AI DRIVEN PETITION MANAGEMENT FOR

TRANSPARENCY AND ACCOUNTABILITY

A PROJECT REPORT

Submitted by

DONA VERGINIYA N (8115U21CS036)

HEMAVATHI G (8115U21CS047)

KEERTHANA K (8115U21CS059)

KHAVIYA K (8115U21CS062)

in partial fulfilment of the award of the degree of

BACHELOR OF ENGINEERING

IN

COMPUTER SCIENCE AND ENGINEERING

K.RAMAKRISHNAN COLLEGE OF
ENGINEERING (AUTONOMOUS),
SAMAYAPURAM, TRICHY – 621 112.

MAY 2025
 K.RAMAKRISHNAN COLLEGE OF ENGINEERING
(AUTONOMOUS)

BONAFIDE CERTIFICATE

Certified that this project report “AI DRIVEN PETITION

MANAGEMENT FOR TRANSPARENCY AND ACCOUNTABILITY ” is
the Bonafide work of “ DONA VERGINIYA N, HEMAVATHI G,
KEERTHANA K, KHAVIYA K” who carried out the project work
under my supervision.

SIGNATURE SIGNATURE
Dr. T.M. NITHYA, M.E., Ph.D., Dr. L. AMUDHA, M.E., Ph.D.,

Associate Professor Assistant Professor

HEAD OF THE DEPARTMENT SUPERVISOR

Computer Science & Engineering Computer Science & Engineering

K. Ramakrishnan College of Engineering K. Ramakrishnan College of Engineering

(Autonomous) (Autonomous)

Samayapuram, Samayapuram,

Trichy – 621112 Trichy – 621112

Submitted for the Project Viva-Voice Examination held on ………………….

INTERNAL EXAMINER EXTERNAL EXAMINER

i
 ACKNOWLEDGEMENT

We thank the almighty GOD, without whom it would not have been
possible for us to complete our project.

We wish to address our profound gratitude to Dr.K.

RAMAKRISHNAN, Chairman, K. Ramakrishnan College of Engineering
(Autonomous), who encouraged and gave us all help throughout the course.

We express our hearty gratitude and thanks to our honourable and

grateful Executive Director Dr.S.KUPPUSAMY, B.Sc., MBA., Ph.D.,
K.Ramakrishnan College of Engineering (Autonomous).

We are glad to thank our principal Dr.D.SRINIVASAN, M.E., Ph.D.,

FIE., MIIW., MISTE., MISAE., C.Engg, for giving us permission to carry
out this project.

We wish to convey our sincere thanks to Dr.T.M.NITHYA, M.E.,

Ph.D., Head of the Department, Computer Science and Engineering for
giving us constants encouragement and advice throughout the course.

We are grateful to Dr. L. AMUDHA, M.E., Ph.D., Assistant Professor

in the Department of Computer Science and Engineering, K.Ramakrishnan
College of Engineering (Autonomous), for her guidance and valuable
suggestions during the course of study.

Finally, we sincerely acknowledge in no less term for all our faculty

members, colleagues, our parents and friends for their co-operation and help
at various stages in this project work

ii
 DECLARATION

I hereby declare that the work entitled “AI DRIVEN PETITION

MANAGEMENT FOR TRANSPARENCY AND ACCOUNTABILITY”
is submitted in partial fulfilment of the requirement for the reward of the degree
in B.E., Anna University, Chennai, is a record of our own work carried out by
me during the academic year 2024-2025 under the supervision and guidance of
Dr. L.AMUDHA, M.E., Ph.D., Assistant Professor, Department of Computer
Science and Engineering, K.Ramakrishnan College of Engineering
(Autonomous). The extent and source of information are derived from the
existing literature and have been indicated through the dissertation at the
appropriate places. The matter embodied in this work is original and has not
been submitted for the award of any degree or diploma, either in this or any
other University.

DONA VERGINIYA N
(8115U21CS036)

I certify that the declaration made by above candidate is true.

