import os
import torch
import subprocess
import sys

# import pkg_resources
# # 在应用启动前强行打印当前环境，方便在 Space 日志里 debug
# def print_current_env():
#     print("\n" + "="*50)
#     print("🚀 正在扫描当前 Space 运行环境...")
#     print(f"Python 版本: {sys.version.split()[0]}")
    
#     # 你最关心的核心库
#     core_libs = [
#         "torch", "torchvision", "diffusers", "transformers", 
#         "accelerate", "spaces", "gradio", "bitsandbytes", 
#         "torchao", "peft", "safetensors", "tqdm"
#     ]
    
#     print("\n📦 核心依赖库版本:")
#     for lib in core_libs:
#         try:
#             version = pkg_resources.get_distribution(lib).version
#             print(f"  - {lib:15}: {version}")
#         except pkg_resources.DistributionNotFound:
#             print(f"  - {lib:15}: ❌ 未找到")

#     # 获取所有已安装的包并按字母排序
#     installed_packages = pkg_resources.working_set
#     installed_packages_list = sorted(["%s==%s" % (i.key, i.version) for i in installed_packages])
    
#     for package in installed_packages_list:
#         print(package)
        

#     print("\n🔗 PyTorch 细节:")
#     print(f"  - CUDA 是否可用: {torch.cuda.is_available()}")
#     if torch.cuda.is_available():
#         print(f"  - CUDA 版本: {torch.version.cuda}")
#         print(f"  - 当前 GPU: {torch.cuda.get_device_name(0)}")
    
#     print("="*50 + "\n")

#     print("\n" + "="*50 + "\n")
#     print(f"Python 解释器路径: {sys.executable}")
#     print(f"Python 版本: {sys.version}")
#     print("="*60 + "\n")

# # 立即执行扫描
# print_current_env()
# raise RuntimeError("✅ 环境快照已完成，程序已主动中断以保护日志。")


import spaces
import random
import time
import shutil
import gradio as gr
from glob import glob
from pathlib import Path
import uuid
import argparse
import uvicorn
from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
import trimesh
from transformers import AutoProcessor, AutoModelForImageClassification
from PIL import Image

import shlex
print('install custom')
subprocess.run(shlex.split("pip install custom_rasterizer-0.1-cp310-cp310-linux_x86_64.whl"), check=True)
    

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

# -------------------- NSFW 检测模型加载 --------------------
nsfw_processor = AutoProcessor.from_pretrained("Falconsai/nsfw_image_detection")
nsfw_model = AutoModelForImageClassification.from_pretrained("Falconsai/nsfw_image_detection").to(args.device)
# -----------------------------------------------------------


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>
    """

HAS_TEXTUREGEN = False
try:
    from hy3dgen.texgen import Hunyuan3DPaintPipeline

    texgen_worker = Hunyuan3DPaintPipeline.from_pretrained(args.texgen_model_path)
    if args.low_vram_mode:
        texgen_worker.enable_model_cpu_offload()
    HAS_TEXTUREGEN = True
except Exception as e:
    print(e)
    print("Failed to load texture generator.")
    print('Please try to install requirements by following README.md')
    HAS_TEXTUREGEN = False



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()


def detect_nsfw(image: Image.Image, threshold: float = 0.5) -> bool:
    """Returns True if image is NSFW"""
    inputs = nsfw_processor(images=image, return_tensors="pt").to(args.device)
    with torch.no_grad():
        outputs = nsfw_model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
        nsfw_score = probs[0][1].item()  # label 1 = NSFW
    return nsfw_score > threshold



progress=gr.Progress()


def get_duration(
    image=None,
    steps=50,
    guidance_scale=7.5,
    seed=1234,
    octree_resolution=256,
    num_chunks=200000,
    target_face_num=10000,
    randomize_seed: bool = False,
):
    if image is None:
        return 10
    nsfw = 1
    bgrm = 2
    mesh = 3 + 0.3 * (steps-5)
    reduce = 20
    texture = 15 + 10 * ((target_face_num-10000)/10000)
    if texture < 15:
        texture = 15
    print(f'mesh duration: {mesh}')
    print(f'texture duration: {texture}')
    duration = nsfw + bgrm +  int(mesh) + reduce + int(texture)
    print(f'function duration: {duration}')
    return duration


