import spaces
import gradio as gr
import os
import sys
from glob import glob
import time
from typing import Any, Union

import numpy as np
import torch
import trimesh
from huggingface_hub import snapshot_download
from PIL import Image
from accelerate.utils import set_seed

from src.utils.data_utils import get_colored_mesh_composition, scene_to_parts, load_surfaces
from src.utils.render_utils import render_views_around_mesh, render_normal_views_around_mesh, make_grid_for_images_or_videos, export_renderings
from src.pipelines.pipeline_partcrafter import PartCrafterPipeline
from src.utils.image_utils import prepare_image
from src.models.briarmbg import BriaRMBG

# Constants
MAX_NUM_PARTS = 16
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
DTYPE = torch.float16

# Download and initialize models
partcrafter_weights_dir = "pretrained_weights/PartCrafter"
rmbg_weights_dir = "pretrained_weights/RMBG-1.4"
snapshot_download(repo_id="wgsxm/PartCrafter", local_dir=partcrafter_weights_dir)
snapshot_download(repo_id="briaai/RMBG-1.4", local_dir=rmbg_weights_dir)

rmbg_net = BriaRMBG.from_pretrained(rmbg_weights_dir).to(DEVICE)
rmbg_net.eval()
pipe: PartCrafterPipeline = PartCrafterPipeline.from_pretrained(partcrafter_weights_dir).to(DEVICE, DTYPE)

@spaces.GPU()
@torch.no_grad()
def run_triposg(image: Image.Image,
                num_parts: int,
                seed: int,
                num_tokens: int,
                num_inference_steps: int,
                guidance_scale: float,
                max_num_expanded_coords: float,
                use_flash_decoder: bool,
                rmbg: bool):
    """
    Generate 3D part meshes from an input image.
    """
    if rmbg:
        img_pil = prepare_image(image, bg_color=np.array([1.0, 1.0, 1.0]), rmbg_net=rmbg_net)
    else:
        img_pil = image

    set_seed(seed)
    start_time = time.time()
    outputs = pipe(
        image=[img_pil] * num_parts,
        attention_kwargs={"num_parts": num_parts},
        num_tokens=num_tokens,
        generator=torch.Generator(device=pipe.device).manual_seed(seed),
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        max_num_expanded_coords=max_num_expanded_coords,
        use_flash_decoder=use_flash_decoder,
    ).meshes
    duration = time.time() - start_time
    print(f"Generation time: {duration:.2f}s")

    # Ensure no None outputs
    for i, mesh in enumerate(outputs):
        if mesh is None:
            outputs[i] = trimesh.Trimesh(vertices=[[0,0,0]], faces=[[0,0,0]])

    # Merge and color
    merged = get_colored_mesh_composition(outputs)

    # Export meshes and return results
    timestamp = time.strftime("%Y%m%d_%H%M%S")
    export_dir = os.path.join("results", timestamp)
    os.makedirs(export_dir, exist_ok=True)
    for idx, mesh in enumerate(outputs):
        mesh.export(os.path.join(export_dir, f"part_{idx:02}.glb"))
    merged.export(os.path.join(export_dir, "object.glb"))

    return merged, export_dir

# Gradio Interface
def build_demo():
    with gr.Blocks() as demo:
        gr.Markdown("# PartCrafter 3D Generation Demo")
        with gr.Row():
            with gr.Column(scale=1):
                input_image = gr.Image(type="pil", label="Input Image")
                num_parts = gr.Slider(1, MAX_NUM_PARTS, value=4, step=1, label="Number of Parts")
                seed = gr.Number(value=0, label="Random Seed", precision=0)
                num_tokens = gr.Slider(256, 2048, value=1024, step=64, label="Num Tokens")
                num_steps = gr.Slider(1, 100, value=50, step=1, label="Inference Steps")
                guidance = gr.Slider(1.0, 20.0, value=7.0, step=0.1, label="Guidance Scale")
                max_coords = gr.Text(value="1e9", label="Max Expanded Coords")
                flash_decoder = gr.Checkbox(value=False, label="Use Flash Decoder")
                remove_bg = gr.Checkbox(value=False, label="Remove Background (RMBG)")
                run_button = gr.Button("Generate 3D Parts")
            with gr.Column(scale=1):
                output_model = gr.Model3D(label="Merged 3D Object")
                output_dir = gr.Textbox(label="Export Directory")

        run_button.click(fn=run_triposg,
                         inputs=[input_image, num_parts, seed, num_tokens, num_steps,
                                 guidance, max_coords, flash_decoder, remove_bg],
                         outputs=[output_model, output_dir])
    return demo

if __name__ == "__main__":
    demo = build_demo()
    demo.launch()