mport gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import DiffusionPipeline
from PIL import Image
import io
from PIL import ImageEnhance

def get_edge_color(image):
    """
    Get a random color from the edge of the image
    """
    # Convert to numpy array
    img_array = np.array(image)
    
    # Get pixels from all edges
    top_edge = img_array[0, :, :]
    bottom_edge = img_array[-1, :, :]
    left_edge = img_array[:, 0, :]
    right_edge = img_array[:, -1, :]
    
    # Combine all edge pixels
    edge_pixels = np.concatenate([top_edge, bottom_edge, left_edge, right_edge])
    
    # Pick a random edge pixel
    random_edge_color = tuple(edge_pixels[random.randint(0, len(edge_pixels)-1)])
    
    return random_edge_color

def color_match_tshirt(tshirt_image, target_color, threshold=30):
    """
    Change white/near-white areas of the t-shirt to the target color
    """
    # Convert to numpy array
    img_array = np.array(tshirt_image)
    
    # Create a mask for near-white pixels
    white_mask = np.all(np.abs(img_array - [255, 255, 255]) < threshold, axis=2)
    
    # Apply the new color to masked areas
    img_array[white_mask] = target_color
    
    return Image.fromarray(img_array)

def add_logo_watermark(image, logo_path='logo.png', size_percentage=0.2):
    """
    Add a logo watermark to the bottom right corner
    """
    # Open and resize logo
    logo = Image.open(logo_path)
    
    # Calculate new logo size (20% of main image width)
    new_width = int(image.size[0] * size_percentage)
    new_height = int(new_width * logo.size[1] / logo.size[0])
    logo = logo.resize((new_width, new_height), Image.Resampling.LANCZOS)
    
    # If logo has transparency, use it as mask
    if logo.mode == 'RGBA':
        mask = logo.split()[3]
    else:
        mask = None
    
    # Calculate position (bottom right corner with padding)
    position = (image.size[0] - logo.size[0] - 10, 
               image.size[1] - logo.size[1] - 10)
    
    # Create a copy of the image
    result = image.copy()
    result.paste(logo, position, mask)
    
    return result

def create_tshirt_preview(design_image, tshirt_color="white"):
    """
    Overlay the design onto the t-shirt template with color matching
    """
    # Load the template t-shirt image
    tshirt = Image.open('image.jpeg')
    tshirt_width, tshirt_height = tshirt.size
    
    # Get a random edge color from the design
    edge_color = get_edge_color(design_image)
    
    # Color match the t-shirt
    tshirt = color_match_tshirt(tshirt, edge_color)
    
    # Resize design (35% of shirt width)
    design_width = int(tshirt_width * 0.35)
    design_height = int(design_width * design_image.size[1] / design_image.size[0])
    design_image = design_image.resize((design_width, design_height), Image.Resampling.LANCZOS)
    
    # Calculate position to center design
    x = (tshirt_width - design_width) // 2
    y = int(tshirt_height * 0.2)
    
    # Create mask if design has transparency
    if design_image.mode == 'RGBA':
        mask = design_image.split()[3]
    else:
        mask = None
    
    # Paste design onto shirt
    tshirt.paste(design_image, (x, y), mask)
    
    # Add logo watermark
    tshirt = add_logo_watermark(tshirt)
    
    return tshirt

@spaces.GPU()
def infer(prompt, style=None, tshirt_color="white", seed=42, randomize_seed=False, 
          width=1024, height=1024, num_inference_steps=4, 
          progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    
    enhanced_prompt = enhance_prompt_for_tshirt(prompt, style)
    generator = torch.Generator().manual_seed(seed)
    
    # Generate the design
    design_image = pipe(
        prompt=enhanced_prompt,
        width=width,
        height=height,
        num_inference_steps=num_inference_steps,
        generator=generator,
        guidance_scale=0.0
    ).images[0]
    
