- when opening VSCode, install the suggested extensions (Python, Black Formatter and Pylance)
- create your python environment
python3 -m venv .venv
. Make sure to have a python version that tensorflow supports (ex. 3.11.4) - Use Gitbush terminal
- activate your environment with
source activate path\to\activation\file
- run the server with
python app.py
The server should answer on http://localhost:5000
You can deactivate the environment with deactivate
.
if you need to use new librairies, you can do it with pip
pip install [library name]
or pip3 install [library name]
or 'conda install'
- This is an academic project in the context of a "introduction to software development" course at Ecole des Ponts.
- The dataset exists in the folder Dataset and was taken from Kaggle open datasets.
- The provided python notebook contains data preprocessing and vectorization, model definition and training and inference. It's the main part of the project.
- The web interface is developed using python for backend and html/js/css for frontend.
- Actually, not all the packages in the file requirements.txt are required for this project. This file will be soon updated.