app.py

from pathlib import Path
from num2words import num2words
import numpy as np
import os
import random
import re
import torch
import json
from shapely.geometry.polygon import Polygon
from shapely.affinity import scale
from PIL import Image, ImageDraw, ImageOps, ImageFilter, ImageFont, ImageColor

os.system('pip install gradio==2.7.5')
import gradio as gr

from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM

finetuned = AutoModelForCausalLM.from_pretrained('model')
tokenizer = AutoTokenizer.from_pretrained('gpt2')

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

# Utility functions

def containsNumber(value):
    for character in value:
        if character.isdigit():
            return True
    return False
    
def creativity(intensity):
    if(intensity == 'Low'):
        top_p = 0.95
        top_k = 10
    elif(intensity == 'Medium'):
        top_p = 0.9
        top_k = 50
    if(intensity == 'High'):
        top_p = 0.85
        top_k = 100
    return top_p, top_k

housegan_labels = {"living_room": 1, "kitchen": 2, "bedroom": 3, "bathroom": 4, "missing": 5, "closet": 6, 
                         "balcony": 7, "corridor": 8, "dining_room": 9, "laundry_room": 10}

architext_colors = [[0, 0, 0], [249, 222, 182], [195, 209, 217], [250, 120, 128], [126, 202, 234], [190, 0, 198], [255, 255, 255], 
                   [6, 53, 17], [17, 33, 58], [132, 151, 246], [197, 203, 159], [6, 53, 17],]

regex = re.compile(".*?\((.*?)\)")

def draw_polygons(polygons, colors, im_size=(512, 512), b_color="white", fpath=None):
    image = Image.new("RGBA", im_size, color="white")
    draw = ImageDraw.Draw(image)
    for poly, color, in zip(polygons, colors):
        #get initial polygon coordinates
        xy = poly.exterior.xy
        coords = np.dstack((xy[1], xy[0])).flatten()
        # draw it on canvas, with the appropriate colors
        draw.polygon(list(coords), fill=(0, 0, 0))
        #get inner polygon coordinates
        small_poly = poly.buffer(-1, resolution=32, cap_style=2, join_style=2, mitre_limit=5.0)
        if small_poly.geom_type == 'MultiPolygon':
            mycoordslist = [list(x.exterior.coords) for x in small_poly]
            for coord in mycoordslist:
                coords = np.dstack((np.array(coord)[:,1], np.array(coord)[:, 0])).flatten()
                draw.polygon(list(coords), fill=tuple(color)) 
        elif poly.geom_type == 'Polygon':
            #get inner polygon coordinates
            xy2 = small_poly.exterior.xy
            coords2 = np.dstack((xy2[1], xy2[0])).flatten()
            # draw it on canvas, with the appropriate colors
            draw.polygon(list(coords2), fill=tuple(color))
    image = image.transpose(Image.FLIP_TOP_BOTTOM)
    if(fpath):
        image.save(fpath, quality=100, subsampling=0)
    return draw, image

def prompt_to_layout(user_prompt, intensity, fpath=None):
    if(containsNumber(user_prompt) == True):
        spaced_prompt = user_prompt.split(' ')
        new_prompt = ' '.join([word if word.isdigit() == False else num2words(int(word)).lower() for word in spaced_prompt])
        model_prompt = '[User prompt] {} [Layout]'.format(new_prompt)
    top_p, top_k = creativity(intensity)
    model_prompt = '[User prompt] {} [Layout]'.format(user_prompt)
    input_ids = tokenizer(model_prompt, return_tensors='pt').to(device)
    output = finetuned.generate(**input_ids, do_sample=True, top_p=top_p, top_k=top_k, 
                                eos_token_id=50256, max_length=400)
    output = tokenizer.batch_decode(output, skip_special_tokens=True)
    layout = output[0].split('[User prompt]')[1].split('[Layout] ')[1].split(', ')
    spaces = [txt.split(':')[0] for txt in layout]
    coords = [txt.split(':')[1].rstrip() for txt in layout]
    coordinates = [re.findall(regex, coord) for coord in coords]
    
    num_coords = []
    for coord in coordinates:
        temp = []
        for xy in coord:
            numbers = xy.split(',')
            temp.append(tuple([int(num)/14.2 for num in numbers]))
        num_coords.append(temp)
        
    new_spaces = []
    for i, v in enumerate(spaces):
        totalcount = spaces.count(v)
        count = spaces[:i].count(v)
        new_spaces.append(v + str(count + 1) if totalcount > 1 else v)
        
    out_dict = dict(zip(new_spaces, num_coords))
    out_dict = json.dumps(out_dict)
       
    polygons = []
    for coord in coordinates:
        polygons.append([point.split(',') for point in coord])  
    geom = []
    for poly in polygons:
        scaled_poly = scale(Polygon(np.array(poly, dtype=int)), xfact=2, yfact=2, origin=(0,0))
        geom.append(scaled_poly)  
    colors = [architext_colors[housegan_labels[space]] for space in spaces]
    _, im = draw_polygons(geom, colors, fpath=fpath)
    html = '<img class="labels" src="images/labels.png" />'
    legend = Image.open("labels.png")
    imgs_comb = np.vstack([im, legend])
    imgs_comb = Image.fromarray(imgs_comb)
    return imgs_comb, out_dict 
     
