbox(label="Password", type="password", placeholder="Enter your password", value="ugd!")
                with gr.Row():
                    login_button = gr.Button("Login", size="sm")
                login_message = gr.Markdown(visible=False)


    with gr.Tab("QR Code Art Generator", visible=False) as app_container:
        with gr.Row():
            with gr.Column():
                qr_code_content = gr.Textbox(
                    label="QR Code Content",
                    placeholder="Enter URL or text for your QR code",
                    info="This is what your QR code will link to or display when scanned.",
                    value="https://theunderground.digital/",
                    lines=1,
                )

                prompt = gr.Textbox(
                    label="Artistic Prompt",
                    placeholder="Describe the style or theme for your QR code art (For best results, keep the prompt to 75 characters or less as seen below)",
                    value="A high-res, photo-realistic minimalist rendering of Mount Fuji as a sharp, semi-realistic silhouette on the horizon. The mountain conveys strength and motion with clean, crisp lines and natural flow. Features detailed snow textures, subtle ridge highlights, and a powerful yet serene atmosphere. Emphasizes strength with clarity and precision in texture and light.",
                    info="Describe the style or theme for your QR code art (For best results, keep the prompt to 75 characters or less as seen in the example)",
                    lines=8,
                )
                negative_prompt = gr.Textbox(
                    label="Elements to Avoid",
                    placeholder="Describe what you don't want in the image",
                    value="ugly, disfigured, low quality, blurry, nsfw, bad_pictures, poorly drawn, distorted, overexposed, flat shading, bad proportions, deformed, pixelated, messy details, lack of contrast, unrealistic textures, bad anatomy, rough edges, low resolution",
                    info="List elements or styles you want to avoid in your QR code art.",
                    lines=4,
                )

                run_btn = gr.Button("🎨 Create Your QR Art", variant="primary")  

                with gr.Accordion(label="Needs Some Prompting Help?", open=False, visible=True):
                    gr.Markdown(
                        """
                        ## 🌟 Tips for Spectacular Results:
                        - Use concise details in your prompt to help the AI understand your vision.
                        - Use negative prompts to avoid unwanted elements in your image.
                        - Experiment with different ControlNet models and diffusion models to find the best combination for your prompt.

                        ## 🎭 Prompt Ideas to Spark Your Creativity:
                        - "A serene Japanese garden with cherry blossoms and a koi pond"
                        - "A futuristic cityscape with neon lights and flying cars"
                        - "An abstract painting with swirling colors and geometric shapes"
                        - "A vintage-style travel poster featuring iconic landmarks"

                        Remember, the magic lies in the details of your prompt and the fine-tuning of your settings. 
                        Happy creating!
                        """
                    )

                with gr.Accordion("Set Custom QR Code Colors", open=False):
                    bg_color = gr.ColorPicker(
                        label="Background Color", 
                        value="#FFFFFF",
                        info="Choose the background color for the QR code"
                    )
                    qr_color = gr.ColorPicker(
                        label="QR Code Color", 
                        value="#000000",
                        info="Choose the color for the QR code pattern"
                    )
                    invert_final_image = gr.Checkbox(
                        label="Invert Final Image", 
                        value=False,
                        info="Check this to invert the colors of the final image",
                        visible=False,
                    )
                with gr.Accordion("AI Model Selection", open=False):
                    controlnet_model_dropdown = gr.Dropdown(
                        choices=list(CONTROLNET_MODELS.keys()),
                        value="QR Code Monster",
                        label="ControlNet Model",
                        info="Select the ControlNet model for QR code generation"
                    )
                    diffusion_model_dropdown = gr.Dropdown(
                        choices=list(DIFFUSION_MODELS.keys()),
                        value="GhostMix",
                        label="Diffusion Model",
                        info="Select the main diffusion model for image generation"
                    )
                
            
                with gr.Accordion(label="QR Code Image (Optional)", open=False, visible=False):
                    qr_code_image = gr.Image(
                        label="QR Code Image (Optional). Leave blank to automatically generate QR code",
                        type="pil",
                    )
            
            with gr.Column():
                gr.Markdown("### Your Generated QR Code Art")
                result_image = gr.Image(
                    label="Your Artistic QR Code", 
                    show_download_button=True, 
                    show_fullscreen_button=True, 
                    container=True
                )
                gr.Markdown("💾 Right-click and save to download your QR code art.")

