- An object detector model : This app only works with Object Detector models
- client id
- client secret
- model url
- optionnal : an app url, to generate doc
- fork this project and then clone
- fill the .env file ( see .env.example )
- Create a python virtual env and install ( we advise you t use python 3.8)
git clone [email protected]:previsionio/prevision-object-detector-app.git
cd prevision-object-detector-app.git
python3.8 -m venv env
pip install -r requirements.txt
Then create a .env file from the env.example and fill it with the information from your model
client_id=<YOUR CLIENT ID>>
client_secret=<YOUR CLIENT_SECRET>
model_url=<YOUR_MODEL_URL>
Then launch server using gunicorn
gunicorn --reload --bind 127.0.0.1:8080 --timeout 120 --limit-request-line 0 --access-logfile - run:app
Go to http://localhost:8080/ and check everything is fine. You should see a webapp. You can test the API with the curl command ( using any image you want ):
curl --location --request POST 'http://localhost:8080/api/model/prediction' \
--form 'img=@"./test_img.jpg"'
If both the webapp and curl command are ok, you can proceed to customisation or deployment
Server is flask based with jinja as template engine. You can edit the app/templates/base.html to tweak your footer and header.
You can change the content of the app/templates/about page or add page to you project in the run.py file.
You can change colors and font in the app/styles.css file.
About your model, you can edit advice from object detected and threshold in the routers/blueprint_model/adviceabout function.
If everything is fine you can deploy to your account. Juste deploy a new app, do not forget to fill the env var with the content of your .env file.
