import gradio as gr
import argparse
import os, sys
import torch

def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument('--device', type=str, default='cpu')
    parser.add_argument('--theme', type=str)
    parser.add_argument('--live', action='store_true')
    parser.add_argument('--share', action='store_true')
    parser.add_argument('--port', type=int)
    parser.add_argument('--disable-queue',
                        dest='enable_queue',
                        action='store_false')
    parser.add_argument('--allow-flagging', type=str, default='never')
    parser.add_argument('--allow-screenshot', action='store_true')
    return parser.parse_args()

_TITLE = '''DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation'''

_DESCRIPTION = '''
<div>
<a style="display:inline-block" href="https://dreamgaussian.github.io"><img src='https://img.shields.io/badge/public_website-8A2BE2'></a>
<a style="display:inline-block; margin-left: .5em" href="https://arxiv.org/abs/2309.16653"><img src="https://img.shields.io/badge/2306.16928-f9f7f7?logo=data:image/png;base64,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"></a>
<a style="display:inline-block; margin-left: .5em" href='https://github.com/dreamgaussian/dreamgaussian'><img src='https://img.shields.io/github/stars/dreamgaussian/dreamgaussian?style=social'/></a>
</div>
We present DreamGausssion, a 3D content generation framework that significantly improves the efficiency of 3D content creation. 
'''
_IMG_USER_GUIDE = "Please upload an image in the block above (or choose an example above) and click **Run Generation**." 
_TXT_USER_GUIDE = "Please type what you want to generate in the block above and click **Run Generation**." 

# trigger Image-to-3D model
def inference_img(img):
    pass

# trigger Text-to-3D model
def inference_txt(txt):
    pass

def run_demo():
    args = parse_args()
    args.device = 'cuda' if torch.cuda.is_available() else 'cpu'
    print('*** Now using %s.'%(args.device))

    # append README as extra info
    with open('README.md', 'r') as f:
        article = f.read()

    # NOTE: Examples must match inputs
    example_folder = os.path.join(os.path.dirname(__file__), 'demo_examples')
    example_fns = os.listdir(example_folder)
    example_fns.sort()
    examples_full = [os.path.join(example_folder, x) for x in example_fns if x.endswith('.png')]

    # Compose demo layout & data flow
    with gr.Blocks(title=_TITLE, css="style.css") as demo:
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown('# ' + _TITLE)
            with gr.Column(scale=0):
                gr.DuplicateButton(value='Duplicate Space for private use',
                            elem_id='duplicate-button')
        gr.Markdown(_DESCRIPTION)

        # Image-to-3D
        with gr.Row(variant='panel'):
            with gr.Column(scale=6):
                image_block = gr.Image(type='pil', image_mode='RGBA', height=290, label='Input image', tool=None)

                elevation_slider = gr.Slider(-90, 90, value=0, step=1, label='Estimated elevation angle')
                gr.Markdown("default to 0 (horizontal), range from [-90, 90]. If you upload a look-down image, try a value like -30")

                preprocess_chk = gr.Checkbox(True, label='Preprocess image automatically (remove background and recenter object)')

                gr.Examples(
                    examples=examples_full,  # NOTE: elements must match inputs list!
                    inputs=[image_block],
                    outputs=[image_block],
                    cache_examples=False,
                    label='Examples (click one of the images below to start)',
                    examples_per_page=40
                )
                img_run_btn = gr.Button('Run Generation', variant='primary', interactive=False)
                img_guide_text = gr.Markdown(_IMG_USER_GUIDE, visible=True)
                
            with gr.Column(scale=4):
                processed_image_block = gr.Image(type='pil', image_mode='RGBA', height=290, label='Processed image', tool=None)
                img_mesh_output = gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="Textured Mesh", elem_id="img-model-3d-out")
        
        # Text-to-3D
        with gr.Row(variant='panel'):
            with gr.Column(scale=6):
                text_block = gr.Textbox(label="Input text")

                txt_run_btn = gr.Button('Run Generation', variant='primary', interactive=False)
                txt_guide_text = gr.Markdown(_TXT_USER_GUIDE, visible=True)
                
            with gr.Column(scale=4):
                txt_mesh_output = gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="Textured Mesh", elem_id="txt-model-3d-out")
        
        gr.Markdown(article)
        gr.HTML("""
            <div class="footer">
                <p> 
                This is a test demo
                </p>
            </div>
        """)

    demo.queue().launch(share=True, max_threads=80) # auth=("admin", os.environ['PASSWD'])

if __name__ == '__main__':
    run_demo()