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 functools import partial

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

#HF_TOKEN = os.getenv("HF_TOKEN")

HEADER = """

"""


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

d = os.environ.get("DEVICE", None)
if d != None:
    device = d 

model = TSR.from_pretrained(
    "stabilityai/TripoSR",
    config_name="config.yaml",
    weight_name="model.ckpt",
#    token=HF_TOKEN
)
model.renderer.set_chunk_size(131072)
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(input_image, 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 = input_image.convert("RGB")
        image = remove_background(image, rembg_session)
        image = resize_foreground(image, foreground_ratio)
        image = fill_background(image)
    else:
        image = input_image
        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 = to_gradio_3d_orientation(mesh)
    mesh_path = tempfile.NamedTemporaryFile(suffix=".obj", delete=False)
    mesh_path2 = tempfile.NamedTemporaryFile(suffix=".glb", delete=False)
    mesh.export(mesh_path.name)
    mesh.export(mesh_path2.name)
    return mesh_path.name, mesh_path2.name

def run_example(image_pil):
    preprocessed = preprocess(image_pil, False, 0.9)
    mesh_name, mesn_name2 = generate(preprocessed)
    return preprocessed, mesh_name, mesh_name2

with gr.Blocks() as demo:
    gr.Markdown(HEADER)
    with gr.Row(variant="panel"):
        with gr.Column():
            with gr.Row():
                input_image = gr.Image(
                    label="Input Image",
                    image_mode="RGBA",
                    sources="upload",
                    type="pil",
                    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("obj"):
                output_model = gr.Model3D(
                    label="Output Model",
                    interactive=False,
                )
            with gr.Tab("glb"):
                output_model2 = gr.Model3D(
                    label="Output Model",
                    interactive=False,
                )
    # with gr.Row(variant="panel"):
    #     gr.Examples(
    #         examples=[
    #             os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))
    #         ],
    #         inputs=[input_image],
    #         outputs=[processed_image, output_model, output_model2],
    #         #cache_examples=True,
    #         fn=partial(run_example),
    #         label="Examples",
    #         examples_per_page=20
    #     )
    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, output_model2],
    )

demo.queue(max_size=10)
demo.launch()