DEV Community: Dhruva Shaw

Assessing the Feasibility of Creating a WALL-E-Like Robot For Human Assistance

Dhruva Shaw — Wed, 04 Oct 2023 04:04:15 +0000

Abstract:

The concept of creating robots for human assistance has gained significant attention in recent years. This abstract explores the feasibility of developing a robot akin to WALL-E, the beloved fictional character from Pixar's animated film, as an assistant for human tasks and interactions. Drawing inspiration from WALL-E's characteristics, including mobility, communication abilities, and emotional resonance, this study examines the technological, practical, and ethical considerations of bringing such a robot to reality. The feasibility assessment encompasses several dimensions. First, the technological aspect delves into the current state of robotics, AI (Artificial Intelligence) and mobility systems, evaluating their potential to replicate WALL-E's functionality. This involves analyzing advancements in AI cognition, sensor miniaturization, battery efficiency, and mobility mechanisms. Second, practical considerations encompass the robot's physical design, its capacity to navigate real-world environments, and its ability to perform a range of tasks – from basic chores to complex interactions requiring emotional comprehension. Furthermore, the study addresses the emotional connection between humans and robots, a pivotal aspect demonstrated by WALL-E's ability to evoke empathy.

Ethical implications surrounding user data privacy, emotional manipulation, and the potential for over-reliance on such robots are explored.

Introduction

Initially in the movie the WALL-E robot was the last robot on the earth and was meant to clean the waste left by the mankind. Here in this assessment we look on why WALL-E like robot, how to design it’s brain, implementation of electrical and electronics parts, challenges to be faced while designing a robot like WALL-E. We also would need to study and understand as to how these kinds of robots would help mankind provide the betterment in life or day-today activities.

Designing of brain for the robot

The designing of brain is one of the most important aspect of the robot to function as per the expectations set.

At a glance it may look like that brain for the robot would be plain simple AI model like of ChatGPT or Bard, but in practical it would be different as laid down below:

Brain of the robot can be divided into 6 different basic required parts:

Manual mode switcher:
This is the most important thing required in robot, there should be a manual mode switcher as not everything can be left to AI or automation in general. In this one program of the robot to work via voice commands or program it via some external signals which can be activated via the push buttons.
AI Model :
This is the main part of the brain where all the automation would take place. One can develop its own AI Model or can use API’s of Google or OpenAI. The model should be more of like the conversational bot mixed with AI assistant. Heavy applications of NLP and ANN is required. Simply designing and training the AI model or using api’s won’t be enough.

We will need to develop the logic on where on which commands or actions the electronic parts would be used or on where basic speaking using the speaker is required. Designing of the logic to channelize the commands or actions of robot to specific electronic hardware is also equally important.

Pathfinding Algorithm: After the competition of AI model and its logic the major work required would be to implement the loads of various different pathfinding algorithms for the smooth traversal around its user or acting according to instructions given by the user.

For the efficient path traversal through the unknown area one needs to use a mix of different path traversal algorithm for example using of A* with BFS via a modified heuristic for the traversal of unknown maze/path. One can use fuzzy logic for the pathfinding but it also needs to be kept in mind about the time complexity of the function.

Area mapper/eyes of the brain:
It is better to implement this part of the robot brain in a separate area, isolating its memory computation. It would also be better to allocate more computation power to this area or use the concept of threading with parallel computation. This would provide basic sensory input to the robot for it to function. Options to use and integrate camera can also be done. With camera we can map an area using GPS also. This needs to be computed as fast as possible so that the other work(s) of the robot can be done without any lag.
Sensor fusion component:
This also needs to be implemented in a separate microprocessor/microcontroller as sensor fusion requires a lot of matrix multiplication and requires to store a continuous range of data. As matrix multiplication itself is power hungry thus it is better to implement it separately in a separate pipeline. Eg: one can fuse Ultrasonic sensor with the TOF sensor using Kalman Filter or also one can fuse IMU data with the Magnetometer using the Kalman Filter or other filtering techniques.
End effector controller:
This can be implemented in various ways, either inbuilt or separated (code or PCB). Proper implementation of PID control should be there as one can either.

