import os
import spaces
import random
import shutil
import gradio as gr
from glob import glob
from pathlib import Path
import uuid
import argparse
import torch
import uvicorn
from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
import trimesh

parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str, default='tencent/Hunyuan3D-2mini')
parser.add_argument("--subfolder", type=str, default='hunyuan3d-dit-v2-mini-turbo')
parser.add_argument("--texgen_model_path", type=str, default='tencent/Hunyuan3D-2')
parser.add_argument('--port', type=int, default=7860)
parser.add_argument('--host', type=str, default='0.0.0.0')
parser.add_argument('--device', type=str, default='cuda')
parser.add_argument('--mc_algo', type=str, default='mc')
parser.add_argument('--cache_path', type=str, default='gradio_cache')
parser.add_argument('--enable_t23d', action='store_true')
parser.add_argument('--disable_tex', action='store_true')
parser.add_argument('--enable_flashvdm', action='store_true')
parser.add_argument('--compile', action='store_true')
parser.add_argument('--low_vram_mode', action='store_true')
args = parser.parse_args()
args.enable_flashvdm = True

SAVE_DIR = args.cache_path
os.makedirs(SAVE_DIR, exist_ok=True)

CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))

HTML_HEIGHT = 500
HTML_WIDTH = 500


def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed


def gen_save_folder(max_size=200):
    os.makedirs(SAVE_DIR, exist_ok=True)

    # 获取所有文件夹路径
    dirs = [f for f in Path(SAVE_DIR).iterdir() if f.is_dir()]

    # 如果文件夹数量超过 max_size，删除创建时间最久的文件夹
    if len(dirs) >= max_size:
        # 按创建时间排序，最久的排在前面
        oldest_dir = min(dirs, key=lambda x: x.stat().st_ctime)
        shutil.rmtree(oldest_dir)
        print(f"Removed the oldest folder: {oldest_dir}")

    # 生成一个新的 uuid 文件夹名称
    new_folder = os.path.join(SAVE_DIR, str(uuid.uuid4()))
    os.makedirs(new_folder, exist_ok=True)
    print(f"Created new folder: {new_folder}")

    return new_folder

def export_mesh(mesh, save_folder, textured=False, type='glb'):
    if textured:
        path = os.path.join(save_folder, f'textured_mesh.{type}')
    else:
        path = os.path.join(save_folder, f'white_mesh.{type}')
    if type not in ['glb', 'obj']:
        mesh.export(path)
    else:
        mesh.export(path, include_normals=textured)
    return path

def build_model_viewer_html(save_folder, height=660, width=790, textured=False):
    # Remove first folder from path to make relative path
    if textured:
        related_path = f"./textured_mesh.glb"
        template_name = './assets/modelviewer-textured-template.html'
        output_html_path = os.path.join(save_folder, f'textured_mesh.html')
    else:
        related_path = f"./white_mesh.glb"
        template_name = './assets/modelviewer-template.html'
        output_html_path = os.path.join(save_folder, f'white_mesh.html')
        
    offset = 50 if textured else 10
    with open(os.path.join(CURRENT_DIR, template_name), 'r', encoding='utf-8') as f:
        template_html = f.read()

    with open(output_html_path, 'w', encoding='utf-8') as f:
        template_html = template_html.replace('#height#', f'{height - offset}')
        template_html = template_html.replace('#width#', f'{width}')
        template_html = template_html.replace('#src#', f'{related_path}/')
        f.write(template_html)

    rel_path = os.path.relpath(output_html_path, SAVE_DIR)
    iframe_tag = f'<iframe src="/static/{rel_path}" height="{height}" width="100%" frameborder="0"></iframe>'
    print(
        f'Find html file {output_html_path}, {os.path.exists(output_html_path)}, relative HTML path is /static/{rel_path}')

    return f"""
        <div style='height: {height}; width: 100%;'>
        {iframe_tag}
        </div>
    """


