import gradio as gr
import numpy as np
import cv2
from PIL import Image
import torch
import base64
import requests 
from io import BytesIO
from region_control import MultiDiffusion, get_views, preprocess_mask
from sketch_helper import get_high_freq_colors, color_quantization, create_binary_matrix
MAX_COLORS = 12

sd = MultiDiffusion("cuda", "2.0")

canvas_html = "<div id='canvas-root'></div>"
load_js = """
async () => {
const url = "https://huggingface.co/datasets/radames/gradio-components/raw/main/sketch-canvas.js"
fetch(url)
  .then(res => res.text())
  .then(text => {
    const script = document.createElement('script');
    script.type = "module"
    script.src = URL.createObjectURL(new Blob([text], { type: 'application/javascript' }));
    document.head.appendChild(script);
  });
}
"""

get_js_colors = """
async (canvasData) => {
  const canvasEl = document.getElementById("canvas-root");
  return [canvasEl._data]
}
"""

set_canvas_size ="""
async (aspect) => {
  if(aspect ==='square'){
    _updateCanvas(512,512)
  }
  if(aspect ==='horizontal'){
    _updateCanvas(768,512)
  }
  if(aspect ==='vertical'){
    _updateCanvas(512,768)
  }
}
"""

def process_sketch(canvas_data, binary_matrixes):
  base64_img = canvas_data['image']
  image_data = base64.b64decode(base64_img.split(',')[1])
  image = Image.open(BytesIO(image_data))
  im2arr = np.array(image)
  colors = [tuple(map(int, rgb[4:-1].split(','))) for rgb in canvas_data['colors']]
  colors_fixed = []
  for color in colors:
    r, g, b = color
    if any(c != 255 for c in (r, g, b)):
      binary_matrix = create_binary_matrix(im2arr, (r,g,b))
      binary_matrixes.append(binary_matrix)
      colors_fixed.append(gr.update(value=f'<div style="display:flex;align-items: center;justify-content: center"><img width="20%" style="margin-right: 1em" src="file/{binary_matrix}" /><div class="color-bg-item" style="background-color: rgb({r},{g},{b})"></div></div>'))
  visibilities = []
  colors = []
  for n in range(MAX_COLORS):
    visibilities.append(gr.update(visible=False))
    colors.append(gr.update(value=f'<div class="color-bg-item" style="background-color: black"></div>'))
  for n in range(len(colors)-1):
    visibilities[n] = gr.update(visible=True)
    colors[n] = colors_fixed[n]
  return [gr.update(visible=True), binary_matrixes, *visibilities, *colors]

def process_generation(binary_matrixes, master_prompt, *prompts):
    clipped_prompts = prompts[:len(binary_matrixes)]
    prompts = [master_prompt] + list(clipped_prompts)
    neg_prompts = [""] * len(prompts)
    fg_masks = torch.cat([preprocess_mask(mask_path, 512 // 8, 512 // 8, "cuda") for mask_path in binary_matrixes])
    bg_mask = 1 - torch.sum(fg_masks, dim=0, keepdim=True)
    bg_mask[bg_mask < 0] = 0
    masks = torch.cat([bg_mask, fg_masks])
    print(masks.size())
    image = sd.generate(masks, prompts, neg_prompts, 512, 512, 50, bootstrapping=20)
    return(image)

css = '''
#color-bg{display:flex;justify-content: center;align-items: center;}
.color-bg-item{width: 100%; height: 32px}
#main_button{width:100%}
.isPopup.svelte-160vdtq {
    top: -342px !important;
    z-index: 10001 !important;
    left: -25px !important;
}
.alpha.svelte-2ybi8r, .color.svelte-2ybi8r {
    width: 25px !important;
    height: 25px !important;
}
<style>

'''
def update_css(aspect):
  if(aspect=='Square'):
    return [gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)]
  elif(aspect == 'Horizontal'):
   return [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)]
  elif(aspect=='Vertical'):
    return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)]

with gr.Blocks(css=css) as demo:
  binary_matrixes = gr.State([])
  gr.Markdown('''## Control your Stable Diffusion generation with Sketches
  This Space demonstrates MultiDiffusion region-based generation using Stable Diffusion model. To get started, draw your masks and type your prompts. More details in the [project page](https://multidiffusion.github.io).
  ''')
  with gr.Row():
    with gr.Box(elem_id="main-image"):
      #with gr.Row():
      canvas_data = gr.JSON(value={}, visible=False)
      canvas = gr.HTML(canvas_html)
      #image = gr.Image(interactive=True, tool="color-sketch", source="canvas", type="pil", shape=(512,512), brush_radius=45)
      
      #image_horizontal = gr.Image(interactive=True, tool="color-sketch", source="canvas", type="pil", shape=(768,512), visible=False, brush_radius=45)
      #image_vertical = gr.Image(interactive=True, tool="color-sketch", source="canvas", type="pil", shape=(512, 768), visible=False, brush_radius=45)
      #with gr.Row():
      #    aspect = gr.Radio(["Square", "Horizontal", "Vertical"], value="Square", label="Aspect Ratio")
      button_run = gr.Button("I've finished my sketch",elem_id="main_button", interactive=True)
      
      prompts = []
      colors = []
      color_row = [None] * MAX_COLORS
      with gr.Column(visible=False) as post_sketch:
        general_prompt = gr.Textbox(label="General Prompt")
        for n in range(MAX_COLORS):
          with gr.Row(visible=False) as color_row[n]:
            with gr.Box(elem_id="color-bg"):
              colors.append(gr.HTML('<div class="color-bg-item" style="background-color: black"></div>'))
            prompts.append(gr.Textbox(label="Prompt for this mask"))
        final_run_btn = gr.Button("Generate!")
    
    out_image = gr.Image(label="Result")
  gr.Markdown('''
  ![Examples](https://multidiffusion.github.io/pics/tight.jpg)
  ''')
  #css_height = gr.HTML("<style>#main-image{width: 512px} .fixed-height{height: 512px !important}</style>")
  #aspect.change(update_css, inputs=aspect, outputs=[image, image_horizontal, image_vertical])
  button_run.click(process_sketch, inputs=[canvas_data, binary_matrixes], outputs=[post_sketch, binary_matrixes, *color_row, *colors], _js=get_js_colors)
  final_run_btn.click(process_generation, inputs=[binary_matrixes, general_prompt, *prompts], outputs=out_image)
  demo.load(None, None, None, _js=load_js)
demo.launch(debug=True)