Implementation of electrical and electronics parts

One should be able to perfectly picturize for which application or where the robot needs to be used or deployed, thus the sizing of the robot would matter accordingly. In respect to the sizing of robot the miniaturization or expansion of the PCB would be required. For example for a 5 kg robot we can use (15cm x 15cm) with 4 layer PCB.

This would be sufficient to house and mount the basic components like microcontroller, signal transmitter, electrolytic capacitors for the voltage and power regulators. It is very essential to keep in mind while designing the PCB that, there should be no 90° turns in the copper traces in the PCB. Though there won’t be any signal going beyond the 10GHz, still use of best practice is recommended in the possible areas while designing the PCB to optimize the power consumption.

Challenges to be faced while designing and deploying this kind of robot in the respective areas

Some major challenges to be faced while developing/designing the WALL-E kind are listed below but not limited to the list below:

Selection of adequate power source for the robot, whether it is LiPo or Lead Acid, depending on the size of robot and power consumption.
Gaining the trust of the users to use the robots.
Gaining the trust of the user to believe in them about the data privacy regarding the robot.
Providing the training to staff in order to use the robot and repair it when require.
Unexpected shutting down of the api might disrupt the services if it is used.

How these kinds of robots will help people to lead a better life than ever?

These WALL-E types of robots are well suited for the companion to the old-aged/paralyzed or lonely people.

These kind of robots can easily cure depression and loneliness as it provides companionship.

It would aid people to do their daily chores and also act as a reminder assistant to take their medicines properly on time, etc.

References

[1]	W. contributors, "WALL-E," 26 August 2023. [Online]. Available: https://en.wikipedia.org/w/index.php?title=WALL-E&oldid=1172280439.
[2]	N. McLaughlin, "Maze Solving Algorithms for Micro Mouse," SlideShow, [Online]. Available: https://slideplayer.com/slide/8569125/.
[3]	james-ralph8555, DrexelMicromouse2020.
[4]	M. A. Dharmasiri, "Micromouse from scratch
[5]	W. contributors, "Micromouse," Wikipedia, The Free Encyclopedia., [Online]. Available: https://en.wikipedia.org/w/index.php?title=Micromouse&oldid=1158883816.
[6]	GreatScott!, "From Idea to Schematic to PCB - How to do it easily! - YouTube," [Online]. Available: https://www.youtube.com/watch?v=35YuILUlfGs.

Note

Soon a research paper this would be published in the IEEE journal when I finish completing on this project of my own WALL-E :)

fluxpoint.py

Dhruva Shaw — Wed, 15 Jun 2022 05:00:20 +0000

fluxpoint.py

============

A modern, easy to use, feature-rich, and async ready API wrapper for
Fluxpoint written in Python.

Key Features

Modern Pythonic API using async and await.
Proper rate limit handling.
Optimised in both speed and memory.

Installing

Python 3.8 or higher is required

To install the library, you can just run the following command:

# Linux/macOS
python3 -m pip install -U fluxpoint.py

# Windows
py -3 -m pip install -U fluxpoint.py

To speedup the api wrapper you should run the following command:

# Linux/macOS
python3 -m pip install -U "fluxpoint.py[speed]"

# Windows
py -3 -m pip install -U fluxpoint.py[speed]

To install the development version, do the following:

$ git clone https://github.com/Dhruvacube/fluxpoint.py
$ cd fluxpoint.py
$ python3 -m pip install -U .[speed]

Quick Example

from fluxpoint import FluxpointClient
import asyncio
import sys

# setting up the fluxpoint client handler
a = FluxpointClient(api_token="get api token from https://fluxpoint.dev/api/access")

# setting up the windows loop policy according to the operating system
if sys.platform.startswith('win32') or sys.platform.startswith('cygwin'):
    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())

# getting the image url of AZURLANE image
print(asyncio.run(a.azurlane()))

You can find more examples in the examples
directory.