from hy3dgen.shapegen import FaceReducer, FloaterRemover, DegenerateFaceRemover, MeshSimplifier, \
    Hunyuan3DDiTFlowMatchingPipeline
from hy3dgen.shapegen.pipelines import export_to_trimesh
from hy3dgen.rembg import BackgroundRemover

rmbg_worker = BackgroundRemover()
i23d_worker = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained(
    args.model_path,
    subfolder=args.subfolder,
    use_safetensors=True,
    device=args.device,
)
if args.enable_flashvdm:
    mc_algo = 'mc' if args.device in ['cpu', 'mps'] else args.mc_algo
    i23d_worker.enable_flashvdm(mc_algo=mc_algo)
if args.compile:
    i23d_worker.compile()

floater_remove_worker = FloaterRemover()
degenerate_face_remove_worker = DegenerateFaceRemover()
face_reduce_worker = FaceReducer()

progress=gr.Progress()

@spaces.GPU(duration=60)
def gen_shape(
    image=None,
    steps=50,
    guidance_scale=7.5,
    seed=1234,
    octree_resolution=256,
    num_chunks=200000,
    target_face_num=10000,
    randomize_seed: bool = False,
):
    progress(0,desc="Starting")

    def callback(step_idx, timestep, outputs):
        progress_value = ((step_idx+1.0)/steps)*(0.5/1.0)
        progress(progress_value, desc=f"Mesh generating, {step_idx + 1}/{steps} steps")


    if image is None:
        raise gr.Error("Please provide either a caption or an image.")


    seed = int(randomize_seed_fn(seed, randomize_seed))
    octree_resolution = int(octree_resolution)
    save_folder = gen_save_folder()
    # 先移除背景
    image = rmbg_worker(image.convert('RGB'))

    # 生成模型
    generator = torch.Generator()
    generator = generator.manual_seed(int(seed))
    outputs = i23d_worker(
        image=image,
        num_inference_steps=steps,
        guidance_scale=guidance_scale,
        generator=generator,
        octree_resolution=octree_resolution,
        num_chunks=num_chunks,
        output_type='mesh',
        callback=callback,
        callback_steps=1
    )

    mesh = export_to_trimesh(outputs)[0]

    path = export_mesh(mesh, save_folder, textured=False)

    # model_viewer_html = build_model_viewer_html(save_folder, height=HTML_HEIGHT, width=HTML_WIDTH)

    # return model_viewer_html, path

    if args.low_vram_mode:
        torch.cuda.empty_cache()
    
    if path is None:
        raise gr.Error('Please generate a mesh first.')

    # 简化模型
    print(f'exporting {path}')
    print(f'reduce face to {target_face_num}')

    mesh = trimesh.load(path)
    progress(0.5,desc="Optimizing mesh")

    mesh = floater_remove_worker(mesh)
    mesh = degenerate_face_remove_worker(mesh)
    progress(0.6,desc="Reducing mesh faces")
    mesh = face_reduce_worker(mesh, target_face_num)
    save_folder = gen_save_folder()

    progress(0.9,desc="Converting format")
    file_type = "obj"
    sourceObjPath = export_mesh(mesh, save_folder, textured=False, type=file_type)
    rel_objPath = os.path.relpath(sourceObjPath, SAVE_DIR)
    objPath = "/static/"+rel_objPath

    # for preview
    save_folder = gen_save_folder()
    _ = export_mesh(mesh, save_folder, textured=False)
    model_viewer_html = build_model_viewer_html(save_folder, height=HTML_HEIGHT, width=HTML_WIDTH, textured=False)


    glbPath = os.path.join(save_folder, f'white_mesh.glb')
    rel_glbPath = os.path.relpath(glbPath, SAVE_DIR)
    glbPath = "/static/"+rel_glbPath


    progress(1,desc="Complete")
    return model_viewer_html, gr.update(value=sourceObjPath, interactive=True), glbPath, objPath


    
def get_example_img_list():
    print('Loading example img list ...')
    return sorted(glob('./assets/example_images/**/*.png', recursive=True))
example_imgs = get_example_img_list()

