# Import spaces first to control GPU initialization
import spaces

# Now import other packages
import torch
import gradio as gr
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 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

# Rest of your code remains the same...


# Function to convert a PIL image to a binary mask
def pil_to_binary_mask(pil_image, threshold=0):
    np_image = np.array(pil_image.convert("L"))
    mask = (np_image > threshold).astype(np.uint8) * 255
    return Image.fromarray(mask)


# Base paths for pre-trained models and examples
base_path = 'yisol/IDM-VTON'
example_path = os.path.join(os.path.dirname(__file__), 'example')

# Load the UNet model for try-on
unet = UNet2DConditionModel.from_pretrained(base_path, subfolder="unet", torch_dtype=torch.float16)
unet.requires_grad_(False)

# Load tokenizers and other required models
tokenizer_one = AutoTokenizer.from_pretrained(base_path, subfolder="tokenizer", use_fast=False)
tokenizer_two = AutoTokenizer.from_pretrained(base_path, subfolder="tokenizer_2", 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)
UNet_Encoder = UNet2DConditionModel_ref.from_pretrained(base_path, subfolder="unet_encoder", torch_dtype=torch.float16)

# Load parsing and openpose models
parsing_model = Parsing(0)
openpose_model = OpenPose(0)

# Freeze the parameters of the models to avoid gradients
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)

# Image transformation function
tensor_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])

# Initialize the pipeline for try-on
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


# Main function for try-on with error handling
@spaces.GPU
def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed):
    try:
        device = "cuda"

        # Prepare the device and models for computation
        openpose_model.preprocessor.body_estimation.model.to(device)
        pipe.to(device)
        pipe.unet_encoder.to(device)

        # Prepare the images
        garm_img = garm_img.convert("RGB").resize((768, 1024))
        human_img_orig = dict["background"].convert("RGB")

        # Handle cropping if needed
        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))

        # Apply masking if selected
        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_gray = (1 - transforms.ToTensor()(mask)) * tensor_transform(human_img)
        mask_gray = to_pil_image((mask_gray + 1.0) / 2.0)

        # Apply pose estimation
        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')
        )
        pose_img = args.func(args, human_img_arg)
        pose_img = pose_img[:, :, ::-1]
        pose_img = Image.fromarray(pose_img).resize((768, 1024))

        # Generate the try-on image
        with torch.no_grad():
            with torch.cuda.amp.autocast():
                prompt = "model is wearing " + garment_des
                negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
                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
                )

                # Cloth prompt embedding
                prompt = "a photo of " + garment_des
                prompt_embeds_c, _, _, _ = pipe.encode_prompt(
                    prompt, num_images_per_prompt=1, do_classifier_free_guidance=False, negative_prompt=negative_prompt
                )

                # Convert pose image and garment to tensors
                pose_img = tensor_transform(pose_img).unsqueeze(0).to(device, torch.float16)
                garm_tensor = tensor_transform(garm_img).unsqueeze(0).to(device, torch.float16)
                generator = torch.Generator(device).manual_seed(seed) if seed is not None else None

                # Run the pipeline
                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
        else:
            return images[0], mask_gray

    except Exception as e:
        print(f"Error during try-on: {e}")
        return None, None


# Gradio interface setup
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 = [{"background": ex_human, "layers": None, "composite": None} for ex_human in human_list_path]

# Gradio blocks UI
with gr.Blocks() as image_blocks:
    with gr.Column():
        with gr.Row():
            # imgs = gr.Image(source='upload', type="pil", label='Person Image')
            imgs = gr.Image(type="pil", label='Person Image')  # Remove the 'source' argument
            is_checked = gr.Checkbox(label="Check if mask needed")
            is_checked_crop = gr.Checkbox(label="Check to crop")
            ex_img = gr.Examples(inputs=imgs, examples_per_page=9, examples=human_ex_list)
        with gr.Column():
            garm_img = gr.Image(source='upload', type="pil", label='Cloth')
            garment_des = gr.Textbox(label="Garment Description", value='garment,shirt')
            ex_garm = gr.Examples(inputs=garm_img, examples_per_page=9, examples=garm_list_path)
        with gr.Row():
            denoise_steps = gr.Slider(label="denoise steps", minimum=1, maximum=50, step=1, value=25)
            seed = gr.Slider(label="Seed (for reproducible results)", minimum=0, maximum=2147483647, step=1)
        with gr.Row():
            try_button = gr.Button("Try it on")
        with gr.Row():
            out_img = gr.Image(label="Generated tryon output")
            masked_img = gr.Image(label="Mask")

    try_button.click(
        start_tryon,
        inputs=[imgs, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed],
        outputs=[out_img, masked_img]
    )

# Launch Gradio app
image_blocks.launch(server_name="0.0.0.0", server_port=7860)