# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_hough2image.py
# The original license file is LICENSE.ControlNet in this repo.
import gradio as gr


def create_demo(process, max_images=12, default_num_images=3):
    with gr.Blocks() as demo:
        with gr.Row():
            gr.Markdown('### Use a photo of your room and reimagine it with different styles with the power of ControlNet')
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(source='upload', type='numpy')
                prompt = gr.Textbox(label='Prompt')
                run_button = gr.Button(label='Run')
                with gr.Accordion('Advanced options', open=False):
                    num_samples = gr.Slider(label='Images',
                                            minimum=1,
                                            maximum=max_images,
                                            value=default_num_images,
                                            step=1)
                    image_resolution = gr.Slider(label='Image Resolution',
                                                 minimum=256,
                                                 maximum=512,
                                                 value=512,
                                                 step=256)
                    detect_resolution = gr.Slider(label='Hough Resolution',
                                                  minimum=128,
                                                  maximum=512,
                                                  value=512,
                                                  step=1)
                    mlsd_value_threshold = gr.Slider(
                        label='Hough value threshold (MLSD)',
                        minimum=0.01,
                        maximum=2.0,
                        value=0.1,
                        step=0.01)
                    mlsd_distance_threshold = gr.Slider(
                        label='Hough distance threshold (MLSD)',
                        minimum=0.01,
                        maximum=20.0,
                        value=0.1,
                        step=0.01)
                    num_steps = gr.Slider(label='Steps',
                                          minimum=1,
                                          maximum=100,
                                          value=20,
                                          step=1)
                    guidance_scale = gr.Slider(label='Guidance Scale',
                                               minimum=0.1,
                                               maximum=30.0,
                                               value=9.0,
                                               step=0.1)
                    seed = gr.Slider(label='Seed',
                                     minimum=-1,
                                     maximum=2147483647,
                                     step=1,
                                     randomize=True)
                    a_prompt = gr.Textbox(
                        label='Added Prompt',
                        value='best quality, extremely detailed, photo from Pinterest, interior, cinematic photo, ultra-detailed, ultra-realistic, award-winning')
                    n_prompt = gr.Textbox(
                        label='Negative Prompt',
                        value=
                        'lowres, cropped, worst quality, low quality'
                    )
            with gr.Column():
                result = gr.Gallery(label='Output',
                                    show_label=False,
                                    elem_id='gallery').style(grid=2,
                                                             height='auto')
        inputs = [
            input_image,
            prompt,
            a_prompt,
            n_prompt,
            num_samples,
            image_resolution,
            detect_resolution,
            num_steps,
            guidance_scale,
            seed,
            mlsd_value_threshold,
            mlsd_distance_threshold,
        ]
        prompt.submit(fn=process, inputs=inputs, outputs=result)
        run_button.click(fn=process,
                         inputs=inputs,
                         outputs=result,
                         api_name='hough')
        ex = gr.Examples(examples = [
            ["room.jpg", 
             "a room for gaming, with gaming chairs, gaming consoles and gaming computers",
            "best quality, extremely detailed, photo from Pinterest, interior, cinematic photo, ultra-detailed, ultra-realistic, award-winning",
            "lowres, cropped, worst quality, low quality",
            2,
            512,
            512,
            20,
            9.0,
            132794,
            0.1,
            0.1,
        ]], 
                         inputs = inputs, outputs = result, fn = process, cache_examples = True)
        
    return demo