@spaces.GPU(duration=get_duration)
def _gen_shape_on_gpu(
    image=None,
    steps=50,
    guidance_scale=7.5,
    seed=1234,
    octree_resolution=256,
    num_chunks=200000,
    target_face_num=10000,
    randomize_seed: bool = False,
):
    start_time_0 = time.time()
   
    progress(0,desc="Starting")

    def callback(step_idx, timestep, outputs):
        if HAS_TEXTUREGEN:
            progress_value = ((step_idx+1.0)/steps)*(0.4/1.0)
        else:
            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:
        error_info = {
            "error": "Please provide either a caption or an image.",
            "status": "failed",
        }
        return None,None,None,None,error_info

    rgbImage = image.convert('RGB')
    
    start_time = time.time()
    # NSFW 检测
    if nsfw_model and nsfw_processor:
        if detect_nsfw(rgbImage):
            error_info = {
                "error": "The input image contains NSFW content and cannot be used. Please provide a different image and try again.",
                "status": "failed",
            }
            return None,None,None,None,error_info
    print(f'NSFW checker cost: {time.time() - start_time} ms')

    start_time = time.time()
    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(rgbImage)

    print(f'Background remover cost: {time.time() - start_time} ms')

    start_time = time.time()
    # 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
    )
    print(f'num_chunks: {num_chunks}')
    print(f'steps: {steps}')
    print(f'octree_resolution: {octree_resolution}')
    print(f'Mesh generator cost: {time.time() - start_time} ms')

    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:
        error_info = {
            "error": "'Please generate a mesh first.'",
            "status": "failed",
        }
        return None,None,None,None,error_info

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

    start_time = time.time()
    mesh = trimesh.load(path)
    if HAS_TEXTUREGEN:
        progress(0.4,desc="Optimizing mesh")
    else:
        progress(0.5,desc="Optimizing mesh")

    mesh = floater_remove_worker(mesh)
    mesh = degenerate_face_remove_worker(mesh)
    if HAS_TEXTUREGEN:
        progress(0.5,desc="Reducing mesh faces")
    else:
        progress(0.6,desc="Reducing mesh faces")

    mesh = face_reduce_worker(mesh, target_face_num)
    print(f'target_face_num: {target_face_num}')
    print(f'Mesh Reducing cost: {time.time() - start_time} ms')

    start_time = time.time()
    if HAS_TEXTUREGEN:
      progress(0.7,desc="Texture generating")
      textured_mesh = texgen_worker(mesh, image)
    print(f'Texture generator cost: {time.time() - start_time} ms')

    
    # 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

    start_time = time.time()
    # for preview
    save_folder = gen_save_folder()
    if HAS_TEXTUREGEN:
      sourceGlbPath = export_mesh(textured_mesh, save_folder, textured=True)
    else:
      sourceGlbPath = export_mesh(mesh, save_folder, textured=False)

    model_viewer_html = build_model_viewer_html(save_folder, height=HTML_HEIGHT, width=HTML_WIDTH, textured=HAS_TEXTUREGEN)

    if HAS_TEXTUREGEN:
      glbPath = os.path.join(save_folder, f'textured_mesh.glb')
    else:
      glbPath = os.path.join(save_folder, f'white_mesh.glb')

    rel_glbPath = os.path.relpath(glbPath, SAVE_DIR)
    glbPath = "/static/"+rel_glbPath

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

    print(f'Export cost: {time.time() - start_time} ms')
    progress(1,desc="Complete")
    info = {
        "status": "success"
    }

    print(f'All cost: {time.time() - start_time_0} ms')
    return model_viewer_html, gr.update(value=sourceGlbPath, interactive=True), glbPath, info


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,
):
    # 调用 GPU 函数
    html_export_mesh,file_export,glbPath_output, info = _gen_shape_on_gpu(
        image, 
        steps,
        guidance_scale, 
        seed,
        octree_resolution,
        num_chunks, 
        target_face_num,
        randomize_seed
    )
    # 如果出错，抛出异常
    if info["status"] == "failed":
        raise gr.Error(info["error"])
    print(f'file_export: {file_export}')
    print(f'glbPath_output: {glbPath_output}')
    return html_export_mesh, file_export, glbPath_output
    
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():
                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]
    )    

if __name__ == "__main__":
    # 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()
    from spaces import zero
    zero.startup()
    uvicorn.run(app, host=args.host, port=args.port)