import gradio as gr
import spaces
from PIL import Image
from src.tryon_pipeline import StableDiffusionXLInpaintPipeline as TryonPipeline
from src.unet_hacked_garmnet import UNet2DConditionModel as UNet2DConditionModel_ref
from src.unet_hacked_tryon import UNet2DConditionModel
from transformers import (
    CLIPImageProcessor,
    CLIPVisionModelWithProjection,
    CLIPTextModel,
    CLIPTextModelWithProjection,
)
from diffusers import DDPMScheduler,AutoencoderKL
from typing import List

import torch
import os
from transformers import AutoTokenizer
import numpy as np
from utils_mask import get_mask_location
from torchvision import transforms
import apply_net
from preprocess.humanparsing.run_parsing import Parsing
from preprocess.openpose.run_openpose import OpenPose
from detectron2.data.detection_utils import convert_PIL_to_numpy,_apply_exif_orientation
from torchvision.transforms.functional import to_pil_image

custom_css = """
.container { max-width: 1200px; margin: 0 auto; padding: 20px; }
.header { display: flex; align-items: center; justify-content: space-between; padding: 20px 0; }
.header {
    margin-bottom: 20px; /* Adds space below the header */
}

.main-content {
    margin-top: 20px; /* Adds space above the main content row */
}

.upload-box { 
    border: 2px dashed #E5E7EB !important;
    border-radius: 8px !important;
    min-height: 300px !important;
    display: flex !important;
    flex-direction: column !important;
    align-items: center !important;
    justify-content: center !important;
    background: white !important;
    margin: 10px 0 !important;
}
.preview-container {
    background: #F9FAFB;
    border-radius: 8px;
    padding: 15px;
    text-align: center;
}
.browser-header {
    background: #F3F4F6;
    padding: 8px;
    border-radius: 8px 8px 0 0;
    margin-bottom: 10px;
}
.browser-dots {
    display: flex;
    gap: 6px;
}

#contact-button {
    background: #7E22CE !important; /* Purple background */
    color: white !important;       /* White text */
    font-size: 12px;               /* Adjust font size */
    padding: 10px 16px;            /* Larger padding for height */
    border-radius: 15px;           /* Rounded corners for consistent styling */
    cursor: pointer;               /* Pointer cursor on hover */
    text-transform: uppercase;     /* Uppercase text */
    border: none;                  /* No border */
    box-shadow: 0px 2px 4px rgba(0, 0, 0, 0.1); /* Subtle shadow */
    width: 60px;                  /* Smaller width */
    text-align: center;            /* Centers the text */
    display: inline-block;         /* Prevents button from stretching */
    margin: 0 auto;                /* Centers the button horizontally */
}

#contact-button:hover {
    background: #5A189A !important; /* Slightly darker purple on hover */
    box-shadow: 0px 4px 6px rgba(0, 0, 0, 0.15); /* Enhanced shadow on hover */
}

.url-bar {
    background: white;
    border: 1px solid #E5E7EB;
    border-radius: 4px;
    padding: 6px 12px;
    margin: 8px 0;
    display: flex;
    align-items: center;
}
.generate-btn {
    background: #7E22CE !important;
    color: white !important;
    padding: 12px 24px !important;
    border-radius: 8px !important;
    width: 100% !important;
    margin-top: 20px !important;
}

.upload-box { 
    border: 2px dashed #E5E7EB !important; /* Dashed border */
    border-radius: 8px !important;         /* Rounded corners */
    min-height: 300px !important;          /* Fixed height */
    display: flex !important;              /* Flexbox for alignment */
    flex-direction: column !important;     /* Vertical layout */
    align-items: center !important;        /* Center content horizontally */
    justify-content: center !important;    /* Center content vertically */
    background: #F9FAFB !important;        /* Light gray background */
    margin: 10px 0 !important;             /* Spacing */
    text-align: center;                    /* Center text */
}

