import os
import shlex
import subprocess
import gradio as gr
import spaces
import torch
from diffusers import DiffusionPipeline
from gradio_client import Client, file

subprocess.run(
    shlex.split(
        "pip install wheel/diff_gaussian_rasterization-0.0.0-cp310-cp310-linux_x86_64.whl"
    )
)

TMP_DIR = "/tmp"
os.makedirs(TMP_DIR, exist_ok=True)


image_pipeline = DiffusionPipeline.from_pretrained(
    "ashawkey/imagedream-ipmv-diffusers",
    custom_pipeline="dylanebert/multi-view-diffusion",
    torch_dtype=torch.float16,
    trust_remote_code=True,
).to("cuda")


splat_pipeline = DiffusionPipeline.from_pretrained(
    "dylanebert/LGM",
    custom_pipeline="dylanebert/LGM",
    torch_dtype=torch.float16,
    trust_remote_code=True,
).to("cuda")


@spaces.GPU
def run(input_image, convert):
    input_image = input_image.astype("float32") / 255.0
    images = image_pipeline(
        "", input_image, guidance_scale=5, num_inference_steps=30, elevation=0
    )
    gaussians = splat_pipeline(images)
    output_ply_path = os.path.join(TMP_DIR, "output.ply")
    splat_pipeline.save_ply(gaussians, output_ply_path)
    if convert:
        output_mesh_path = convert_to_mesh(output_ply_path)
        return output_mesh_path
    else:
        return output_ply_path


def convert_to_mesh(input_ply):
    client = Client("https://dylanebert-splat-to-mesh.hf.space/")
    output_mesh_path = client.predict(file(input_ply), api_name="/run")
    client.close()
    return output_mesh_path


_TITLE = """LGM Mini"""

_DESCRIPTION = """
<div>
A lightweight version of <a href="https://huggingface.co/spaces/ashawkey/LGM">LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation</a>.
</div>
"""

css = """
#duplicate-button {
    margin: auto;
    color: white;
    background: #1565c0;
    border-radius: 100vh;
}
"""

block = gr.Blocks(title=_TITLE, css=css)
with block:
    gr.DuplicateButton(
        value="Duplicate Space for private use", elem_id="duplicate-button"
    )

    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("# " + _TITLE)
    gr.Markdown(_DESCRIPTION)

    with gr.Row(variant="panel"):
        with gr.Column(scale=1):

            def update_warning(checked):
                if checked:
                    return '<span style="color: #ff0000;">Warning: Mesh conversion takes several minutes</span>'
                else:
                    return ""

            input_image = gr.Image(label="image", type="numpy")
            convert_checkbox = gr.Checkbox(label="Convert to Mesh")
            warning = gr.HTML()
            convert_checkbox.change(
                fn=update_warning, inputs=[convert_checkbox], outputs=[warning]
            )
            button_gen = gr.Button("Generate")

        with gr.Column(scale=1):
            output_splat = gr.Model3D(label="3D Gaussians")

        button_gen.click(
            fn=run, inputs=[input_image, convert_checkbox], outputs=[output_splat]
        )

    gr.Examples(
        examples=[
            "data_test/frog_sweater.jpg",
            "data_test/bird.jpg",
            "data_test/boy.jpg",
            "data_test/cat_statue.jpg",
            "data_test/dragontoy.jpg",
            "data_test/gso_rabbit.jpg",
        ],
        inputs=[input_image],
        outputs=[output_splat],
        fn=lambda x: run(input_image=x, convert=False),
        cache_examples=True,
        label="Image-to-3D Examples",
    )

block.queue().launch(debug=True, share=True)