Dr. L. AMUDHA, M.E., Ph.D.,

Assistant Professor/CSE

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 DECLARATION

I hereby declare that the work entitled “AI DRIVEN PETITION

MANAGEMENT FOR TRANSPARENCY AND ACCOUNTABILITY”
is submitted in partial fulfilment of the requirement for the reward of the degree
in B.E., Anna University, Chennai, is a record of our own work carried out by
me during the academic year 2024-2025 under the supervision and guidance of
Dr. L.AMUDHA, M.E., Ph.D., Assistant Professor, Department of Computer
Science and Engineering, K.Ramakrishnan College of Engineering
(Autonomous). The extent and source of information are derived from the
existing literature and have been indicated through the dissertation at the
appropriate places. The matter embodied in this work is original and has not
been submitted for the award of any degree or diploma, either in this or any
other University.

HEMAVATHI G
(8115U21CS047)

I certify that the declaration made by above candidate is true.

Dr. L. AMUDHA, M.E., Ph.D.,

Assistant Professor/CSE

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 DECLARATION

I hereby declare that the work entitled “AI DRIVEN PETITION

MANAGEMENT FOR TRANSPARENCY AND ACCOUNTABILITY”
is submitted in partial fulfilment of the requirement for the reward of the degree
in B.E., Anna University, Chennai, is a record of our own work carried out by
me during the academic year 2024-2025 under the supervision and guidance of
Dr. L.AMUDHA, M.E., Ph.D., Assistant Professor, Department of Computer
Science and Engineering, K.Ramakrishnan College of Engineering
(Autonomous). The extent and source of information are derived from the
existing literature and have been indicated through the dissertation at the
appropriate places. The matter embodied in this work is original and has not
been submitted for the award of any degree or diploma, either in this or any
other University.

KEERTHANA K
(8115U21CS059)

I certify that the declaration made by above candidate is true.

Dr. L. AMUDHA, M.E., Ph.D.,

Assistant Professor/CSE

PAGE \* MERGEFORMAT 2
 DECLARATION

I hereby declare that the work entitled “AI DRIVEN PETITION

MANAGEMENT FOR TRANSPARENCY AND ACCOUNTABILITY”
is submitted in partial fulfilment of the requirement for the reward of the degree
in B.E., Anna University, Chennai, is a record of our own work carried out by
me during the academic year 2024-2025 under the supervision and guidance of
Dr. L.AMUDHA, M.E., Ph.D., Assistant Professor, Department of Computer
Science and Engineering, K.Ramakrishnan College of Engineering
(Autonomous). The extent and source of information are derived from the
existing literature and have been indicated through the dissertation at the
appropriate places. The matter embodied in this work is original and has not
been submitted for the award of any degree or diploma, either in this or any
other University.

KHAVIYA K
(8115U21CS062)

I certify that the declaration made by above candidate is true.

Dr. L. AMUDHA, M.E., Ph.D.,

Assistant Professor/CSE

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 ABSTRACT
The Petition Management System is an AI-driven digital governance
platform designed to streamline the creation, submission, and
management of public petitions while fostering transparency,
accountability, and efficiency in grievance redressal. The system allows
users to submit petitions effortlessly, categorizing them based on relevant
government departments and issue types, ensuring they reach the
appropriate authorities for timely resolution. To maintain authenticity and
security, user authentication is enforced through verified details such as
name, address, and location, which also helps in geo-mapping issues for
better policy formulation. The platform incorporates role-based access
controls, allowing different user types, such as citizens, administrators,
and government officials, to interact with petitions based on predefined
permissions, ensuring a customized and secure experience. A key feature
of the system is its AI-powered Natural Language Processing (NLP)
algorithms, which analyze petition content to detect recurring patterns,
cluster similar concerns, and prioritize urgent issues based on sentiment
analysis. This intelligent automation minimizes redundancy, reduces
manual effort, and enables authorities to focus on the most pressing
matters. Furthermore, petitions are ranked dynamically based on public
engagement metrics such as votes, shares, and comments, along with
sentiment scores that measure public urgency and emotion. By elevating
critical petitions for faster resolution, the system ensures that citizens
voices are not only heard but also acted upon efficiently. Additionally,
real-time tracking allows users to monitor petition progress, fostering
greater trust and participation in governance.

