README.md

image-expansion

This is a tool for expansion of images in browser

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   ├── visualization  <- Scripts to create exploratory and results oriented visualizations
│   │   └── visualize.py
│   └── server         <- Server 
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

---

Project based on the cookiecutter data science project template. #cookiecutterdatascience

Run local server

cd src/server
cp .env.example .env
python app.py

Run client

python src/server/client.py

Run production server

cd src/server
zip -r model2prod.zip . -x venv\* -x .git\* -x .idea\* -x __pycache__\*
scp model2prod.zip user@server:projects/model2prod.zip
ssh user@server
cd projects
unzip model2prod.zip -d model2prod
cd model2prod
python -m venv venv 
source venv/bin/activate
pip install -r requirements.txt
waitress-serve --port=12023 app:app

Upload your model

Add your model.ckpt and model.h5 files to package models Change the links on your files in the model.py at server package

Browser extension

To install browser extension take browser_extension package

Go to 'browser://extensions/' and use load unpacked

Docker