Duplicate from architext/Architext_deployed

Co-authored-by: Julien Chaumond <julien-c@users.noreply.huggingface.co>

.gitattributes

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+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: ArchiText
+emoji: 💻
+colorFrom: gray
+colorTo: green
+sdk: gradio
+app_file: app.py
+pinned: false
+duplicated_from: architext/Architext_deployed
+---
+
+# Configuration
+
+`title`: _string_  
+Display title for the Space
+
+`emoji`: _string_  
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_  
+Can be either `gradio` or `streamlit`
+
+`app_file`: _string_  
+Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_  
+Whether the Space stays on top of your list.

app.py

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+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('pip3 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, share=True)

model/config.json

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+{
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPTJForCausalLM"
+  ],
+  "attn_pdrop": 0.0,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 50256,
+  "gradient_checkpointing": false,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gptj",
+  "n_embd": 768,
+  "n_head": 16,
+  "n_layer": 12,
+  "n_positions": 2048,
+  "rotary_dim": 64,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "transformers_version": "4.10.0.dev0",
+  "tokenizer_class": "GPT2Tokenizer",
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "temperature": 1.0,
+      "max_length": 50
+    }
+  },
+  "torch_dtype": "float16",
+  "use_cache": true,
+  "vocab_size": 50400
+}

model/pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4cd37ed39ddd8a7b95e24f558f632c53feb589aa7fa0904922ebda4aad54e89a
+size 329164611

requirements.txt

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+num2words
+numpy
+torch
+shapely
+pillow
+gradio==2.7.5
+jinja2
+transformers