# Not ready to use yet
import spaces
import argparse
import numpy as np
import gradio as gr
from omegaconf import OmegaConf
import torch
from PIL import Image
import PIL
from pipelines import TwoStagePipeline
from huggingface_hub import hf_hub_download
import os
import rembg
from typing import Any
import json
import os
import json
import argparse

from model import CRM
from inference import generate3d

pipeline = None
rembg_session = rembg.new_session()


def expand_to_square(image, bg_color=(0, 0, 0, 0)):
    # expand image to 1:1
    width, height = image.size
    if width == height:
        return image
    new_size = (max(width, height), max(width, height))
    new_image = Image.new("RGBA", new_size, bg_color)
    paste_position = ((new_size[0] - width) // 2, (new_size[1] - height) // 2)
    new_image.paste(image, paste_position)
    return new_image

def check_input_image(input_image):
    if input_image is None:
        raise gr.Error("No image uploaded!")


def remove_background(
    image: PIL.Image.Image,
    rembg_session: Any = None,
    force: bool = False,
    **rembg_kwargs,
) -> PIL.Image.Image:
    do_remove = True
    if image.mode == "RGBA" and image.getextrema()[3][0] < 255:
        # explain why current do not rm bg
        print("alhpa channl not enpty, skip remove background, using alpha channel as mask")
        background = Image.new("RGBA", image.size, (0, 0, 0, 0))
        image = Image.alpha_composite(background, image)
        do_remove = False
    do_remove = do_remove or force
    if do_remove:
        image = rembg.remove(image, session=rembg_session, **rembg_kwargs)
    return image

def do_resize_content(original_image: Image, scale_rate):
    # resize image content wile retain the original image size
    if scale_rate != 1:
        # Calculate the new size after rescaling
        new_size = tuple(int(dim * scale_rate) for dim in original_image.size)
        # Resize the image while maintaining the aspect ratio
        resized_image = original_image.resize(new_size)
        # Create a new image with the original size and black background
        padded_image = Image.new("RGBA", original_image.size, (0, 0, 0, 0))
        paste_position = ((original_image.width - resized_image.width) // 2, (original_image.height - resized_image.height) // 2)
        padded_image.paste(resized_image, paste_position)
        return padded_image
    else:
        return original_image

def add_background(image, bg_color=(255, 255, 255)):
    # given an RGBA image, alpha channel is used as mask to add background color
    background = Image.new("RGBA", image.size, bg_color)
    return Image.alpha_composite(background, image)


def preprocess_image(image, background_choice, foreground_ratio, backgroud_color):
    """
    input image is a pil image in RGBA, return RGB image
    """
    print(background_choice)
    if background_choice == "Alpha as mask":
        background = Image.new("RGBA", image.size, (0, 0, 0, 0))
        image = Image.alpha_composite(background, image)
    else:
        image = remove_background(image, rembg_session, force=True)
    image = do_resize_content(image, foreground_ratio)
    image = expand_to_square(image)
    image = add_background(image, backgroud_color)
    return image.convert("RGB")

@spaces.GPU
def gen_image(input_image, seed, scale, step):
    global pipeline, model, args
    pipeline.set_seed(seed)
    rt_dict = pipeline(input_image, scale=scale, step=step)
    stage1_images = rt_dict["stage1_images"]
    stage2_images = rt_dict["stage2_images"]
    np_imgs = np.concatenate(stage1_images, 1)
    np_xyzs = np.concatenate(stage2_images, 1)

    glb_path = generate3d(model, np_imgs, np_xyzs, args.device)
    return Image.fromarray(np_imgs), Image.fromarray(np_xyzs), glb_path#, obj_path


def process_and_generate(input_image, background_choice, foreground_ratio, backgroud_color, seed, scale, step):
    """Process the input image and generate the 3D model in a single function"""
    if input_image is None:
        raise gr.Error("No image uploaded!")
    