#cloud-icon {
    font-size: 48px;                       /* Larger icon size */
    margin-bottom: 10px;                   /* Spacing below icon */
}

.supported-formats {
    color: #6B7280;                        /* Gray text color */
    font-size: 12px;                       /* Smaller font size */
    text-align: center;                    /* Center text */
    margin-top: 10px;                      /* Spacing above */
}

.sample-btn {
    background: #E3D8FF !important;        /* Light purple background */
    color: #7E22CE !important;             /* Purple text color */
    padding: 6px 12px !important;          /* Button padding */
    border-radius: 4px !important;         /* Rounded button */
    font-size: 14px;                       /* Button font size */
    text-transform: uppercase;             /* Uppercase text */
    margin-top: 10px !important;           /* Spacing above button */
    cursor: pointer;                       /* Pointer cursor */
}

"""



def pil_to_binary_mask(pil_image, threshold=0):
    np_image = np.array(pil_image)
    grayscale_image = Image.fromarray(np_image).convert("L")
    binary_mask = np.array(grayscale_image) > threshold
    mask = np.zeros(binary_mask.shape, dtype=np.uint8)
    for i in range(binary_mask.shape[0]):
        for j in range(binary_mask.shape[1]):
            if binary_mask[i,j] == True :
                mask[i,j] = 1
    mask = (mask*255).astype(np.uint8)
    output_mask = Image.fromarray(mask)
    return output_mask


base_path = 'Roopansh/Ailusion-VTON-DEMO-v1.1'
example_path = os.path.join(os.path.dirname(__file__), 'example')

unet = UNet2DConditionModel.from_pretrained(
    base_path,
    subfolder="unet",
    torch_dtype=torch.float16,
)
unet.requires_grad_(False)
tokenizer_one = AutoTokenizer.from_pretrained(
    base_path,
    subfolder="tokenizer",
    revision=None,
    use_fast=False,
)
tokenizer_two = AutoTokenizer.from_pretrained(
    base_path,
    subfolder="tokenizer_2",
    revision=None,
    use_fast=False,
)
noise_scheduler = DDPMScheduler.from_pretrained(base_path, subfolder="scheduler")

text_encoder_one = CLIPTextModel.from_pretrained(
    base_path,
    subfolder="text_encoder",
    torch_dtype=torch.float16,
)
text_encoder_two = CLIPTextModelWithProjection.from_pretrained(
    base_path,
    subfolder="text_encoder_2",
    torch_dtype=torch.float16,
)
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
    base_path,
    subfolder="image_encoder",
    torch_dtype=torch.float16,
    )
vae = AutoencoderKL.from_pretrained(base_path,
                                    subfolder="vae",
                                    torch_dtype=torch.float16,
)

# "stabilityai/stable-diffusion-xl-base-1.0",
UNet_Encoder = UNet2DConditionModel_ref.from_pretrained(
    base_path,
    subfolder="unet_encoder",
    torch_dtype=torch.float16,
)

parsing_model = Parsing(0)
openpose_model = OpenPose(0)

UNet_Encoder.requires_grad_(False)
image_encoder.requires_grad_(False)
vae.requires_grad_(False)
unet.requires_grad_(False)
text_encoder_one.requires_grad_(False)
text_encoder_two.requires_grad_(False)
tensor_transfrom = transforms.Compose(
            [
                transforms.ToTensor(),
                transforms.Normalize([0.5], [0.5]),
            ]
    )

pipe = TryonPipeline.from_pretrained(
        base_path,
        unet=unet,
        vae=vae,
        feature_extractor= CLIPImageProcessor(),
        text_encoder = text_encoder_one,
        text_encoder_2 = text_encoder_two,
        tokenizer = tokenizer_one,
        tokenizer_2 = tokenizer_two,
        scheduler = noise_scheduler,
        image_encoder=image_encoder,
        torch_dtype=torch.float16,
)
pipe.unet_encoder = UNet_Encoder

