https://github.com/previsionio/prevision-nlp-query-app
Boilerplate to make a query in a corpus with Prevision Platform
https://github.com/previsionio/prevision-nlp-query-app
Last synced: about 1 month ago
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Boilerplate to make a query in a corpus with Prevision Platform
- Host: GitHub
- URL: https://github.com/previsionio/prevision-nlp-query-app
- Owner: previsionio
- Created: 2021-11-19T08:31:19.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-11-22T10:27:57.000Z (over 4 years ago)
- Last Synced: 2025-08-21T21:39:41.169Z (10 months ago)
- Language: CSS
- Size: 271 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Deploying an image detector app
## Pre-requiste
- 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
## Setup for Dev
- 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 git@github.com: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=>
client_secret=
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
## Customisation
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.
## Prod
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.