    # Preprocess the image
    processed = preprocess_image(input_image, background_choice, foreground_ratio, backgroud_color)
    
    # Generate the 3D model
    pipeline.set_seed(seed)
    rt_dict = pipeline(processed, scale=scale, step=step)
    stage1_images = rt_dict["stage1_images"]
    stage2_images = rt_dict["stage2_images"]
    np_imgs = np.concatenate(stage1_images, 1)
    np_xyzs = np.concatenate(stage2_images, 1)

    glb_path = generate3d(model, np_imgs, np_xyzs, args.device)
    return Image.fromarray(np_imgs), Image.fromarray(np_xyzs), glb_path

# Model initialization code
parser = argparse.ArgumentParser()
parser.add_argument(
    "--stage1_config",
    type=str,
    default="configs/nf7_v3_SNR_rd_size_stroke.yaml",
    help="config for stage1",
)
parser.add_argument(
    "--stage2_config",
    type=str,
    default="configs/stage2-v2-snr.yaml",
    help="config for stage2",
)

parser.add_argument("--device", type=str, default="cuda")
args = parser.parse_args()

crm_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="CRM.pth")
specs = json.load(open("configs/specs_objaverse_total.json"))
model = CRM(specs)
model.load_state_dict(torch.load(crm_path, map_location="cpu"), strict=False)
model = model.to(args.device)

stage1_config = OmegaConf.load(args.stage1_config).config
stage2_config = OmegaConf.load(args.stage2_config).config
stage2_sampler_config = stage2_config.sampler
stage1_sampler_config = stage1_config.sampler

stage1_model_config = stage1_config.models
stage2_model_config = stage2_config.models

xyz_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="ccm-diffusion.pth")
pixel_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="pixel-diffusion.pth")
stage1_model_config.resume = pixel_path
stage2_model_config.resume = xyz_path

pipeline = TwoStagePipeline(
    stage1_model_config,
    stage2_model_config,
    stage1_sampler_config,
    stage2_sampler_config,
    device=args.device,
    dtype=torch.float32
)

_DESCRIPTION = '''
* Our [official implementation](https://github.com/thu-ml/CRM) uses UV texture instead of vertex color. It has better texture than this online demo.
* Project page of CRM: https://ml.cs.tsinghua.edu.cn/~zhengyi/CRM/
* If you find the output unsatisfying, try using different seeds:)
'''

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model")
    gr.Markdown(_DESCRIPTION)
    
    with gr.Row():
        with gr.Column():
            image_input = gr.Image(
                label="Image input",
                type="pil",
                image_mode="RGBA",
                sources=["upload"]
            )
            
            with gr.Row():
                background_choice = gr.Radio(
                    choices=["Alpha as mask", "Auto Remove background"],
                    value="Auto Remove background",
                    label="Background choice"
                )
            
            with gr.Row():
                seed = gr.Number(value=1234, label="Seed", precision=0)
                guidance_scale = gr.Number(value=5.5, minimum=3, maximum=10, label="Guidance scale")
                step = gr.Number(value=30, minimum=30, maximum=100, label="Sample steps", precision=0)
            
            generate_btn = gr.Button("Generate 3D shape")
            
            gr.Examples(
                examples=[os.path.join("examples", i) for i in os.listdir("examples")],
                inputs=[image_input],
                examples_per_page=20
            )
        
        with gr.Column():
            image_output = gr.Image(label="Output RGB image", type="pil")
            xyz_output = gr.Image(label="Output CCM image", type="pil")
            output_model = gr.Model3D(label="Output 3D Model")
            
            gr.Markdown("Note: Ensure that the input image is correctly pre-processed into a grey background, otherwise the results will be unpredictable.")

    def process_and_generate_simple(image, seed, scale, step):
        if image is None:
            raise gr.Error("No image uploaded!")
        
        # Use default values for background processing
        processed = preprocess_image(image, "Auto Remove background", 1.0, "#7F7F7F")
        
        # Generate the 3D model
        pipeline.set_seed(seed)
        rt_dict = pipeline(processed, scale=scale, step=step)
        stage1_images = rt_dict["stage1_images"]
        stage2_images = rt_dict["stage2_images"]
        np_imgs = np.concatenate(stage1_images, 1)
        np_xyzs = np.concatenate(stage2_images, 1)

        glb_path = generate3d(model, np_imgs, np_xyzs, args.device)
        return Image.fromarray(np_imgs), Image.fromarray(np_xyzs), glb_path

    generate_btn.click(
        fn=process_and_generate_simple,
        inputs=[image_input, seed, guidance_scale, step],
        outputs=[image_output, xyz_output, output_model]
    )

    demo.queue().launch(

    )