import gradio as gr
import spaces
from gradio_litmodel3d import LitModel3D

import os
import shutil
os.environ['SPCONV_ALGO'] = 'native'
from typing import *
import torch
import numpy as np
import imageio
import uuid
from easydict import EasyDict as edict
from PIL import Image
from trellis.pipelines import TrellisImageTo3DPipeline
from trellis.representations import Gaussian, MeshExtractResult
from trellis.utils import render_utils, postprocessing_utils


MAX_SEED = np.iinfo(np.int32).max
TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
os.makedirs(TMP_DIR, exist_ok=True)


def start_session(req: gr.Request):
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    print(f'Creating user directory: {user_dir}')
    os.makedirs(user_dir, exist_ok=True)
    
def end_session(req: gr.Request):
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    print(f'Removing user directory: {user_dir}')
    shutil.rmtree(user_dir)

def preprocess_image(image: Image.Image) -> Tuple[str, Image.Image]:
    processed_image = pipeline.preprocess_image(image)
    return processed_image

def get_seed(randomize_seed: bool, seed: int) -> int:
    """Get the random seed."""
    return np.random.randint(0, MAX_SEED) if randomize_seed else seed

@spaces.GPU
def image_to_3d(
    image: Image.Image,
    seed: int,
    ss_guidance_strength: float,
    ss_sampling_steps: int,
    slat_guidance_strength: float,
    slat_sampling_steps: int,
    req: gr.Request,
) -> Tuple[str, str, str]:
    """
    Convert an image to a 3D model and save both video preview and full-quality GLB.
    
    Returns:
        Tuple[str, str, str]: (video_path, glb_path, download_path)
    """
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    outputs = pipeline.run(
        image,
        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,
        },
    )
    
    # Generate and save video preview
    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 = str(uuid.uuid4())
    video_path = os.path.join(user_dir, f"{trial_id}.mp4")
    imageio.mimsave(video_path, video, fps=15)
    
    # Save full-quality GLB directly from the generated mesh
    glb = postprocessing_utils.to_glb(
        outputs['gaussian'][0], 
        outputs['mesh'][0],
        simplify=0.0,  # No simplification
        texture_size=2048,  # Maximum texture resolution
        verbose=False
    )
    glb_path = os.path.join(user_dir, f"{trial_id}_full.glb")
    glb.export(glb_path)
    
    torch.cuda.empty_cache()
    return video_path, glb_path, glb_path

with gr.Blocks(delete_cache=(600, 600)) as demo:
    gr.Markdown("""
    ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
    * Upload an image and click "Generate" to create a high-quality 3D model
    * Once generation is complete, you can download the full-quality GLB file
    * The model will be in maximum quality with no reduction applied
    """)
    
    with gr.Row():
        with gr.Column():
            image_prompt = gr.Image(label="Image Prompt", format="png", 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, 500, 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, 500, label="Sampling Steps", value=12, step=1)

            generate_btn = gr.Button("Generate")

        with gr.Column():
            video_output = gr.Video(label="Generated 3D Asset Preview", autoplay=True, loop=True, height=300)
            model_output = LitModel3D(label="3D Model Preview", exposure=20.0, height=300)
            download_glb = gr.DownloadButton(label="Download Full-Quality GLB", interactive=False)
            
    # Example images
    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=[image_prompt],
            run_on_click=True,
            examples_per_page=64,
        )

    # Event handlers
    demo.load(start_session)
    demo.unload(end_session)
    
    image_prompt.upload(
        preprocess_image,
        inputs=[image_prompt],
        outputs=[image_prompt],
    )

    generate_btn.click(
        get_seed,
        inputs=[randomize_seed, seed],
        outputs=[seed],
    ).then(
        image_to_3d,
        inputs=[image_prompt, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps],
        outputs=[video_output, model_output, download_glb],
    ).then(
        lambda: gr.Button(interactive=True),
        outputs=[download_glb],
    )

# Launch the Gradio app
if __name__ == "__main__":
    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()