Creatrix-Net / fluxpoint.py

Async python wrapper for the fluxpoint api

fluxpoint.py

A modern, easy to use, feature-rich, and async ready API wrapper for Fluxpoint written in Python.

Key Features

Modern Pythonic API using async and await.
Proper rate limit handling.
Optimised in both speed and memory.

Installing

Python 3.8 or higher is required

To install the library, you can just run the following command:

# Linux/macOS
python3 -m pip install -U fluxpoint.py

# Windows
py -3 -m pip install -U fluxpoint.py

To speedup the api wrapper you should run the following command:

# Linux/macOS
python3 -m pip install -U "fluxpoint.py[speed]"

# Windows
py -3 -m pip install -U fluxpoint.py[speed]

To install the development version, do the following:

$ git clone https://github.com/Dhruvacube/fluxpoint.py
$ cd fluxpoint.py
$ python3 -m pip install -U .[speed]

Quick Example

from fluxpoint import FluxpointClient
import asyncio
import sys
# setting up the fluxpoint client handler
a = FluxpointClient(api_token="get api token

…

View on GitHub

Konohagakure Search

Dhruva Shaw — Sun, 09 Jan 2022 08:54:38 +0000

Overview of My Submission

Konohagakure Search is a Google Like Search Engine. It is built on the technology of django, dedicated mongo db server, python, scrapy, spacy, and nltk. Whenever a search query is given it first search the database if its present then it searches up the internet then saves the data and then it presents to you in a pretty way :)

Overview of the project

An efficient Search Engine with the following features:
It has distributed crawlers to crawl the private/air-gapped networks (data sources in these networks might include websites, files, databases) and works behind sections of networks secured by firewalls

It uses AI/ML/NLP/BDA for better search (queries and results) It abides by the secure coding practices (and SANS Top 25 web vulnerability mitigation techniques.)

It is a type of a search engine which takes keyword/expression as an input and crawls the web (internal network or internet) to get all the relevant information. The application dosen't have any vulnerabilities, it complies with OWASP Top 10 Outcome. This application scrape data, match it with the query and give out relevant/related information.

Note - Search as robust as possible (eg, it can correct misspelt query, suggest similar search terms, etc) be creative in your approach. Result obtained from search engine should displays the relevant matches as per search query/keyword along with the time taken by search engine to fetch that result.

Submission Category:

Choose Your Own Adventure

Link to Code

Dhruvacube / search-engine

Google Like Search Engine

Konohagakure Search

Overview of the project:

An efficient Search Engine with the following features
It has distributed crawlers to crawl the private/air-gapped networks (data sources in these networks might include websites, files, databases) and works behind sections of networks secured by firewalls

It uses AI/ML/NLP/BDA for better search (queries and results) It abides by the secure coding practices (and SANS Top 25 web vulnerability mitigation techniques.) 

It is a type of a search engine which takes keyword/expression as an input and crawls the web (internal network or internet) to get all the relevant information. The application dosen't have any vulnerabilities, it complies with OWASP Top 10 Outcome. This application scrape data, match it with the query and give out relevant/related information. 

Note - Search as robust as possible (eg, it can correct misspelt query, suggest similar search terms, etc) be creative in your approach. Result obtained from search engine should

…

View on GitHub

Packages that were required in making this project

A YouTube Video Explaining all

How to do a secure login in django

Dhruva Shaw — Mon, 27 Dec 2021 14:11:14 +0000

Secure Login Challenge

https://github.com/Sainya-Ranakshetram-Submission/secure-login

This project addresses all the web vulnerabilities and implements login system in a secure way

Web vulnerabilities addressed

Cross Site Forgery Request
Clickjacking
SQL/NoSQL/LDAP/XML Injection
XSS Attack
Response Manipulation
Sensitive Information Disclosure
Authentication Bypass
Parameter Pollution & Mass Assignment
Credentials Over Unencrypted Channel
Missing Brute-Force Protection
User Enumeration
Throttling Requests
Remote Code Execution

Hosting Guide

1. Download the code

First install git in the system, then type the following command in command prompt

git clone https://github.com/Sainya-Rakshatam-Submission/secure-login.git
cd secure-login

2. Setup the Virtual Environment

Install python-3.9 in the system, then run the following command in the console

pip install virtualenv
virtualenv env
env/scripts/activate
pip install -r requirements.txt

Now rename example.env to .env and now see this video on how to setup the .env file.

