*** Presentation Speech ***

Retails is a dynamic environment, which is highly variable in terms of pricing, branding, shopper behavior and trends. Therefore, continuous testing and optimizing the business decisions are crucial. With Tirico shopper flow analytics, the retailers gain the essential insights to maximize their revenue as well as cutting down redundant expenses. Tirico shopper flow solution is a powerful & popular tool used by retailers and retail researchers for understanding in-store and out-store shopper behavior, in addition to knows more about the affluence of customers.
Tirico is a web app developp on Atome, using TensorFlow for human detection and python for analytics!

• Installation
• Features
• Team
• Contact
• Licences

Dans le fichier d'installation :

git pull https://github.com/VincentBernet/Tirico-ShopCameraAnalitics/

Ou bien si l'application est déjà installé sur l'ordinateur :

git pull origin master

Pour ensuite installer tous les composants nécessaires à l'application :

cd Tirico-ShopCameraAnalitics
npm init -y
npm i --save-dev electron
npm install electron -g
npm install axios -S
npm install keytar
npm install jwt-decode

Pour lancer l'application :

npm start

Pour le python (pas nécessaire actuellement, les graphes sont en locales) : Install ANACONDA, puis éxécuter sur le prompt d'anaconda les commandes suivantes :

conda install pandas                          (pour réaliser les graphes)
conda install plotly                         (pour réaliser les graphes)
conda install -c plotly plotly-orca          (pour save en png les graphes)

On this application we implemetended multiples features such as :

• Login/Register/Logout to acces at your account and your own shops's analytics.
• Multiple analytics such as heatmap, client flow etc .

This whole application is made by 8 students during their cursus in software Engenering at EFREI PARIS :

• Don't hesitate to contact us on Github or on Linkedin :

• MIT license
• Copyright 2021 © Tirico's Team.