| An extensible web platform of ML and AI applications and APIs.
- Clone this repo
- Install requirements
- Run the app.py scripts
- Go to http://localhost:8080
- Register, Sign in, Enjoy the ML apps!
Screenshots :
1.1 Using virtualenv
First install virtualenv package
pip install virtualenv
Create the environement folder
virtualenv /path/to/my/env/envFolder
Activate the environment
- MacOS/Linux :
source envFolder/bin/activate
- Windows :
envFolder\Scripts\activate
1.2 Using Conda
First install conda in a given directory
pip install conda
Create a new environment
conda create --name myEnvName Flask==1.1.1
Activate the new environment
conda activate myEnvName
Install the requirements for the project (Some of them are already installed with Flask but that's ok) *
conda install -r requirements.txt
*Make sure you're in the project directory
In order to make sure the modifications you bring to the project's models give effect to the corresponding sql tables, you must set correctly the migration for each sub-project (web_apps and flowers_recogn for now).
1- Set Environment Variable
within the app.py directory for both Web_Apps & APIs
- MacOS/Linux
export FLASK_APP=app.py
- Windows
set FLASK_APP=app.py
2- Set migration directory
flask db init
3- Set migration file
flask db migrate -m "migration done"
4- Upgrade the migration
flask db upgrade
Download the models weights from this link and place them under the /ML_Models directories.
The modular architecture of the project requires running each compounding app seperately, thus, you should first open 2 separate bash command lines, one within the /Web_Apps directory, and the other under /APIs/FLOWERS_RECOGNITION. Once done, run the following command in both CLI :
python app.py
Open http://localhost:8080 and have fun.