3. Setup the database

If you are in local environment then the project will automatically use the sqlite unless speficied the database url in the .env file.
Following DATABASE URL's are supported Click Here
And then install its respective database connector module from pypi.
If you are in LOCAL environment then no need to install the database connector module since it will be using sqlite :)
Click here for the video explanation

4. Migrate the sql queries to the database

Now in console run the following command

python manage.py migrate

5. Create a superuser for the site

To create a superuser for the site run the following commands line by line in the sole

python manage.py createsuperuser

after running the command provide the necessary details it asks

6. Compress the static files

To compress the static files then run the following command in the console

python manage.py collectcompress

7. Edit the CORS and ALLOWED_HOST header

Make sure to edit the CORS and ALLOWED_HOST header, otherwise you won't be able to access the site from the desired attched domain. Click here to goto the CORS and ALLOWED_HOST header

8. Edit the THROTTLING REQUESTS bumber

Make sure to edit the AXES_FAILURE_LIMIT confiiguration, this is the max number of failed login attempts, Defaults to 5. Click here to goto the THROTTLING REQUESTS configuration

9. Now run the project

For the windows users, run the following command

python manage.py runserver

and for the Linux and Mac users, run the following command

gunicorn securelogin.asgi:application -k securelogin.workers.DynamicUvicornWorker --timeout 500

Kamboom! The site is up on http://127.0.0.1:8000 in local environment, now the credentials that you have given while creating the superuser using the createsuperuser command.

Youtube Video Explaining all

Github Repo

Sainya-Ranakshetram-Submission / secure-login

Secure Login Challenge

This project addresses all the web vulnerabilities and implements login system in a secure way

Web vulnerabilities addressed

Cross Site Forgery Request
Clickjacking
SQL/NoSQL/LDAP/XML Injection
XSS Attack
Response Manipulation
Sensitive Information Disclosure
Authentication Bypass
Parameter Pollution & Mass Assignment
Credentials Over Unencrypted Channel
Missing Brute-Force Protection
User Enumeration
Throttling Requests
Remote Code Execution

Hosting Guide

1. Download the code

First install git in the system, then type the following command in command prompt

git clone https://github.com/Sainya-Rakshatam-Submission/secure-login.git
cd secure-login

2. Setup the Virtual Environment

Install python-3.9 in the system, then run the following command in the console

pip install virtualenv
virtualenv env
env/scripts/activate
pip install -r requirements.txt

Now rename example.env to .env and now see this video on how to setup the .env file.

3. Setup the database

If you are in local environment then the project will automatically use the sqlite unless speficied the database url in…

View on GitHub

Google Like Search Engine

Dhruva Shaw — Mon, 27 Dec 2021 13:58:48 +0000

Konohagakure Search

https://github.com/Sainya-Ranakshetram-Submission/search-engine

We were asked to do the following:

Develop an efficient Search Engine with the following features it should have distributed crawlers to crawl the private/air-gapped networks (data sources in these networks might include websites, files, databases) and must work behind sections of networks secured by firewalls

It should use AI/ML/NLP/BDA for better search (queries and results) It should abide by the secure coding practices (
 and SANS Top 25 web vulnerability mitigation techniques.) feel free to improvise your solution and be creative with your approach Goal

Have a search engine which takes keyword/expression as an input and crawls the web (internal network or internet) to get all the relevant information. The application shouldn't have any vulnerabilities, make sure it complies with OWASP Top 10 Outcome Write a code which will scrape data, match it with the query and give out relevant/related information. Note - Make search as robust as possible (eg, it can correct misspelt query, suggest similar search terms, etc) be creative in your approach. Result obtained from search engine should display all the relevant matches as per search query/keyword along with the time taken by search engine to fetch that result There is no constraint on programming language.