TABLE OF CONTENT
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CHAPTER NO. TITLE
PAGE NO.
ABSTRACT
10
LIST OF FIGURES 11
LIST OF ABBREVITIONS 12

1. INTRODUCTION
1.1 INTRODUCTION 13
1.2 Digital Governance 14
1.3 Natural Language Processing (NLP) 13
1.4 User Authentication 17
1.5 Sentiment Analysis 18
1.6 CHALLENGES OF AI 19
1.7 Challenges in Petition Systems 21
1.8 Civic Engagement 23
1.9 AI-Driven Decision Making 24
1.10 AIM & OBJECTIVE 26
2. LITERATURE SURVEY 29
3. SYSTEM ANALYSIS 39
3.1 EXISTING SYSTEM 39
3.1.1 Limitations OF EXISTING SYSTEM 39
3.2 PROPOSED SYSTEM 41
3.2.1 Advantages of PROPOSED SYSTEM 43
4. SYSTEM REQUIREMENTS 46
4.1 HARDWARE REQUIREMENTS 46
4.2 SOFTWARE REQUIREMENTS 46
4.3 SOFTWARE DESCRPTION 46
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5. SYSTEM DESIGN 49
5.1 USE CASE DIAGRAM 50
5.2 CLASS DIAGRAM 52
5.3 SEQUENCE DIAGRAM 55
5.4 ACTIVITY DIAGRAM 57
6. SYSTEM TESTING 60
6.1 UNIT TESTING 60
6.2 USABILITY TESTING 61
6.3 INTEGRATION TESTING 61
6.4 REGRESSION TESTING
62
7. CONCLUSION AND FUTURE
ENHANCEMENTS 63

8. APPENDICES
69

9. REFERENCES 86

10. CERTIFICATES
88

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 LIST OF FIGURES

FIGURE NO FIGURE NAME PAGE NO

FIGURE 5.1 USE CASE DIAGRAM 51

FIGURE 5.2 CLASS DIAGRAM 54

FIGURE 5.3 SEQUENCE DIAGRAM 56

FIGURE 5.4 ACTIVITY DIAGRAM 58

- ARCHITECTURE DIAGRAM 48

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 LIST OF ABBREVITIONS

ABBREVITIONS FULL FORM

AI ARTIFICIAL INTELLIGENCE

NLP NATURAL LANGUAGE

UI USER INTERFACE

SQL STRUCTURED QUERY LANGUAGE

ORM OBJECT RELATIONAL MAPPER

API APPLICATION PROGRAMMING

INTERFACE

AADHAR UNIQUE IDENTIFICATION NUMBER

(INDIA)

RTI RIGHT TO INFORMATION

BERT BIDIRECTIONAL ENCODER

REPRESENTATION FROM TRANSFORMS

HTML HYPER TEXT MARKUP LANGUAGE

CSS CASCADING STYLE SHEETS

DB DATABASE

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 CHAPTER 1
INTRODUCTION

In the digital age, governance systems are increasingly moving towards

digital platforms to enhance efficiency, transparency, and accountability.
One critical area that can greatly benefit from such a transformation is the
management of public petitions. Traditionally, petition management has
been a manual and cumbersome process, often plagued by delays,
redundancy, and lack of clear prioritization. This results in critical issues
being overlooked, and public trust in the system can erode.

To address these challenges, the Petition Management System (PMS)

proposes a solution that streamlines the entire petition process by
leveraging modern technology. By enabling users to create, view, and
manage petitions through a user-friendly digital interface, the system
makes governance more accessible to the public. Petitions are categorized
according to departments and issues, ensuring better organization and
improved handling by the relevant authorities.

The system ensures accurate user authentication by gathering essential

details such as name, address, and location, which helps in validating
petitioners and ensuring accountability. With a tailored access control
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mechanism, public users can have varying levels of access, ensuring
sensitive information is shared selectively.

