import logging
import os
import tempfile
import time

import gradio as gr
import numpy as np
import rembg
import torch
from PIL import Image

from tsr.system import TSR
from tsr.utils import remove_background, resize_foreground, to_gradio_3d_orientation

HF_TOKEN = os.getenv("HF_TOKEN")

if torch.cuda.is_available():
    device = "cuda:0"
else:
    device = "cpu"

model = TSR.from_pretrained(
    "stabilityai/TripoSR",
    config_name="config.yaml",
    weight_name="model.ckpt",
    token=HF_TOKEN
)
model.to(device)

rembg_session = rembg.new_session()


def check_input_image(input_image):
    if input_image is None:
        raise gr.Error("No image uploaded!")


def preprocess(image_path, do_remove_background, foreground_ratio):
    def fill_background(image):
        image = np.array(image).astype(np.float32) / 255.0
        image = image[:, :, :3] * image[:, :, 3:4] + (1 - image[:, :, 3:4]) * 0.5
        image = Image.fromarray((image * 255.0).astype(np.uint8))
        return image

    if do_remove_background:
        image = remove_background(Image.open(image_path), rembg_session)
        image = resize_foreground(image, foreground_ratio)
        image = fill_background(image)
    else:
        image = Image.open(image_path)
        if image.mode == "RGBA":
            image = fill_background(image)
    return image


def generate(image):
    scene_codes = model(image, device=device)
    mesh = model.extract_mesh(scene_codes)[0]
    mesh.vertices = to_gradio_3d_orientation(mesh.vertices)
    mesh_path = tempfile.NamedTemporaryFile(suffix=".obj", delete=False)
    mesh.export(mesh_path.name)
    return mesh_path.name


with gr.Blocks() as demo:
    gr.Markdown(
        """
    ## TripoSR Demo
    [TripoSR](https://github.com/VAST-AI-Research/TripoSR) is a state-of-the-art open-source model for **fast** feedforward 3D reconstruction from a single image, collaboratively developed by [Tripo AI](https://www.tripo3d.ai/) and [Stability AI](https://stability.ai/).
    """
    )
    with gr.Row(variant="panel"):
        with gr.Column():
            with gr.Row():
                input_image = gr.Image(
                    label="Input Image",
                    sources="upload",
                    type="filepath",
                    elem_id="content_image",
                )
                processed_image = gr.Image(label="Processed Image", interactive=False)
            with gr.Row():
                with gr.Group():
                    do_remove_background = gr.Checkbox(
                        label="Remove Background", value=True
                    )
                    foreground_ratio = gr.Slider(
                        label="Foreground Ratio",
                        minimum=0.5,
                        maximum=1.0,
                        value=0.85,
                        step=0.05,
                    )
            with gr.Row():
                submit = gr.Button("Generate", elem_id="generate", variant="primary")
        with gr.Column():
            with gr.Tab("Model"):
                output_model = gr.Model3D(
                    label="Output Model",
                    interactive=False,
                )
                gr.Markdown(
                    """
                Note: The model shown here will be flipped due to some visualization issues. Please download to get the correct result.
                """
                )
    submit.click(fn=check_input_image, inputs=[input_image]).success(
        fn=preprocess,
        inputs=[input_image, do_remove_background, foreground_ratio],
        outputs=[processed_image],
    ).success(
        fn=generate,
        inputs=[processed_image],
        outputs=[output_model],
    )

demo.queue(max_size=1)
demo.launch()