print("importing gradio")
import gradio as gr
print("importing spaces")
import spaces
print("importing os")
import os
os.environ['SPCONV_ALGO'] = 'native'
print("importing typing")
from typing import *
print("importing torch")
import torch
print("importing numpy")
import numpy as np
print("importing imageio")
import imageio
print("importing uuid")
import uuid
print("importing easydict")
from easydict import EasyDict as edict
print("importing PIL")
from PIL import Image

print(f"Is CUDA available: {torch.cuda.is_available()}")
print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")


print("importing trellis image to 3d pipeline")
from trellis.pipelines import TrellisImageTo3DPipeline
print("importing trellis representations")
from trellis.representations import Gaussian, MeshExtractResult
print("importing trellis utils")
from trellis.utils import render_utils, postprocessing_utils


MAX_SEED = np.iinfo(np.int32).max
TMP_DIR = "/tmp/Trellis-demo"

os.makedirs(TMP_DIR, exist_ok=True)

@spaces.GPU
def preprocess_image(image: Image.Image) -> Tuple[str, Image.Image]:
    """
    Preprocess the input image.

    Args:
        image (Image.Image): The input image.

    Returns:
        str: uuid of the trial.
        Image.Image: The preprocessed image.
    """
    trial_id = str(uuid.uuid4())
    preload()
    processed_image = pipeline.preprocess_image(image)
    processed_image.save(f"{TMP_DIR}/{trial_id}.png")
    return trial_id, processed_image


def pack_state(gs: Gaussian, mesh: MeshExtractResult, trial_id: str) -> dict:
    return {
        'gaussian': {
            **gs.init_params,
            '_xyz': gs._xyz.cpu().numpy(),
            '_features_dc': gs._features_dc.cpu().numpy(),
            '_scaling': gs._scaling.cpu().numpy(),
            '_rotation': gs._rotation.cpu().numpy(),
            '_opacity': gs._opacity.cpu().numpy(),
        },
        'mesh': {
            'vertices': mesh.vertices.cpu().numpy(),
            'faces': mesh.faces.cpu().numpy(),
        },
        'trial_id': trial_id,
    }


def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
    gs = Gaussian(
        aabb=state['gaussian']['aabb'],
        sh_degree=state['gaussian']['sh_degree'],
        mininum_kernel_size=state['gaussian']['mininum_kernel_size'],
        scaling_bias=state['gaussian']['scaling_bias'],
        opacity_bias=state['gaussian']['opacity_bias'],
        scaling_activation=state['gaussian']['scaling_activation'],
    )
    gs._xyz = torch.tensor(state['gaussian']['_xyz'], device='cuda')
    gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda')
    gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda')
    gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda')
    gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda')

    mesh = edict(
        vertices=torch.tensor(state['mesh']['vertices'], device='cuda'),
        faces=torch.tensor(state['mesh']['faces'], device='cuda'),
    )

    return gs, mesh, state['trial_id']


@spaces.GPU
def image_to_3d(trial_id: str, seed: int, randomize_seed: bool, ss_guidance_strength: float, ss_sampling_steps: int, slat_guidance_strength: float, slat_sampling_steps: int) -> Tuple[dict, str]:
    """
    Convert an image to a 3D model.

    Args:
        trial_id (str): The uuid of the trial.
        seed (int): The random seed.
        randomize_seed (bool): Whether to randomize the seed.
        ss_guidance_strength (float): The guidance strength for sparse structure generation.
        ss_sampling_steps (int): The number of sampling steps for sparse structure generation.
        slat_guidance_strength (float): The guidance strength for structured latent generation.
        slat_sampling_steps (int): The number of sampling steps for structured latent generation.

    Returns:
        dict: The information of the generated 3D model.
        str: The path to the video of the 3D model.
    """
    if randomize_seed:
        seed = np.random.randint(0, MAX_SEED)


    preload()
    outputs = pipeline.run(
        Image.open(f"{TMP_DIR}/{trial_id}.png"),
        seed=seed,
        formats=["gaussian", "mesh"],
        preprocess_image=False,
        sparse_structure_sampler_params={
            "steps": ss_sampling_steps,
            "cfg_strength": ss_guidance_strength,
        },
        slat_sampler_params={
            "steps": slat_sampling_steps,
            "cfg_strength": slat_guidance_strength,
        },
    )
    video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color']
    video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal']
    video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
    trial_id = uuid.uuid4()
    video_path = f"{TMP_DIR}/{trial_id}.mp4"
    os.makedirs(os.path.dirname(video_path), exist_ok=True)
    imageio.mimsave(video_path, video, fps=15)
    state = pack_state(outputs['gaussian'][0], outputs['mesh'][0], trial_id)
    return state, video_path


@spaces.GPU
def extract_glb(state: dict, mesh_simplify: float, texture_size: int) -> Tuple[str, str]:
    """
    Extract a GLB file from the 3D model.