One of the standout features of this system is the use of Natural Language
Processing (NLP). NLP algorithms analyze petition content to identify
recurring themes, prioritize urgent issues, and group similar petitions
together. This allows decision-makers to focus on the most critical
matters and take timely action. Furthermore, the system ranks petitions
based on user engagement and sentiment, allowing petitions with high
public interest or urgency to receive immediate attention.

In essence, the Petition Management System aims to revolutionize how

public petitions are managed, promoting transparency, reducing
redundancy, and fostering democratic participation. This innovative
approach not only streamlines the process but also ensures that every
voice is heard and that the system is accountable to the people it serves.

1. The Need for Efficient Petition Management

 Public Participation in Governance: Petitions allow citizens to

voice concerns, suggest reforms, and demand action, making them
a vital part of democratic processes and public governance.

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 Challenges of Traditional Petition Systems:
o Inefficiency: Traditional systems often involve manual
processes, causing delays in petition handling.
o Lack of Transparency: Petitioners are often left in the dark
about the status or progress of their petitions.
o Difficulty in Tracking Progress: Without a streamlined
digital system, it becomes challenging to track the progress
and resolution of submitted petitions.
 Limited Accessibility and Reach: Traditional petition systems
may not be easily accessible to all citizens, especially in remote
areas or for those with limited digital literacy.
 Bureaucratic Delays: Petitions are frequently delayed due to
bureaucratic bottlenecks, leading to slow decision-making and
frustration among the public.
 Lack of Categorization and Prioritization: With manual
processes, there is often no clear method to categorize or prioritize
petitions, which can result in important issues being overlooked.

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  Increased Redundancy: Without an intelligent system to filter and
categorize petitions, similar petitions are often submitted multiple
times, leading to redundant work for authorities.
 Need for Real-Time Updates and Feedback: Citizens often do
not receive timely updates or feedback on the progress of their
petitions, diminishing trust in the system.
 Enhancing Efficiency with Technology: There is a clear need for
an intelligent, automated system that utilizes technology to
organize, prioritize, and streamline the petition process, ensuring
that public concerns are addressed in a timely manner.

 Promoting Accountability: An efficient petition management

system ensures that authorities are held accountable for responding
to the public’s grievances, ensuring transparency in the decision-
making process.
 Facilitating Engagement and Civic Participation: An optimized
system would encourage more citizens to participate in governance
by making it easier for them to submit and track petitions, fostering
greater democratic engagement.

2. Introducing the Petition Management System (PMS)

 The Petition Management System (PMS) is an innovative solution

developed to overcome the inefficiencies and limitations of
traditional petition systems. With the rapid advancement of digital
technologies, PMS takes a digital-first approach to streamline the
entire petition process, offering both petitioners and government
bodies a more efficient, transparent, and user-friendly platform.
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  By enabling users to easily create, view, and manage petitions,
PMS eliminates the complexities and delays typically associated
with paper-based or manually managed petition systems. The
system provides a seamless interface that makes it easy for the
public to submit their concerns and track their petitions' progress.
Whether for requesting government action, addressing grievances,
or suggesting reforms, PMS simplifies the process and encourages
greater participation.
 The design of PMS ensures that all stakeholders have access to a
well-organized and transparent platform. Petitions are
automatically categorized according to relevant issues or
departments, reducing the possibility of mismanagement and
ensuring petitions are directed to the appropriate authorities.
 This system not only improves efficiency but also enhances
accountability by providing real-time updates and making the
entire petition process visible to the public.
 Ultimately, the Petition Management System aims to revolutionize
how public petitions are handled, making governance more
accessible, transparent, and responsive to the needs of the people.

3. Ensuring Accurate User Authentication

Accurate user authentication is a critical component of the Petition

Management System (PMS), ensuring the integrity and authenticity of the
petitions submitted. The system enforces a stringent authentication
process to validate the identity of petitioners before their submissions are
accepted. By requesting key details such as name, address, and location,
the system verifies the petitioner’s identity, ensuring that petitions come
from legitimate sources. This not only helps in confirming that the
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individual behind the petition is real but also aids in maintaining
accountability throughout the petition process.