@spaces.GPU(duration=120)
def start_tryon(dict,garm_img,garment_des,is_checked,is_checked_crop,denoise_steps,seed):
    device = "cuda"
    
    openpose_model.preprocessor.body_estimation.model.to(device)
    pipe.to(device)
    pipe.unet_encoder.to(device)

    garm_img= garm_img.convert("RGB").resize((768,1024))
    human_img_orig = dict["background"].convert("RGB")    
    
    if is_checked_crop:
        width, height = human_img_orig.size
        target_width = int(min(width, height * (3 / 4)))
        target_height = int(min(height, width * (4 / 3)))
        left = (width - target_width) / 2
        top = (height - target_height) / 2
        right = (width + target_width) / 2
        bottom = (height + target_height) / 2
        cropped_img = human_img_orig.crop((left, top, right, bottom))
        crop_size = cropped_img.size
        human_img = cropped_img.resize((768,1024))
    else:
        human_img = human_img_orig.resize((768,1024))


    if is_checked:
        keypoints = openpose_model(human_img.resize((384,512)))
        model_parse, _ = parsing_model(human_img.resize((384,512)))
        mask, mask_gray = get_mask_location('hd', "upper_body", model_parse, keypoints)
        mask = mask.resize((768,1024))
    else:
        mask = pil_to_binary_mask(dict['layers'][0].convert("RGB").resize((768, 1024)))
        # mask = transforms.ToTensor()(mask)
        # mask = mask.unsqueeze(0)
    mask_gray = (1-transforms.ToTensor()(mask)) * tensor_transfrom(human_img)
    mask_gray = to_pil_image((mask_gray+1.0)/2.0)


    human_img_arg = _apply_exif_orientation(human_img.resize((384,512)))
    human_img_arg = convert_PIL_to_numpy(human_img_arg, format="BGR")
     
    

    args = apply_net.create_argument_parser().parse_args(('show', './configs/densepose_rcnn_R_50_FPN_s1x.yaml', './ckpt/densepose/model_final_162be9.pkl', 'dp_segm', '-v', '--opts', 'MODEL.DEVICE', 'cuda'))
    # verbosity = getattr(args, "verbosity", None)
    pose_img = args.func(args,human_img_arg)    
    pose_img = pose_img[:,:,::-1]    
    pose_img = Image.fromarray(pose_img).resize((768,1024))
    
    with torch.no_grad():
        # Extract the images
        with torch.cuda.amp.autocast():
            with torch.no_grad():
                prompt = "model is wearing " + garment_des
                negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
                with torch.inference_mode():
                    (
                        prompt_embeds,
                        negative_prompt_embeds,
                        pooled_prompt_embeds,
                        negative_pooled_prompt_embeds,
                    ) = pipe.encode_prompt(
                        prompt,
                        num_images_per_prompt=1,
                        do_classifier_free_guidance=True,
                        negative_prompt=negative_prompt,
                    )
                                    
                    prompt = "a photo of " + garment_des
                    negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
                    if not isinstance(prompt, List):
                        prompt = [prompt] * 1
                    if not isinstance(negative_prompt, List):
                        negative_prompt = [negative_prompt] * 1
                    with torch.inference_mode():
                        (
                            prompt_embeds_c,
                            _,
                            _,
                            _,
                        ) = pipe.encode_prompt(
                            prompt,
                            num_images_per_prompt=1,
                            do_classifier_free_guidance=False,
                            negative_prompt=negative_prompt,
                        )