    Args:
        state (dict): The state of the generated 3D model.
        mesh_simplify (float): The mesh simplification factor.
        texture_size (int): The texture resolution.

    Returns:
        str: The path to the extracted GLB file.
    """
    gs, mesh, trial_id = unpack_state(state)
    glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
    glb_path = f"{TMP_DIR}/{trial_id}.glb"
    glb.export(glb_path)
    return glb_path, glb_path


def activate_button() -> gr.Button:
    return gr.Button(interactive=True)


def deactivate_button() -> gr.Button:
    return gr.Button(interactive=False)


def update(name):
    return f"Welcome to Gradio, {name}!"

with gr.Blocks() as demo:
    gr.Markdown("""
    ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
    * Upload an image and click "Generate" to create a 3D asset. If the image has alpha channel, it be used as the mask. Otherwise, we use `rembg` to remove the background.
    * If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
    """)


    with gr.Row():
        with gr.Column():

            image_prompt = gr.Image(label="Image Prompt", image_mode="RGBA", type="pil", height=300)

            with gr.Accordion(label="Generation Settings", open=False):
                seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
                gr.Markdown("Stage 1: Sparse Structure Generation")
                with gr.Row():
                    ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
                    ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
                gr.Markdown("Stage 2: Structured Latent Generation")
                with gr.Row():
                    slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=3.0, step=0.1)
                    slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)

            generate_btn = gr.Button("Generate")

            with gr.Accordion(label="GLB Extraction Settings", open=False):
                mesh_simplify = gr.Slider(0.9, 0.98, label="Simplify", value=0.95, step=0.01)
                texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)

            extract_glb_btn = gr.Button("Extract GLB", interactive=False)

        with gr.Column():

            # Todo - getting errors when using video output
            video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)

            model_output = gr.Model3D(label="Extracted GLB")
            # Todo - getting errors when using model output
            # model_output = LitModel3D(label="Extracted GLB", exposure=20.0, height=300)
            download_glb = gr.DownloadButton(label="Download GLB", interactive=False)

    trial_id = gr.Textbox(visible=False)
    output_buf = gr.State()

    # # Example images at the bottom of the page
    # with gr.Row():
    #     examples = gr.Examples(
    #         examples=[
    #             f'assets/example_image/{image}'
    #             for image in os.listdir("assets/example_image")
    #         ],
    #         inputs=[image_prompt],
    #         fn=preprocess_image,
    #         outputs=[trial_id, image_prompt],
    #         run_on_click=True,
    #         examples_per_page=64,
    #     )

    # Handlers
    image_prompt.upload(
        preprocess_image,
        inputs=[image_prompt],
        outputs=[trial_id, image_prompt],
    )
    image_prompt.clear(
        lambda: '',
        outputs=[trial_id],
    )


    generate_btn.click(
        image_to_3d,
        inputs=[trial_id, seed, randomize_seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps],
        outputs=[output_buf, video_output],
    ).then(
        activate_button,
        outputs=[extract_glb_btn],
    )

    video_output.clear(
        deactivate_button,
        outputs=[extract_glb_btn],
    )

    extract_glb_btn.click(
        extract_glb,
        inputs=[output_buf, mesh_simplify, texture_size],
        outputs=[model_output, download_glb],
    ).then(
        activate_button,
        outputs=[download_glb],
    )

    model_output.clear(
        deactivate_button,
        outputs=[download_glb],
    )


# Cleans up the temporary directory every 10 minutes
import threading
import time

def cleanup_tmp_dir():
    while True:
        if os.path.exists(TMP_DIR):
            for file in os.listdir(TMP_DIR):
                # remove files older than 10 minutes
                if time.time() - os.path.getmtime(os.path.join(TMP_DIR, file)) > 600:
                    os.remove(os.path.join(TMP_DIR, file))
        time.sleep(600)

cleanup_thread = threading.Thread(target=cleanup_tmp_dir)
cleanup_thread.start()


@spaces.GPU
def preload():
    global preloaded
    if preloaded:
        return
    preloaded = True
    pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
    pipeline.cuda()
    try:
        pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
    except:
        pass

# Launch the Gradio app
if __name__ == "__main__":
    global pipeline
    # pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
    # pipeline.cuda()
    # try:
    #     pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
    # except:
    #     pass
    demo.launch()