                scan_button = gr.Button("Verify QR Code Works", visible=False)
                scan_result = gr.Textbox(label="Validation Result of QR Code", interactive=False, visible=False)
                used_seed = gr.Number(label="Seed Used", interactive=False)

                with gr.Accordion(label="Use Your Own Image as a Reference", open=True, visible=True) as init_image_acc:
                    init_image = gr.Image(label="Reference Image", type="pil")
                    with gr.Row():
                        use_qr_code_as_init_image = gr.Checkbox(
                            label="Uncheck to use your own image for generation", 
                            value=True, 
                            interactive=True,
                            info="Allows you to use your own image for generation, otherwise a generic QR Code is created automatically as the base image"
                        )
                        reference_image_strength = gr.Slider(
                            minimum=0.0,
                            maximum=5.0,
                            step=0.05,
                            value=0.6,
                            label="Reference Image Influence",
                            info="Controls how much the reference image influences the final result (0 = ignore, 5 = copy exactly)",
                            visible=True  # We'll make this visible when a reference image is uploaded
                        )
                        invert_init_image_button = gr.Button("Invert Init Image", size="sm", visible=False)

    with gr.Tab("Advanced Settings"):
        with gr.Accordion("Advanced Art Controls", open=True):
            with gr.Row():
                controlnet_conditioning_scale = gr.Slider(
                    minimum=0.0,
                    maximum=5.0,
                    step=0.01,
                    value=2,
                    label="QR Code Visibility in Image",
                )
                with gr.Accordion("How Much QR Code Visibility is in Final Image (Click For Explanation)", open=False):
                    gr.Markdown(
                        """
                        **QR Code Visibility** determines how much the QR code itself stands out in the final design. Think of it like balancing between how "artistic" the image looks and how "functional" the QR code is.
                
                        - **Low settings (0.0-1)**: If you choose a lower value, the QR code will blend more into the art, and it might be hard to scan with a phone. This setting is great if you want the image to look amazing, but you might lose some of the scannability. Try this if you care more about art and less about the QR code being easily recognized.
                        
                        - **Medium settings (1-3)**: This is the sweet spot where the QR code remains clearly visible while still blending in with the art. You can still scan it easily with a phone, but it looks more creative. For most users, setting it around **1.1** is a great start to balance both art and function.
                        
                        - **High settings (3-5.0)**: If you need to make sure that the QR code is super easy to scan, even if it means the image looks less like art and more like a regular QR code, then choose a higher value. This is ideal when functionality is the main goal, and the artistic side can take a backseat.
                        
                        Start with **1.3** if you're unsure, and adjust up or down depending on whether you want the QR code to be more artistic or more functional.
                        """
                    )
            
            with gr.Row():
                strength = gr.Slider(
                    minimum=0.0, 
                    maximum=5, 
                    step=0.10, 
                    value=2, 
                    label="Artistic Freedom for the AI When Generating",
                )
                with gr.Accordion("How Much Artistic Freedom the AI has When Generating Image (Click For Explanation)", open=False):
                    gr.Markdown(
                        """
                        **Artistic Freedom** controls how much the AI is allowed to change the QR code's look to match your description. It's like telling the AI how creative it can get with your QR code:
                
                        - **Low settings (0.10-2)**: If you set this low, the AI will make small changes and your QR code will look more like a regular, plain QR code. This is useful if you want something that is still creative but not too wild, keeping it simple and easy to scan.
                
                        - **Medium settings (2-3)**: Here, the AI will add more artistic touches but keep the QR code recognizable. You get the best of both worlds—your QR code will have some creative flair, but it will still be easy to scan. For most cases, setting it to **0.6** is a great way to keep the code functional and artistic.
                
                        - **High settings (3-5)**: If you set this high, the AI will go all-out creative. The QR code will look amazing, but it might be difficult to scan because the art can start to take over the code. This setting is perfect if you're aiming for a highly creative piece of art and don't mind if it's a bit harder to scan. Start at **0.9** to explore creative but functional designs.
                        """
                    )
            
            with gr.Row():
                guidance_scale = gr.Slider(
                    minimum=0.0,
                    maximum=100.0,
                    step=0.25,
                    value=7.5,
                    label="How Closely the AI Follows the Prompt",
                )
                with gr.Accordion("How Closely the AI Follows the Prompt (Click For Explanation)", open=False):
                    gr.Markdown(
                        """
                        **Follow the Prompt** tells the AI how closely it should follow your description when creating the QR code art. Think of it like giving the AI instructions on how strict or flexible it can be with your design ideas:
                
                        - **Low settings (0-5)**: If you choose a lower value, the AI has more freedom to get creative on its own and may not stick too closely to your description. This is great if you want to see how the AI interprets your ideas in unexpected ways.
                
