import gradio as gr
import numpy as np
import torch
import cv2
from PIL import Image
from torchvision import transforms
from cloth_segmentation.networks.u2net import U2NET  

# Load U²-Net model
model_path = "cloth_segmentation/networks/u2net.pth"
model = U2NET(3, 1)
state_dict = torch.load(model_path, map_location=torch.device('cpu'))
state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
model.load_state_dict(state_dict)
model.eval()

def segment_dress(image_np):
    """Segment dress using U²-Net"""
    transform_pipeline = transforms.Compose([
        transforms.ToTensor(),
        transforms.Resize((320, 320))
    ])
    
    image = Image.fromarray(image_np).convert("RGB")
    input_tensor = transform_pipeline(image).unsqueeze(0)

    with torch.no_grad():
        output = model(input_tensor)[0][0].squeeze().cpu().numpy()

    dress_mask = (output > 0.5).astype(np.uint8) * 255
    dress_mask = cv2.resize(dress_mask, (image_np.shape[1], image_np.shape[0]), interpolation=cv2.INTER_NEAREST)

    return dress_mask

def recolor_dress(image_np, dress_mask, target_color):
    """Change dress color naturally while keeping textures intact"""
    
    # Convert target color to LAB
    target_color_lab = cv2.cvtColor(np.uint8([[target_color]]), cv2.COLOR_BGR2LAB)[0][0]

    # Convert image to LAB for better color control
    img_lab = cv2.cvtColor(image_np, cv2.COLOR_RGB2LAB)
    
    # Compute mean LAB values of dress pixels
    dress_pixels = img_lab[dress_mask > 0]
    mean_L, mean_A, mean_B = dress_pixels[:, 0].mean(), dress_pixels[:, 1].mean(), dress_pixels[:, 2].mean()

    # Compute new color adjustment
    img_lab[..., 1] = np.where(dress_mask > 128, img_lab[..., 1] - mean_A + target_color_lab[1], img_lab[..., 1])
    img_lab[..., 2] = np.where(dress_mask > 128, img_lab[..., 2] - mean_B + target_color_lab[2], img_lab[..., 2])

    # Convert back to RGB
    img_recolored = cv2.cvtColor(img_lab, cv2.COLOR_LAB2RGB)

    # Smooth edges for natural blending
    img_recolored = cv2.seamlessClone(img_recolored, image_np, dress_mask, (image_np.shape[1]//2, image_np.shape[0]//2), cv2.NORMAL_CLONE)

    return img_recolored

def change_dress_color(image_path, color):
    """Main function to change dress color naturally"""
    if image_path is None:
        return None

    img = Image.open(image_path).convert("RGB")
    img_np = np.array(img)

    # Get dress segmentation mask
    dress_mask = segment_dress(img_np)
    
    if dress_mask is None:
        return img  # No dress detected

    # Convert the selected color to BGR
    color_map = {
        "Red": (0, 0, 255), "Blue": (255, 0, 0), "Green": (0, 255, 0), "Yellow": (0, 255, 255),
        "Purple": (128, 0, 128), "Orange": (0, 165, 255), "Cyan": (255, 255, 0), "Magenta": (255, 0, 255),
        "White": (255, 255, 255), "Black": (0, 0, 0)
    }
    new_color_bgr = np.array(color_map.get(color, (0, 0, 255)), dtype=np.uint8)

    # Apply recoloring with blending
    img_recolored = recolor_dress(img_np, dress_mask, new_color_bgr)

    return Image.fromarray(img_recolored)

# Gradio Interface
demo = gr.Interface(
    fn=change_dress_color,
    inputs=[
        gr.Image(type="filepath", label="Upload Dress Image"),
        gr.Radio(["Red", "Blue", "Green", "Yellow", "Purple", "Orange", "Cyan", "Magenta", "White", "Black"], label="Choose New Dress Color")
    ],
    outputs=gr.Image(type="pil", label="Color Changed Dress"),
    title="Realistic Dress Color Changer",
    description="Upload an image of a dress and select a new color. The AI will change the dress color naturally while keeping the fabric texture."
)

if __name__ == "__main__":
    demo.launch()