The objective is this repository is to evaluate various techniques to extract and organize information from a floor plan
- Room Classification on Floor Plan Graphs using Graph Neural Networks.pdf.
- Parsing Line Segments of Floor Plan Images Using Graph Neural Networks.
- Extracting real estate values of rental apartment floor plans using graph convolutional networks.
- Room semantics inference using random forest and relational graph convolutional networks.
Here are the steps to upload floor plan and generate procurement log:
1 User enters name and email address.
2 User uploads floor plan as PDF file.
3a System fires email to Selection.AI with user name, email and attached floor plan.
3b System notifies sender/user the floor plan has been received with an estimated completion date.
...
4 Company generates procurement log.
5 Systems fires email to user with attached procurement log.
| Package | Version |
|---|---|
| python | 3.9.16 |
| torch | 2.0.1 |
| openai | 0.27.1 |
| matplotlib | 3.7.1 |
| scikit-learn | 1.2.2 |
| numpy | 1.24.3 |
| pandas | 2.0.2 |
| langchain | 0.0.2 |
| polars | 0.17.0 |
| fastapi | 0.97.0 |
| uvicorn | 0.22.0 |
| requests | 2.31.0 |
| pydantic | 1.10.9 |
| jinja2 | 3.1.2. |
| Date | Version |
|---|---|
| 06.27.2023 | 0.1 |
| 08.04.2023 | 0.2 |
pip install -r requirements
aiohttp==3.8.4
aiosignal==1.3.1
annotated-types==0.5.0
anyio==3.7.1
async-timeout==4.0.2
attrs==23.1.0
certifi==2023.5.7
charset-normalizer==3.1.0
click==8.1.6
distlib==0.3.7
docopt==0.6.2
exceptiongroup==1.1.2
fastapi==0.100.1
filelock==3.12.2
frozenlist==1.3.3
h11==0.14.0
idna==3.4
Jinja2==3.1.2
joblib==1.2.0
MarkupSafe==2.1.3
multidict==6.0.4
pbr==5.11.1
pipreqs==0.4.13
platformdirs==3.10.0
pydantic==2.1.1
pydantic_core==2.4.0
python-multipart==0.0.6
requests==2.31.0
sniffio==1.3.0
starlette==0.27.0
stevedore==5.1.0
threadpoolctl==3.1.0
tqdm==4.65.0
typing_extensions==4.7.1
uvicorn==0.23.2
virtualenv==20.24.2
virtualenv-clone==0.5.7
virtualenvwrapper==4.8.4
yarg==0.1.9
yarl==1.9.2
boto3~=1.28.18
cryptography~=41.0.3
argparse~=1.4.0
jmespath~=1.0.1
beautifulsoup4~=4.12.2
soupsieve~=2.4.1
cffi~=1.15.1
future~=0.18.2
Pillow~=9.5.0
pip~=23.1.2
configparser~=6.0.0
wheel~=0.38.4
docutils~=0.20.1
auth~=0.5.3
tornado~=6.3.2
botocore~=1.31.18
httplib2~=0.22.0
six~=1.15.0
wrapt~=1.15.0
PyYAML~=6.0
pytz~=2023.3
s3transfer~=0.6.1
Cython~=3.0.0
setuptools~=65.5.1
sympy~=1.12
mpmath~=1.3.0
selenium~=2.53.6
Unidecode~=1.3.6
SQLAlchemy~=2.0.15
python-dateutil~=2.8.2
numexpr~=2.8.4
fsspec~=2023.6.0
gunicorn~=21.2.0
greenlet~=2.0.2
networkx~=3.1
tenacity~=8.2.2
contourpy~=1.0.7
fonttools~=4.39.4
packaging~=23.1
tiktoken~=0.4.0
regex~=2023.6.3
pycparser~=2.21
pyparsing~=3.0.9
cycler~=0.11.0
kiwisolver~=1.4.4
zipp~=3.16.2
dl~=0.1.0
marshmallow~=3.19.0
toml~=0.10.2
Brotli~=1.0.9
keyring~=24.2.0
urllib3~=1.25.4
dnspython~=2.4.1
rsa~=4.9
pyasn1~=0.5.0
falcon~=3.1.1
Werkzeug~=2.3.6
cachetools~=5.3.1
google-api-python-client~=2.95.0
google-api-core~=2.11.1
google-auth~=2.22.0
google-auth-httplib2~=0.1.0
google-auth-oauthlib~=1.0.0
googleapis-common-protos~=1.60.0
blinker~=1.6.2
eventlet~=0.33.3
oauthlib~=3.2.2
uritemplate~=4.1.1
The minimum requirements are support for FastAPI Web interface and PostgreSQL Heroku/Production-standard
radiant-thicket-07669
Auto Cert Mgmt: true
Dynos: web: 1
Git URL: https://git.heroku.com/radiant-thicket-07669.git
Owner: pnicolas57@yahoo.com
Region: us
Repo Size: 4 MB
Slug Size: 159 MB
Stack: heroku-22
Web URL: https://radiant-thicket-07669-41bc01837f36.herokuapp.com/
Step 1: Review/validate requirement.txt.
Step 2: Specify Python version
echo python-3.9.6 > runtime.txt
Step 3: Set configuration variable
heroku config
heroku config:set TOKEN= ...
heroku config
heroku config:unset TOKEN // If needed.
Step 4: update GIT repository.
git add // if necessary.
git commit -m ‘…’ .
git push
Step 5: Build, launch and monitor application.
git push heroku main
heroku open
heroku logs --tail
Step 6: HTTPS support.
heroku certs:auto:enable –a app-name.
heroku certs:auto –a app-name.
There are several options to generate a bill of material from floor plan:
Direct image processing using a sequence of convolutional neural network and graph convolutional neural networks:
Convert grayscale and predefined shape of pixels into vocabulary for fine tuning of a large language model such as GPT or LLMA
The concept is to match patterns in the floor plan to a spreadsheet entry (row). The unique spreadsheet entries, partially defined become the labels for training the label.
The objective is to find graphic pattern that match each entry in the spreadsheet.
From a Meta paper: https://arxiv.org/abs/2308.13418v1
- Redesign/refresh GUI.
- Test with various browsers.
- Switch to HTTPS - Set up but failed "CDN not returning HTTP challenge".
- Fix issue with drag-drop file with UploadFile [Optional].