                        - **Medium settings (5-15)**: This is a good balance where the AI will mostly follow your prompt but will also add some of its own creative touches. If you want to see some surprises but still want the design to look like what you described, start at around **7.5**.
                
                        - **High settings (15+)**: If you choose a higher value, the AI will stick very closely to what you wrote in the description. This is good if you have a very specific idea and don't want the AI to change much. Just keep in mind that this might limit the AI's creativity.
                        
                        Start at **7.5** for a balanced approach where the AI follows your ideas but still adds some artistic flair.
                        """
                    )
            
            with gr.Row():
                sampler = gr.Dropdown(
                    choices=list(SAMPLER_MAP.keys()), 
                    value="DPM++ Karras SDE", 
                    label="Art Style Used to Create the Image",
                )
                with gr.Accordion("Details on Art Style the AI Uses to Create the Image (Click For Explanation)", open=False):
                    gr.Markdown(
                        """
                        **Art Style** changes how the AI creates the image, using different methods (or "samplers"). Each method has a different effect on how detailed or artistic the final QR code looks:
                
                        - **DPM++ Karras SDE**: This is a great all-around option for creating high-quality, detailed images. It's a good place to start if you want a balance of sharpness and creativity.
                
                        - **Euler**: This method creates very sharp, detailed images, making the QR code look crisp and clear. Choose this if you want a precise, well-defined design.
                
                        - **DDIM**: This method is better if you want the QR code to have a more artistic, abstract style. It's great for when you want the QR code to look like a piece of modern art.
                        
                        Feel free to experiment with different samplers to see what works best for the look you're going for!
                        """
                    )
            
            with gr.Row():
                seed = gr.Slider(
                    minimum=-1,
                    maximum=9999999999,
                    step=1,
                    value=-1,
                    label="Creative Seed for the Image Generation",                 
                )
                with gr.Accordion("How Creative Seed Works for Generating New and Unique Images (Click For Explanation)", open=False):
                    gr.Markdown(
                        """
                        **Creative Seed** controls whether the AI creates a completely new design each time or sticks to a specific design. Think of it like a recipe: with the same seed number, you get the same "recipe" for your QR code every time.
                
                        - **-1**: This setting makes the AI create something completely new every time you run it. Use this if you want to explore different design ideas with each attempt.
                
                        - **Any other number**: If you set a specific number, the AI will always create the same image based on that number. This is useful if you find a design you like and want to recreate it exactly.
                
                        Try **-1** if you want to explore and generate different designs. If you find something you really love, write down the seed number and use it again to recreate the same design.
                        """
                    )

            with gr.Row():
                reference_image_strength = gr.Slider(
                    minimum=0.0,
                    maximum=5.0,
                    step=0.05,
                    value=0.6,
                    label="Reference Image Influence",
                    info="Controls how much the reference image influences the final result (0 = ignore, 5 = copy exactly)",
                    visible=False  # We'll make this visible when a reference image is uploaded
                )

    with gr.Tab("Image Editing"):
        with gr.Column():
            image_selector = gr.Dropdown(label="Select Image to Edit", choices=[], interactive=True, visible=False)
            image_to_edit = gr.Image(label="Your Artistic QR Code", show_download_button=True, show_fullscreen_button=True, container=True)

            with gr.Row():
                qr_overlay = gr.Checkbox(label="Overlay Original QR Code", value=False, visible=False)
                qr_opacity = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, value=0.5, label="QR Overlay Opacity", visible=False)
                edge_enhance = gr.Slider(minimum=0.0, maximum=5.0, step=0.1, value=0.0, label="Edge Enhancement", visible=False)