The authentication process helps reduce the risk of fraudulent or

misleading submissions, safeguarding the credibility of the petition
system. By ensuring that each petition is tied to a verified individual, the
system prevents impersonation or submission of false claims, maintaining
the overall trustworthiness of the platform. Furthermore, accurate user
authentication helps in efficiently routing petitions to the appropriate
departments or authorities, ensuring that the right parties handle each
petition. This level of validation ensures that petitioners are accountable
for their submissions, encouraging responsible participation in the
petition process.

4. Leveraging Natural Language Processing (NLP) for Enhanced

Decision Making

A standout feature of the Petition Management System (PMS) is its use

of Natural Language Processing (NLP) algorithms to process and analyze
the content of petitions. NLP, a branch of artificial intelligence, enables
the system to understand and interpret human language, allowing it to sift
through large volumes of petition data and extract valuable insights.

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By analyzing the text of each petition, the system can identify recurring
issues and common themes, such as requests for policy changes or
concerns about specific problems.

This automated categorization of petitions ensures that each one is routed

to the appropriate department or authority, reducing the time spent on
manual sorting and enabling more efficient management.

In addition to categorization, NLP also allows the system to prioritize

petitions based on their urgency and relevance. The algorithms can detect
the tone of the content—whether the petition expresses urgency,
frustration, or other strong emotions—and determine which issues need to
be addressed immediately. By analyzing sentiment and user engagement,
the system can identify petitions that have generated significant public
interest, ensuring that high-priority concerns are not overlooked.

Moreover, NLP enables the system to group similar petitions together,

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creating a more streamlined decision-making process. Instead of
processing each petition individually, related petitions can be handled
collectively, making it easier for decision-makers to address widespread
issues more effectively.

Overall, NLP enhances the Petition Management System's ability to make

data-driven decisions, ensuring that public concerns are addressed
promptly and appropriately. By leveraging advanced language processing
capabilities, the system can respond to the needs of the public more
efficiently, ultimately improving the transparency and accountability of
the petition management process.

5. Promoting Transparency and Accountability

At the core of the Petition Management System (PMS) lies the

commitment to transparency and accountability. Transparency ensures
that the petition process is open, visible, and accessible to all
stakeholders.

The system empowers petitioners by providing real-time updates on the

status of their petitions, allowing them to track their concerns from
submission to resolution. Whether a petition is under review, awaiting
action, or resolved, users are kept informed at every stage.

This level of visibility fosters a sense of trust between the public and
government bodies, as citizens can easily see how their petitions are
being handled. The real-time tracking feature not only allows users to
stay updated but also encourages decision-makers to act with greater

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urgency and efficiency, knowing that the process is being closely
monitored.

By promoting transparency, the PMS helps ensure that the petition

process is fair and free from any bias or hidden agendas. Citizens can
hold authorities accountable for their actions, making sure that every
petition receives appropriate attention and resolution in a timely manner.
This transparent approach not only enhances public trust but also
contributes to building a more responsive, accountable, and democratic
system of governance.

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 CHAPTER 2
LITERATURE SURVEY

1. Title: Destiny of Legal Petition: Accept or Reject ?

Author: Yashaswi Sharma; Abhinav Mishra; Nivedita Shukla; Deepika

Sharma

Year: 2021Presently, the Indian judiciary system grapples with around

30 million pending cases, translating to approximately 73,000 cases per
judge. This backlog introduces biases into the system, compounded by
the undue influence of political figures and affluent individuals,
jeopardizing the judiciary's independence. Additionally, issues such as
inconsistent data across different courts, the absence of fixed case
completion deadlines, rising crime rates, and a surge in Public Interest
Litigation (PIL) further exacerbate the problem. Despite the
establishment of fast-track courts, tribunals, and other specialized entities
to expedite case resolution, the incorporation of technology has become
indispensable. A rapid and automated decision support system is urgently
required to address backlogs and ensure data coherence within the
judiciary. This study focuses on predicting the initial decision for
petitions, specifically determining whether a petition should proceed to
further legal proceedings based on the statements framed by legal
practitioners. The work employs the ILDC dataset, comprising
approximately 7500 data points. Various supervised machine learning
algorithms, including logistic regression, support vector machine, random