To Submit: - A Readme having steps to install and run the application - Entire code repo - Implement your solution/model in Dockers only. - A video of the working search engine

Features

Corrected Spelling suggestions
Auto Suggested
3 different types of crawler
Distributed crawlers
A site submit form
Blazingly fast And so on...

Vulnerabilities the application that it address

It address the following SANS Top 25 Most Dangerous Software Errors and OWASP Top 10 Vulnerabilities

Injection
Broken Authentication
Sensitive Data Exposure
XML External Entities
Broken Access Control
Security Misconfiguration
Cross-Site Scripting
Insecure Deserialization
Using Components with Known Vulnerabilities
Insufficient Logging and Monitoring
Improper Restriction of Operations within the Bounds of a Memory Buffer
Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
Improper Input Validation
Information Exposure
Out-of-bounds Read
Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')
Use After Free
Integer Overflow or Wraparound
Cross-Site Request Forgery (CSRF)
Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')
Out-of-bounds Write
Improper Authentication
NULL Pointer Dereference
Incorrect Permission Assignment for Critical Resource
Unrestricted Upload of File with Dangerous Type
Improper Restriction of XML External Entity Reference
Improper Control of Generation of Code ('Code Injection')
Use of Hard-coded Credentials
Uncontrolled Resource Consumption
Missing Release of Resource after Effective Lifetime
Untrusted Search Path
Deserialization of Untrusted Data
Improper Privilege Management
Improper Certificate Validation

Building Docker Image

Just run

docker build .

Also check this out
If you wish you can do teh necessary image tagging.

After building the image install the docker image.

Hosting Guide (without the docker)

To run Konohagakure Search you need python3.9, latest version of golang,
postgres, rabbitmq and redis

See their installation instruction and download it properly.

After downloading the above mentioned softwares, now run the following commands in console after opening the terminal:

1. Clone the repository

Clone the repository using git

git clone https://github.com/Sainya-Ranakshetram-Submission/search-engine.git

2. Install the virtual environment

pip install --upgrade virtualenv
cd search-engine
virtualenv env
env/scripts/activate

3. Install the dependencies

pip install --upgrade -r requirements.min.txt

python -m spacy download en_core_web_md
python -m nltk.downloader stopwords
python -m nltk.downloader words

go install -v github.com/projectdiscovery/subfinder/v2/cmd/subfinder@latest

4. Setup the environment variables

Rename the example.env to .env and setup the environment variables according to your choice.

5. Create a database

Now open pgadmin and create a database named search_engine. After creating the database reassign the DATABASE_URL value acordingly in .env file.
Note please read this also

6. Start Rabitmq and Redis Instance

Read their docs regarding how to start them. redis rabbitmq

7. Migrate the data

python manage.py migrate

And to migrate the 10 Lakh dataset of the website for the crawler to crawl, do

python manage.py migrate_default_to_be_crawl_data

I have also given some crawled datasets for the reference, you can see it here data_backup

8. Compress the static files

Now run the following command in the console:

python manage.py collectcompress

9. Create a superuser for the site

python manage.py createsuperuser

It asks for some necessary information, give it then it will create a superuser for the site.

10. Running the celery worker and beat

Now run this command in the terminal

python manage.py add_celery_tasks_in_panel

Now, open two different terminals
And run these commands respectively :-

celery -A search_engine worker --loglevel=INFO

celery -A search_engine beat -l INFO --scheduler django_celery_beat.schedulers:DatabaseScheduler

11. Run the application

Before running the application, make sure that you have the redis up and running :)

For windows, mac-os, linux

Without IP address bound

    uvicorn search_engine.asgi:application --reload --lifespan off

IP address bound

     uvicorn search_engine.asgi:application --reload --lifespan off --host 0.0.0.0

If you are on Linux OS then you can run this command also instead of the above one:

    gunicorn search_engine.asgi:application -k search_engine.workers.DynamicUvicornWorker --timeout 500

