ksharma67 / easyway

On Demand Home Services offers you all the home services that you may need, such as maintenance, cleaning, personal services, etc..

EasyWay

View Project Description as PDF | Download Project Description as Word Document

File Structure

Our application is structured as follows:

File Name	Description
ProjectDocs	This folder contains all the Project Deliverable files featured on the project Wiki page.
TeamPhotos	This folder contains the photos of each team member that are used on the project Wiki page.
client	This folder contains the codes for Front End.
db	This folder contains the database schema and dummy data.
server	This folder contains the codes for Back End server.

Technology Stack:

• Framework : Angular
• Backend : GoLang, Flask
• Database : MySQL (GORM Library)
• Version Control: Git
• Code Editor : Visual Studio Code

Project Board:

Link : https://github.com/users/ksharma67/projects/2

API Documentation:

Link : https://documenter.getpostman.com/view/23815648/2s93eSZant

Running Backend Server:

• Clone the repository

git clone https://github.com/ksharma67/EasyWay.git

• Make sure you have mysql installed and correctly set up.
• Create a new database in MySQL using:

mysql -u root -p

Enter mysql password, then run:

create database easyWay;

• Goto config.go and update your mysql password

cd server/config/
code config.go

• Now navigate to server folder and run go server:

cd ./server/
go run main.go

Ignore any errors as it will check for required datatables (show the error), then automatically creates the datatables.

Running Backend Server - Object Detection Server:

• Clone the repository

git clone https://github.com/ksharma67/EasyWay.git

• Install Python from https://www.python.org/downloads/
• Install Pip from https://pip.pypa.io/en/stable/installation/
• Navigate to server folder and run go server:

cd ./server/

• Install the required libraries

# TensorFlow CPU
pip install -r requirements.txt

# TensorFlow GPU
pip install -r requirements-gpu.txt

• For Linux: Let's download official yolov3 weights pretrained on COCO dataset.

# Downloading yolov3 weights
wget https://pjreddie.com/media/files/yolov3.weights -O weights/yolov3.weights

• Load the weights using load_weights.py script. This will convert the yolov3 weights into TensorFlow .ckpt model files!

# Loading yolov3 weights
python load_weights.py

• Starting the Flask Server

python app.py

Running Frontend Server:

Link : https://easywayapp.netlify.app

• Clone the repository

git clone https://github.com/ksharma67/EasyWay.git

• Install NodeJS LTS version from https://nodejs.org/en/ for your Operating System.
• Navigate to client folder and install required libraries:

cd ./client/
npm install

• In case of any error run audit and install once more:

npm audit fix --force && npm install

• Run the Angular Server:

npm start