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forest, decision tree, and naive Bayes, were utilized to train the model.
The results revealed that the random forest algorithm yielded the highest
accuracy of 79% with a 10% test size

2. Title: A Fraud Detection System Using Machine Learning

Author: Dhananjay Kalbande; Pulin Prabhu; Anisha Gharat; Tania

Rajabally

Year: 2021

With financial services being an integral part

of modern society and online transactions becoming
more prevalent, the complexity and volume of
financial activities have also increased, leading to a
rise in fraudulent activities. The authors identify the
need for an automated fraud detection system to
combat this alarming increase in frauds. The sheer
volume of transactions in online payments and
credit card systems makes it virtually impossible to
manually detect fraudulent activities with high
speed and accuracy. The paper proposes a Machine
Learning-based solution designed to address this
issue efficiently and accurately. The model aims to
distinguish between "fraudulent" and "genuine"
transactions in real-time, providing a scalable
solution that can be employed across various sectors
involved in finance. The proposed system is not
only robust but also cost-effective, offering an
efficient method to detect and prevent fraudulent
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 activities. By analyzing various factors during a
transaction, it helps assess whether the transaction
could potentially be harmful. The system’s ability
to predict and detect fraud before it occurs can
significantly reduce the number of unfortunate
incidents related to financial fraud. The integration
of Machine Learning techniques ensures the
system's ability to adapt to evolving fraud patterns,
providing an effective and dynamic tool for fraud
detection in today’s fast-paced digital economy.

3. Title: A Privacy-Preserving Fine-Grained Electronic Petition System

for Health and Political Petitions

Author: Xiangman Li; Yunke Liu; Jianbing Ni; Yuanyuan

Year: 2021

E-petitions have become a crucial platform

for collecting public opinions and requesting
actions from authorities regarding health and
political issues. However, this widespread use of e-
petitions poses significant privacy concerns for the
signers involved, as their personal data and opinions
may be exposed. The authors address this issue by
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proposing a privacy-preserving, fine-grained e-
petition system designed to protect signer privacy
while ensuring the credibility of the petition
process. The proposed system utilizes attribute-
based identity verification for signers, enabling the
definition of specific attribute policies. These
policies ensure that only individuals whose
attributes align with the requirements of the petition
can participate. The fine-grained approach is a
significant improvement over traditional e-petitions,
as it proactively selects signers to enhance the
trustworthiness of the petition results. To protect
signer identities, the system employs a non-
interactive zero-knowledge proof mechanism,
allowing signers to remain anonymous during the
petition process. Furthermore, the system
incorporates a traceability feature to detect and
prevent double-signing, where an anonymous signer
may submit multiple signatures without detection.
The authors demonstrate that the proposed system
successfully achieves key security properties such
as anonymity, unforgeability, and traceability.
Additionally, they show that the system is efficient
enough for implementation on mobile devices,
making it a practical solution for secure, privacy-
preserving e-petitions in health and political
domains.

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4. Title: “Enhancing Online Petitions in Somalia for Civic Engagement
and Policy-Making Using Blockchain”

Author: Mohamed Ibrahim Abdullahi; Abdirahman Abdiaziz Geesey;

Abdirahman Ali Maow; Mohamed Hasan Omar; Bashir Abdinur Ahmed

Year: 2021

In pursuit of a stable and inclusive

government in Somalia, public engagement in civic
activities and policy-making is of paramount
importance. This paper presents a blockchain-based
solution designed specifically to enhance online
petitions, enabling Somali citizens to participate
more effectively in political and social advocacy.
Traditional systems of petitioning are often
hindered by challenges such as restricted access,
safety concerns, and vulnerabilities stemming from
centralized control—including risks of data
manipulation, lack of transparency, and fraudulent
activities. Recognizing the need for a secure and
trustworthy system, the authors propose a
decentralized online petition platform powered by
blockchain technology.

