Project involves to create a RESTFUL API with use case:
- Upload pictures and store them
- Upload pictures and predict from a model
This project uses PyTorch densenet161 as example. Does the image proccesing for that specific pre-trained model. Model could be change on enviroment and the width and height of image too.
As it had been said, this project has 2 endpoints. Uploading an image
URL: localhost:5000/api/upload
Content-type: form-data
Body:
> image: image with extension .jpeg, .jpg or .png
> token: secret of a user for saving method
Prediction on image
URL: localhost:5000/api/predict
Content-type: form-data
Body:
> image: image with extension .jpeg, .jpg or .png
> token: secret of a user for saving method
Project uses PyTorch and other packages, to install run the followings commands
pip install -r requirements.txt
pip install https://download.pytorch.org/whl/cu100/torch-1.1.0-cp37-cp37m-linux_x86_64.whl
pip install https://download.pytorch.org/whl/cu100/torchvision-0.3.0-cp37-cp37m-linux_x86_64.whlNote that could be other versions as pip is currently not working for PyTorch.
Project does use an .env with the following variables
UPLOAD_DIRECTORY='path/to/directory/' default='app/persistent/'
DEV_MODE=True default
PORT=5000 default
MODEL_PATH='path/to/model' default='app/models'
WIDTH=224
HEIGHT=224
Running on root folder of the project and set the FLASK APP as following Windows
set FLASK_APP=app/app.py
Unix
export FLASK_APP=app/app.pythen run
flask run
Running on root folder of the project
pytestProject has a dockefile running a not standart PyTorch image. To build the image
docker build -t curious_api .To run the container
docker run -p 5000:5000 curious_api