HTML_OUTPUT_PLACEHOLDER = f"""
<div style='height: {500}px; width: 100%; border-radius: 8px; border-color: #e5e7eb; border-style: solid; border-width: 1px; display: flex; justify-content: center; align-items: center;'>
    <div style='text-align: center; font-size: 16px; color: #6b7280;'>
    <p style="color: #8d8d8d;">No mesh here.</p>
    </div>
</div>
"""
MAX_SEED = 1e7

title = "## AI 3D Model Generator"
description = "Our Image-to-3D Generator transforms your 2D photos into stunning, AI generated 3D models—ready for games, AR/VR, or 3D printing. Our AI 3D Modeling is based on Hunyuan 2.0. Check more in [imgto3d.ai](https://www.imgto3d.ai)."

with gr.Blocks().queue() as demo:
    gr.Markdown(title)
    gr.Markdown(description)
    with gr.Row():
        with gr.Column(scale=3):
            gr.Markdown("#### Image Prompt")
            image = gr.Image(sources=["upload"], label='Image', type='pil', image_mode='RGBA', height=290)
            gen_button = gr.Button(value='Generate Shape', variant='primary')
            with gr.Accordion("Advanced Options", open=False):
                with gr.Column():
                    seed = gr.Slider(
                        label="Seed",
                        minimum=0,
                        maximum=MAX_SEED,
                        step=1,
                        value=1234,
                        min_width=100,
                    )
                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
                with gr.Column():
                    num_steps = gr.Slider(maximum=100, minimum=1, value=5, step=1, label='Inference Steps')
                    octree_resolution = gr.Slider(maximum=512, minimum=16, value=256, label='Octree Resolution')
                with gr.Column():
                    cfg_scale = gr.Slider(maximum=20.0, minimum=1.0, value=5.5, step=0.1, label='Guidance Scale')
                    num_chunks = gr.Slider(maximum=5000000, minimum=1000, value=8000, label='Number of Chunks')
                target_face_num = gr.Slider(maximum=1000000, minimum=100, value=10000, label='Target Face Number')

        with gr.Column(scale=6):
            gr.Markdown("#### Generated Mesh")
            html_export_mesh = gr.HTML(HTML_OUTPUT_PLACEHOLDER, label='Output')
            file_export = gr.DownloadButton(label="Download", variant='primary', interactive=False)
            with gr.Row():
                objPath_output = gr.Text(label="Obj Path",interactive=False)
                glbPath_output = gr.Text(label="Glb Path",interactive=False)
                
        with gr.Column(scale=3):
            gr.Markdown("#### Image Examples")
            gr.Examples(examples=example_imgs, inputs=[image],
                        label=None, examples_per_page=18)
            
    gen_button.click(
        fn=gen_shape,
        inputs=[image,num_steps,cfg_scale,seed,octree_resolution,num_chunks,target_face_num, randomize_seed], 
        outputs=[html_export_mesh,file_export, glbPath_output, objPath_output]
    )    

 # https://discuss.huggingface.co/t/how-to-serve-an-html-file/33921/2
# create a FastAPI app
app = FastAPI()
# create a static directory to store the static files
static_dir = Path(SAVE_DIR).absolute()
static_dir.mkdir(parents=True, exist_ok=True)
app.mount("/static", StaticFiles(directory=static_dir, html=True), name="static")
shutil.copytree('./assets/env_maps', os.path.join(static_dir, 'env_maps'), dirs_exist_ok=True)

if args.low_vram_mode:
    torch.cuda.empty_cache()

app = gr.mount_gradio_app(app, demo, path="/")
# demo.launch()
uvicorn.run(app, host=args.host, port=args.port)