Table of Contents

1. Welcome to to GreenR
2. How to use our app
3. Features

1. Welcome to GreenR

Introduction about Greenr

Problem statement: How can we develop a tool to help individuals track and reduce their overall carbon footprint through their daily commute, consumption, and communication in a consolidated fashion?

Solution: Green e-commerce application that will revolutionize shopping habits. Users will be incentivised to make purchases that are more sustainable. Users are also able to track their carbon footprint (CO2 emission per kwh based on country) of home appliances

2. How to use our app

Pre-requisites

• Familiarity with environment variables, MySQL, Docker container local hosting or cloud hosting

Basic Set-Up

1. Clone the repository
2. Go to terminal, and type npm install
3. Create and host a MySQL server
4. Create and name a database in MySQL
5. Create a new connection (with password) to the server and allow access to the newly created database
6. Follow the next steps below
7. Once you have hosted the containers, created a MySQL server and configured the environment variables. Go to terminal and run nodemon

Deploy and configure AI Model Containers

Due to the nature of AI libraries, we have utilized docker containers to make deployment and usage of our AI models easier. Our docker containers are configured to run uwsgi-nginx-flask at startup. For more information on uwsgi-nginx-flask please refer here.

As we come to an end to the project, we will be retiring our hosted container services due to cost concern. To test our application you may have to deploy the docker containers in the cloud. AWS, GCP and Azure provides container hosting services, and they offer free credits for new users. Do utilize them.

The following are our docker images that we have published on docker hub categorized by members :

Nuzul

• Aspect Extraction Model Service -> When pullling this docker image, please specify the :fixed tag

• Aspect Based Sentiment Analysis Model Service

• Recommendation System Service -> When pulling this docker image, you will have to configure the environment variable to allow it to access the MySQL server

Change Environment Variable & and other credentials

After deploying the containers above, you will have to create a .env file in the project folder and add the following environment variables

when assigning the values in the variables, do not include the double quotations.

db_host = "ip address of mysql server"
db_database = "database name"
db_username =  "user connection name"
db_password = "password for the user connection"

//nuzul's services
aspect_extract_service_address = "address to the aspect extraction service"
absa_service_address = "address to the absa service"
recommendation_system_address = "address to the recommendation service"

//saran's service
register_audio_api = 'address to train the two audio files'
predict_speaker_api = 'address to predict the audio'
//for google auth
CLIENT_ID = 'YOUR CLIENT ID'
CLEINT_SECRET = 'YOUR CLIENT SECRET'
REDIRECT_URI = 'https://developers.google.com/oauthplayground'
REFRESH_TOKEN = 'YOUR REFRESH TOKEN'

we might also be retiring our paypal sandbox and google auth credentials. Please generate new ones and update them in index.js for paypal credentials and admin.js and main.js for google API credentials

3. Features

Nuzul

AI Features

Aspect Extraction (AE)

• Utilized a pretrained BERT model on NER task to extract aspects in IOB format.

• Fine-tuned the model on SemEval 2016 laptop reviews dataset

• Then used RPA to scrape reviews from consumer affairs, annotated them on LabelStudio and fine-tuned the model further for fridge domain. The model returns an IOB formatted output.

Aspect Based Sentiment Analysis (ABSA)

• Utilized ABSA library that takes a text and aspects as input and classify sentiments based in relation to its aspect.

AE + ABSA = Better review consolidation

• With both models combined, we will have a better consolidation of all reviews about a fridge.

Recommendation system

• Utilized Sentence-Transformers as a content based recomnmendation system.
• Generate embeddings for a description of a currently viewed fridge and compares it to all the other embeddings of different product description. Utilized cosine similarity to compute similarity score

Other notable features

• User Authentication with passport
• Add to cart | Orders | Buy
• Buyer -> Seller Onboarding
• Product listing | Sell
• Payment with Paypal
• Intellitick Chatbot

Saran

AI Features

Speaker Recongition

Used Siamese neural network for speaker recongition which is a type of deep learning architecture used to compare two inputs and predict if they are similar or not. I have trained the model using 50 audio files whhich are sampled to 16000hz and 1 second from 5 different speaker.

• At registration page, users have to give two audio files which will be used train the model. One of the audio files will be saved into the database.

The python code used for training the model.

• At the TWO-FA page, user have to upload an audio file to verify. Using the model to predict whether the audio is similar to the audio file saved in the databse under the user.

The python code used to predict two different audio:.

Other notable features

• User register their audio file
• Users have to verify their email before login.
• Users can reset their password if they forgot by using a link send to their email.
• Admin only can access the admin pages when she is logged in; other users can access the admin pages (404 page will be returned if anyone manually enter the admin pages links)
• Admin delete specific user (notification will be sent to the removed user).
• Admin able to email specific user.
• Admin approving the sellers after requested by the buyer to be seller (notification will be send to the user)
• Admin can choose not to approve the user to be a seller ( notification send to the user).
• All the email is done by using google api

Nigel

-Image classification model to detect if its a Samsung fridge or LG fridge, -My dataset was gotten from online images, carousell and the Samsung and LG websites -pre trained using vgg16 -model and api functions are dockerised and put on azure container, this is the main link - http://greenr-fridge-brand.ceadcbb5fbbwbbc5.southeastasia.azurecontainer.io -the functions /filepredict is to predict using file upload and /camerapredict is to predict using camera -webcam feature whereby users can turn it on to detect if it is a samsung fridge or lg fridge -file upload feature whereby users can upload a picture of a fridge to detect if it is a samsung fridge or lg fridge -after the model detects the brand of the fridge, it redirects to the product listing page with the identified brand

Samuel

AI Feature

Image classification on LG Fridge Models

• Utilized a pretrained VGG16 model
• Images are scraped from the offical LG website
• User will upload a picture of a LG fridge and it will be able to detect the fridge model through the function /filepredict