    
# Gradio App

custom_css="""
@import url("https://use.typekit.net/nid3pfr.css");
.gradio_wrapper .gradio_bg[is_embedded=false] {
  min-height: 80%;
}

.gradio_wrapper .gradio_bg[is_embedded=false] .gradio_page {
  display: flex;
  width: 100vw;
  min-height: 50vh;
  flex-direction: column;
  justify-content: center;
  align-items: center;
  margin: 0px;
  max-width: 100vw;
  background: #FFFFFF;
}

.gradio_wrapper .gradio_bg[is_embedded=false] .gradio_page .content {
  padding: 0px;
  margin: 0px;
}

.gradio_interface {
  width: 100vw;
  max-width: 1500px;
}

.gradio_interface .panel:nth-child(2) .component:nth-child(3) { 
    display:none
}

.gradio_wrapper .gradio_bg[theme=default] .panel_buttons {
  justify-content: flex-end;
}

.gradio_wrapper .gradio_bg[theme=default] .panel_button {
  flex: 0 0 0;
  min-width: 150px;
}

.gradio_wrapper .gradio_bg[theme=default] .gradio_interface .panel_button.submit { 
  background: #11213A;
  border-radius: 5px;
  color: #FFFFFF;
  text-transform: uppercase;
  min-width: 150px;
  height: 4em;
  letter-spacing: 0.15em;
  flex: 0 0 0;
}
.gradio_wrapper .gradio_bg[theme=default] .gradio_interface .panel_button.submit:hover { 
  background: #000000;
}

.input_text:focus {
  border-color: #FA7880;
}
.gradio_wrapper .gradio_bg[theme=default] .gradio_interface .input_text input, 
.gradio_wrapper .gradio_bg[theme=default] .gradio_interface .input_text textarea {
  font: 200 45px garamond-premier-pro-display, serif;
  line-height: 110%;
  color: #11213A;
  border-radius: 5px;
  padding: 15px;
  border: none;
  background: #F2F4F4;
}
.input_text textarea:focus-visible { 
  outline: none;
}
.gradio_wrapper .gradio_bg[theme=default] .gradio_interface .input_radio .radio_item.selected {
    background-color: #11213A;
}
.gradio_wrapper .gradio_bg[theme=default] .gradio_interface .input_radio .selected .radio_circle {
    border-color: #4365c4;
}
.gradio_wrapper .gradio_bg[theme=default] .gradio_interface .output_image {
    width: 100%;
    height: 40vw;
    max-height: 630px;
}
.gradio_wrapper .gradio_bg[theme=default] .gradio_interface .output_image .image_preview_holder {
    background: transparent;
}
.panel:nth-child(1) {
  margin-left: 50px;
  margin-right: 50px;
  margin-bottom: 80px;
  max-width: 750px;
}
.panel {
  background: transparent;
}
.gradio_wrapper .gradio_bg[theme=default] .gradio_interface .component_set {
  background: transparent;
  box-shadow: none;
}
.panel:nth-child(2) .gradio_wrapper .gradio_bg[theme=default] .gradio_interface .panel_header {
  display: none;
}

.gradio_wrapper .gradio_bg[is_embedded=false] .gradio_page .footer {
    transform: scale(0.75);
    filter: grayscale(1);
}

.labels {
    height: 20px;
    width: auto;
}

@media (max-width: 1000px){
    .panel:nth-child(1) {
        margin-left: 0px;
        margin-right: 0px;
    }
    .gradio_wrapper .gradio_bg[theme=default] .gradio_interface .output_image {
        height: auto;
    }
}
""" 
creative_slider = gr.inputs.Radio(["Low", "Medium", "High"], default="Low", label='Creativity')
textbox = gr.inputs.Textbox(placeholder='An apartment with two bedrooms and one bathroom', lines="3", 
                            label="DESCRIBE YOUR IDEAL APARTMENT")
generated = gr.outputs.Image(label='Generated Layout')
layout = gr.outputs.Textbox(label='Layout Coordinates')

iface = gr.Interface(fn=prompt_to_layout, inputs=[textbox, creative_slider], 
                     outputs=[generated, layout],
                     css=custom_css,
                     theme="default",
                     allow_flagging='never',
                     allow_screenshot=False,
                     thumbnail="thumbnail_gradio.PNG")

iface.launch(enable_queue=True)