Python custom commands reference

add_celery_tasks_in_panel : Add the celery tasks to the django panel
crawl_already_crawled : Scraps already scrapped/crawled sites in database
crawl_to_be_crawled : Scraps newly entered sites in database || The sites that needs to be crawled ||
migrate_default_to_be_crawl_data : Enters BASE data of the websites that needs to be crawled, its about 10 Lakh sites

Distributed Crawlers

For the distributed web crawlers refer to the following scrapy documentation link

Running crawler manually from command line

There are 3 different ways in order to achieve this

1. crawl_already_crawled

This is custom django management command and it starts crawling the already crawled and stored sites and then updates it

python manage.py crawl_already_crawled

2. crawl_to_be_crawled

This is custom django management command and it starts crawling the site which were entered using either the migrate_default_to_be_crawl_data custom command or it was entered using submit_site/ endpoint

python manage.py crawl_to_be_crawled

3. Scrapy Command Line Crawler

This is a scrapy project that crawls the site using the command line
Here in example.com replace it with the site you want to crawl (without http or https`)

scrapy crawl konohagakure_to_be_crawled_command_line -a allowed_domains=example.com

Youtube Video Explaining all

Github Repo

Sainya-Ranakshetram-Submission / search-engine

Google Like Search Engine

Konohagakure Search

We were asked to do the following:

Develop an efficient Search Engine with the following features it should have distributed crawlers to crawl the private/air-gapped networks (data sources in these networks might include websites, files, databases) and must work behind sections of networks secured by firewalls
It should use AI/ML/NLP/BDA for better search (queries and results) It should abide by the secure coding practices (
 and SANS Top 25 web vulnerability mitigation techniques.) feel free to improvise your solution and be creative with your approach Goal

Have a search engine which takes keyword/expression as an input and crawls the web (internal network or internet) to get all the relevant information. The application shouldn't have any vulnerabilities, make sure it complies with OWASP Top 10 Outcome Write a code which will scrape data, match it with the query and give out relevant/related information. Note - Make search as robust

…

View on GitHub

Quest DB - My experience at QuestDB during Hacktoberfest

Dhruva Shaw — Mon, 27 Dec 2021 13:31:15 +0000

Where did I find about "QuestDB" ?

Well it was mid of #Hacktoberfest when I got to know about the Quest DB from this site https://hacktoberfest-swag.com

How was my experience ?

My experience was was amazing! Actually in their swag referral program I was entitled for the Level - 1 Swag but after few days of hacktoberfest, I got an email from QuestDB community manager, stating due to some problems with manufacturer they will be sending me a T-shirt withinh the DEC 2021!!!! XD .
:)

I got my swag in the month of Nov 2021 only, but I didn't had the time to write a blog about this

What is QuestDB

QuestDB is the fastest open source time series database

and ofc they are amazing, even for my college projects we now use the Quest DB! Yup its that amazing!
You could know more about them from these links:

site
questdb/questdb
docs
community page #Here about info their swag referral program is mentioned
slack
stackoverflow
twitter

My Quest DB Swag

questdb / questdb

An open source time-series database for fast ingest and SQL queries

QuestDB

QuestDB is an open-source time-series database for high throughput ingestion and fast SQL queries with operational simplicity.

QuestDB is well-suited for financial market data, IoT sensor data, ad-tech and real-time dashboards. It shines for datasets with high cardinality and is a drop-in replacement for InfluxDB via support for the InfluxDB Line Protocol.

QuestDB implements ANSI SQL with native time-series SQL extensions. These SQL extensions make it simple to filter and downsample data or correlate data from multiple sources using relational and time-series joins.

We achieve high performance by adopting a column-oriented storage model, parallelized vector execution, SIMD instructions, and low-latency techniques The entire codebase is built from the ground up in Java, C++ and Rust with no dependencies and zero garbage collection.