    # Add logo to design
    design_image = add_logo_watermark(design_image)
    
    # Create t-shirt preview
    tshirt_preview = create_tshirt_preview(design_image, tshirt_color)
    
    return design_image, tshirt_preview, seed

# Available t-shirt colors
TSHIRT_COLORS = {
    "White": "#FFFFFF",
    "Black": "#000000",
    "Navy": "#000080",
    "Gray": "#808080"
}

examples = [
    ["Cool geometric mountain landscape", "minimal", "White"],
    ["Vintage motorcycle with flames", "vintage", "Black"],
    ["flamingo in scenic forset", "realistic", "White"],
    ["Adventure Starts typography", "typography", "White"]
]

styles = [
    "minimal",
    "vintage",
    "artistic",
    "geometric",
    "typography",
    "realistic"
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 1200px !important;
    padding: 20px;
}
.main-title {
    text-align: center;
    color: #2d3748;
    margin-bottom: 1rem;
    font-family: 'Poppins', sans-serif;
}
.subtitle {
    text-align: center;
    color: #4a5568;
    margin-bottom: 2rem;
    font-family: 'Inter', sans-serif;
    font-size: 0.95rem;
    line-height: 1.5;
}
.design-input {
    border: 2px solid #e2e8f0;
    border-radius: 10px;
    padding: 12px !important;
    margin-bottom: 1rem !important;
    font-size: 1rem;
    transition: all 0.3s ease;
}
.results-row {
    display: grid;
    grid-template-columns: 1fr 1fr;
    gap: 20px;
    margin-top: 20px;
}
"""

with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(
            """
            # 👕Deradh's T-Shirt Design Generator
            """,
            elem_classes=["main-title"]
        )
        
        gr.Markdown(
            """
            Create unique t-shirt designs using Deradh's AI. 
            Describe your design idea and select a style to generate professional-quality artwork 
            perfect for custom t-shirts.
            """,
            elem_classes=["subtitle"]
        )
        
        with gr.Row():
            with gr.Column(scale=2):
                prompt = gr.Text(
                    label="Design Description",
                    show_label=False,
                    max_lines=1,
                    placeholder="Describe your t-shirt design idea",
                    container=False,
                    elem_classes=["design-input"]
                )
            with gr.Column(scale=1):
                style = gr.Dropdown(
                    choices=[""] + styles,
                    value="",
                    label="Style",
                    container=False
                )
            with gr.Column(scale=1):
                tshirt_color = gr.Dropdown(
                    choices=list(TSHIRT_COLORS.keys()),
                    value="White",
                    label="T-Shirt Color",
                    container=False
                )
            run_button = gr.Button(
                "✨ Generate",
                scale=0,
                elem_classes=["generate-button"]
            )
        
        with gr.Row(elem_classes=["results-row"]):
            result = gr.Image(
                label="Generated Design",
                show_label=True,
                elem_classes=["result-image"]
            )
            preview = gr.Image(
                label="T-Shirt Preview",
                show_label=True,
                elem_classes=["preview-image"]
            )
        
        with gr.Accordion("🔧 Advanced Settings", open=False):
            with gr.Group():
                seed = gr.Slider(
                    label="Design Seed",
                    minimum=0,
                    maximum=MAX_SEED,
                    step=1,
                    value=0,
                )
                randomize_seed = gr.Checkbox(
                    label="Randomize Design",
                    value=True
                )
                
                with gr.Row():
                    width = gr.Slider(
                        label="Width",
                        minimum=256,
                        maximum=MAX_IMAGE_SIZE,
                        step=32,
                        value=1024,
                    )
                    height = gr.Slider(
                        label="Height",
                        minimum=256,
                        maximum=MAX_IMAGE_SIZE,
                        step=32,
                        value=1024,
                    )
                
                num_inference_steps = gr.Slider(
                    label="Generation Quality (Steps)",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=4,
                )
        
        gr.Examples(
            examples=examples,
            fn=infer,
            inputs=[prompt, style, tshirt_color],
            outputs=[result, preview, seed],
            cache_examples=True
        )
        
        gr.on(
            triggers=[run_button.click, prompt.submit],
            fn=infer,
            inputs=[prompt, style, tshirt_color, seed, randomize_seed, width, height, num_inference_steps],
            outputs=[result, preview, seed]
        )

demo.launch()