            with gr.Row():
                red_balance = gr.Slider(minimum=-1.0, maximum=1.0, step=0.1, value=0.0, label="Red Balance")
                green_balance = gr.Slider(minimum=-1.0, maximum=1.0, step=0.1, value=0.0, label="Green Balance")
                blue_balance = gr.Slider(minimum=-1.0, maximum=1.0, step=0.1, value=0.0, label="Blue Balance")


            with gr.Row():
                brightness = gr.Slider(minimum=0.1, maximum=2.0, step=0.1, value=1.0, label="Brightness")
                contrast = gr.Slider(minimum=0.1, maximum=2.0, step=0.1, value=1.0, label="Contrast")
                saturation = gr.Slider(minimum=0.1, maximum=2.0, step=0.1, value=1.0, label="Saturation")
            with gr.Row():
                invert_button = gr.Button("Invert Image", size="sm")

            with gr.Row():
                edited_image = gr.Image(label="Edited QR Code", show_download_button=True, show_fullscreen_button=True, visible=False)
                scan_button = gr.Button("Verify QR Code Works", size="sm", visible=False)
                scan_result = gr.Textbox(label="Validation Result of QR Code", interactive=False, visible=False)
                
            used_seed = gr.Number(label="Seed Used", interactive=False)
            
            gr.Markdown(
                """
                ### 🔍 Analyzing Your Creation
                - Is the QR code scannable? Check with your phone camera to see if it can scan it.
                - If not scannable, use the Brightness, Contrast, and Saturation sliders to optimize the QR code for scanning.
                - Does the art style match your prompt? If not, try adjusting the 'Prompt Adherence'.
                - Want more artistic flair? Increase the 'Artistic Freedom'.
                - Need a clearer QR code? Raise the 'QR Code Visibility'.
                """
            )

    def scan_and_display(image):
        if image is None:
            return "No image to scan"
        
        scanned_text = scan_qr_code(image)
        if scanned_text:
            return f"Scanned successfully: {scanned_text}"
        else:
            return "Failed to scan QR code. Try adjusting the settings for better visibility."

    def invert_displayed_image(image):
        if image is None:
            return None
        return invert_image(image)

    scan_button.click(
        scan_and_display,
        inputs=[result_image],
        outputs=[scan_result]
    )

    invert_button.click(
        invert_displayed_image,
        inputs=[result_image],
        outputs=[result_image]
    )

    invert_init_image_button.click(
        invert_init_image_display,
        inputs=[init_image],
        outputs=[init_image]
    )

    brightness.change(
        adjust_image,
        inputs=[result_image, brightness, contrast, saturation],
        outputs=[result_image]
    )
    contrast.change(
        adjust_image,
        inputs=[result_image, brightness, contrast, saturation],
        outputs=[result_image]
    )
    saturation.change(
        adjust_image,
        inputs=[result_image, brightness, contrast, saturation],
        outputs=[result_image]
    )

    # Add logic to show/hide the reference_image_strength slider
    def update_reference_image_strength_visibility(init_image, use_qr_code_as_init_image):
        return gr.update(visible=init_image is not None and not use_qr_code_as_init_image)

    init_image.change(
        update_reference_image_strength_visibility,
        inputs=[init_image, use_qr_code_as_init_image],
        outputs=[reference_image_strength]
    )

    use_qr_code_as_init_image.change(
        update_reference_image_strength_visibility,
        inputs=[init_image, use_qr_code_as_init_image],
        outputs=[reference_image_strength]
    )

    run_btn.click(
        fn=inference,
        inputs=[
            qr_code_content,
            prompt,
            negative_prompt,
            guidance_scale,
            controlnet_conditioning_scale,
            strength,
            seed,
            init_image,
            qr_code_image,
            use_qr_code_as_init_image,
            sampler,
            bg_color,
            qr_color,
            invert_final_image,
            controlnet_model_dropdown,
            diffusion_model_dropdown,
            reference_image_strength,
        ],
        outputs=[result_image, used_seed],
        concurrency_limit=20
    )

    # Define login button click behavior
    login_button.click(
        login,
        inputs=[username, password],
        outputs=[app_container, login_message, login_button, login_message]
    )

    # Define password textbox submit behavior
    password.submit(
        login,
        inputs=[username, password],
        outputs=[app_container, login_message, login_button, login_message]
    )

# Load models on launch
load_models_on_launch()

blocks.queue(max_size=20)
blocks.launch(share=False, show_api=True)