QuestDB supports schema-agnostic…

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ELECTRICITY BILLING MANAGEMENT SYSTEM

Dhruva Shaw — Tue, 22 Dec 2020 14:25:11 +0000

THIS WAS OUR SCHOOL PROJECT
The link is to project is Click Here for the link to project

Content

Sl. No.	Topic
1	Modules Used (In-Built & User created modules)
2	Objective, Scope & Backbone of the Project
3	Table Structure Used
4	Working Description
6	Bibliography

MODULES USED

Inbuilt modules

sys : The system module is used to close the interpreter programmatically using sys.exit()
mysql-connector : This module is used to perform the backend operations with the MySQL database.
os : This module is imported in the program clear the terminal screen programatically, get the current working directory and make the program Operating System independent.
json : This module is used to import data from .json files to the program.
math : From this module the ceil function is imported to roundoff the generated value for the electric bill.
smtplib : This module is imported to send the electric bills to respective customer.
email : This module is imported to work accordance with smtplib module and ease the template making of the emails.
datetime : This module is imported to get the current time.
csv : This module is imported to read and write the csv files.
hashlib : This module is imported to hash the password using the md5 hash algorithm and return the hash in a hexadecimal number
time : From this module sleep function is imported to suspend execution of the calling thread for the given number of seconds
cProfile : This module is to provide a deterministic profiling of the python program
re : From the regular expression module compile function imported and is used to compile a regular expression pattern into a regular expression object
pyinstaller : This is used to convert the python file to exe file.

Custom (user made) Modules

adminBillGen : This contains function for the Admin Homepage.
clearscreen : This contains the function for the clearscreen based on the operating system.
customerView : This contains the function for the billing the view bill and this is accessible to customer only.
billEmail : This contains the function for the emailing the bill to respective customer.
billGen : This contains the function for to generate the bill for the corresponding month.
login : This function to logged into the user in correct department.
logout : This contains the function to logout the user.

Objective, Scope & Backbone of the Project

Our project entitled " Electricity Billing System" aim is to generate electricity bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard. The aim of our project is to develop a system that is meant to partially computerize the work performed in the Electricity Board like generating monthly electricity bill, record of consuming unit of energy, store record of the customer and previous unpaid record. We used Python 3.8 as front end and MySql-marriaDB engine as back end for developing our project. Our project is independent of any OS and can run on any platform.

The overall project report is divided into further sub-parts which includes developing of the model system with scope for enhancement depending on the functionality of the organisation. The codes written were developed by the team jointly, tested with dummy data and found to be successful worth implementation with suitable modifications for further implementation.

Backbone of the Project :

This Project was completed using the methods which can be used in connecting MySQL and Python together. Python was chosen due to its simple structure, robustness and high capability in creating definitions. MySQL as a backend tool was chosen as a combination to give Python the meaning of flexibility and adaptability due it's simple table management system while primarily used for storing the data related to the billing system and customer details.

We as a team hope that the humble effort taken from our side would be able to create a significant change for the betterment of the lives of the people who would be using the system with adaptations as required.

Table Structure

Table Name	Customer
Field Name	Type
id	integer
meterno	integer
consumerno	biginteger
consumername	varchar()
load_con	varchar()
unit_consumed	integer
month	varchar()
year	integer
email	varchar()
address	text
amountgen	decimal

Table	User
Field Name	Type
id	int
username	varchar()
password	varchar()
branch	varchar()
dept_no	int
useradmin_id	varchar()

Table Name	DEPT
Field Name	Type
id	int
dept_no	int
deptname	text

Table Name	Login
Field Name	Type
id	int
userid	varchar()
branch	text
session_in	datetime
session_out	datetime
dept_no	int

WORKING DESCRIPTION

FILES GENERATED** :**

config.json, customer_details.csv, employee_details.csv, admin_message.txt, billEmailnotAdmin_message.txt, billGennotAdmin_message.txt, create_msg.txt, createdBill.txt, custdetails.txt, welcome_message.txt

An exe file is generated for distribution.

DIRECTORY STRUCTURE :

The master folder contains a folder named 'files'.