                    pose_img =  tensor_transfrom(pose_img).unsqueeze(0).to(device,torch.float16)
                    garm_tensor =  tensor_transfrom(garm_img).unsqueeze(0).to(device,torch.float16)
                    generator = torch.Generator(device).manual_seed(seed) if seed is not None else None
                    images = pipe(
                        prompt_embeds=prompt_embeds.to(device,torch.float16),
                        negative_prompt_embeds=negative_prompt_embeds.to(device,torch.float16),
                        pooled_prompt_embeds=pooled_prompt_embeds.to(device,torch.float16),
                        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds.to(device,torch.float16),
                        num_inference_steps=denoise_steps,
                        generator=generator,
                        strength = 1.0,
                        pose_img = pose_img.to(device,torch.float16),
                        text_embeds_cloth=prompt_embeds_c.to(device,torch.float16),
                        cloth = garm_tensor.to(device,torch.float16),
                        mask_image=mask,
                        image=human_img, 
                        height=1024,
                        width=768,
                        ip_adapter_image = garm_img.resize((768,1024)),
                        guidance_scale=2.0,
                    )[0]

    if is_checked_crop:
        out_img = images[0].resize(crop_size)        
        human_img_orig.paste(out_img, (int(left), int(top)))    
        # return human_img_orig, mask_gray
        return human_img_orig
    else:
        # return images[0], mask_gray
        return images[0]
    # return images[0], mask_gray

garm_list = os.listdir(os.path.join(example_path,"cloth"))
garm_list_path = [os.path.join(example_path,"cloth",garm) for garm in garm_list]

human_list = os.listdir(os.path.join(example_path,"human"))
human_list_path = [os.path.join(example_path,"human",human) for human in human_list]

human_ex_list = []
for ex_human in human_list_path:
    ex_dict= {}
    ex_dict['background'] = ex_human
    ex_dict['layers'] = None
    ex_dict['composite'] = None
    human_ex_list.append(ex_dict)

##default human


with gr.Blocks(css=custom_css) as demo:
    with gr.Column():
        # Header
        with gr.Row(elem_classes="header"):
            gr.Markdown("<h1 style='text-align: left;'>AILUSION</h1>", elem_id="component-0")
            gr.Markdown("<h1 style='text-align: center; font-weight: bold;'>Feature Trail</h1>")
            gr.Button("Contact us", variant="primary", elem_id="contact-button")

        # Main content
        with gr.Row(elem_classes="main-content"):
            # Upload Human
            with gr.Column():
                gr.Markdown("<h2 style='text-align: center; font-weight: bold;'>Upload Human</h2>", elem_id="upload-human-title")
                with gr.Column(elem_classes="upload-box"):
                    imgs = gr.Image(type="pil", interactive=True)
                gr.Markdown(
                    "<p class='supported-formats'>Supported formats: JPEG, PNG</p>",
                    elem_classes="supported-formats"
                )
                example = gr.Examples(
                inputs=human_input,
                examples_per_page=5,
                examples=human_list_path)

            # Upload Garment
            with gr.Column():
                gr.Markdown("<h2 style='text-align: center; font-weight: bold;'>Upload Garment</h2>", elem_classes="upload-title")
                with gr.Column(elem_classes="upload-box"):
                    garm_img = gr.Image(type="pil", interactive=True)
                gr.Markdown(
                    "<p class='supported-formats'>Supported formats: JPEG, PNG</p>",
                    elem_classes="supported-formats"
                )
                example = gr.Examples(
                    inputs=garment_input,
                    examples_per_page=5,
                    examples=garm_ex_list
                )

            # Preview Section
            with gr.Column(elem_classes="preview-container"):
                preview_output = gr.Image(type="pil")
                gr.Markdown("\u20b9998")
                with gr.Row():
                    gr.Button("Add to cart", variant="secondary")
                    gr.Button("\u2661", variant="secondary")

        # Generate Output button
        with gr.Row():
            generate_btn = gr.Button("Generate Output", elem_classes="generate-btn", scale=2)

        # Set the function to process images
        generate_btn.click(
            fn=start_tryon,
            inputs=[human_input, garment_input],
            outputs=preview_output,
            api_name='tryon'
        )

demo.launch(share=True)