Then the files folder contains the following 5 folder.

config_file', 'customerBillfolder', 'details', 'export', 'messages'

The program has been designed with following modes of operation:

Admin
Bill Generation
Bill Delivery
Customer Bill View

Admin : It part has the privileges of a super user. It has the power to create, delete and edit, etc.

Bill Generation: This module has been designed to generate electricity bills based on the inputs of meter reading.

Bill Delivery: This module will email the bill to respective customers address and thus bring the concept of a reduce paper and reduce carbon footprint making the environment greener and sustainable.

Customer View Bill: This portal is only for use by the consumer to view the bill for the current month.

This is all in one program where electricity department can enter the data through the MySQL database, where a consumer can view its own bill just by using this program.

Features:

It has an Admin Panel which the super user can access to enter the data of the consumer to database given by the electricity meter department in form of a csv file. It has a login system where the password are hashed using md5 hash algorithm then the hash are converted to the hexadecimal units. The super user can also add the details of a new operator or delete its details.

It also a configurable json file, and configure the contents of a program.

This program is also Operating System independent.

It also has a portal for the Bill Generation and Bill Delivery Department where the respective operator can generate the electricity bill with help of only one command and also deliver the bill to customers using their emails.

It has also portals for the customers where a consumer can enter its consumer no and get the bill details for the current month.

Cons :

A constant Internet Connection is required.

The database of the consumer has to be constantly updated by the admin every month through csv files.

And in the customers or consumer portal in case of any emergency or help requiring situation one cannot contact any authority as would be required to resolve the problem in hand.

Biblio graphy

My very first HACKTOBER FEST participation!!!!

Dhruva Shaw — Sat, 17 Oct 2020 05:04:16 +0000

Hackotber had a really amazing experince for me thought I have been using github for a very long time and knew about the pull request but never made a one. So this was the very first time that I have made the very first PR of my life.

DEV Community: Dhruva Shaw

Assessing the Feasibility of Creating a WALL-E-Like Robot For Human Assistance

Abstract:

Introduction

Designing of brain for the robot

Implementation of electrical and electronics parts

Challenges to be faced while designing and deploying this kind of robot in the respective areas

How these kinds of robots will help people to lead a better life than ever?

References

Note

fluxpoint.py

fluxpoint.py

Key Features

Installing

Quick Example

Links

Creatrix-Net / fluxpoint.py

Async python wrapper for the fluxpoint api

fluxpoint.py

Key Features

Installing

Quick Example

Konohagakure Search

Overview of My Submission

Overview of the project

Submission Category:

Link to Code

Dhruvacube / search-engine

Google Like Search Engine

Konohagakure Search

Packages that were required in making this project

A YouTube Video Explaining all

How to do a secure login in django

Secure Login Challenge

Hosting Guide

1. Download the code

2. Setup the Virtual Environment

3. Setup the database

4. Migrate the sql queries to the database

5. Create a superuser for the site

6. Compress the static files

7. Edit the CORS and ALLOWED_HOST header

8. Edit the THROTTLING REQUESTS bumber

9. Now run the project

Youtube Video Explaining all

Github Repo

Sainya-Ranakshetram-Submission / secure-login

Secure Login Challenge

Secure Login Challenge

Hosting Guide

1. Download the code

2. Setup the Virtual Environment

3. Setup the database

Google Like Search Engine

Konohagakure Search

Features

Vulnerabilities the application that it address

Building Docker Image

Hosting Guide (without the docker)

1. Clone the repository

2. Install the virtual environment

3. Install the dependencies

4. Setup the environment variables

5. Create a database

6. Start Rabitmq and Redis Instance

7. Migrate the data

8. Compress the static files

9. Create a superuser for the site

10. Running the celery worker and beat

11. Run the application

Python custom commands reference

Distributed Crawlers

Running crawler manually from command line

1. crawl_already_crawled

2. crawl_to_be_crawled

3. Scrapy Command Line Crawler

Youtube Video Explaining all

Github Repo

Sainya-Ranakshetram-Submission / search-engine

Google Like Search Engine