Unlike existing globalized platforms, this solution is tailored to Somalia’s

unique socio-political landscape. It leverages blockchain to guarantee
data immutability, transparency, and the verifiability of petition
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signatures. Through smart contracts and decentralized storage, the system
ensures that no authority can tamper with the petitions or participant data,
thus promoting credibility and public trust. A user-friendly interface is
also integrated into the platform, allowing citizens to effortlessly create,
sign, and track petitions. Real-time monitoring, interactive visualizations,
and community engagement tools are embedded to further encourage
grassroots participation.

The authors emphasize how the petition data can serve as a valuable
resource for policymakers, enabling decisions that genuinely reflect the
public’s voice. By supporting both civic empowerment and political
accountability, this system demonstrates how blockchain can reshape
governance mechanisms in developing regions. Ultimately, the proposed
model represents a significant step toward fostering democratic
participation, enhancing transparency, and building an inclusive digital
public sphere in Somalia.

not revealed even to system administrators. A notable feature of this

solution is its ability to trace and prevent double-signing—where the
same anonymous user might try to sign a petition multiple times—which
helps maintain fairness and reliability. The authors provide a thorough
analysis of the system’s security, confirming that it meets essential
requirements such as anonymity, unforgeability, and traceability.
Moreover, experimental evaluations indicate that the system is efficient
and feasible for deployment on resource-constrained devices like
smartphones, making it a practical solution for large-scale, secure, and
trustworthy e-petitions in real-world scenarios.

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5. Title: Machine Learning Approaches for Grievance Redressal and
Public Petition Management

Author: Ayesha Rahman, David J. Wilson

Year: 2021

Description: This paper by Ayesha Rahman and David J. Wilson,

published in 2021, delves into the integration of machine learning (ML)
and Natural Language Processing (NLP) techniques in the grievance
redressal and public petition management domains. The authors propose
an automated framework designed to efficiently handle and prioritize
public grievances, thereby improving the responsiveness and
transparency of the system. By leveraging NLP techniques, the system
can process large volumes of text data from petitions, classify them into
relevant categories, and identify patterns or recurring issues that require
attention.

The paper emphasizes the importance of machine learning in automating

the classification of petitions, detecting urgent issues, and prioritizing
them based on factors like sentiment and public engagement. This
approach significantly reduces the time spent by human operators in
manual processing, thus enabling quicker resolution of public grievances.
The authors also explore how sentiment analysis can be used to
understand the urgency of a petition and rank issues accordingly,
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ensuring that critical concerns are addressed promptly. Ultimately, this
research highlights the potential of AI-driven systems to enhance the
efficiency, transparency, and accountability of grievance redressal
processes in public governance.

6.Title: Artificial Intelligence for Public Grievance Management: A

Comprehensive Review

Author: Ravi Kumar, Aditi Mehra

Year: 2020

Description:
The paper Artificial Intelligence for Public Grievance Management: A
Comprehensive Review by Ravi Kumar and Aditi Mehra (2020) presents
a detailed analysis of how artificial intelligence (AI), especially Natural
Language Processing (NLP), can be utilized to improve the management
of public grievances. The study explores the potential of AI to automate
the grievance redressal process, ensuring timely and efficient handling of
public petitions. By employing NLP techniques, the system can analyze,
categorize, and prioritize grievances based on their content, enabling
quicker identification of urgent concerns. This application enhances
transparency by allowing citizens to track the progress of their complaints
and receive automated updates, thus fostering trust in the system. The
authors emphasize the role of AI in reducing manual intervention,
minimizing human error, and ensuring accountability within grievance
management frameworks. Additionally, the paper discusses how AI can
optimize decision-making, facilitate public participation, and contribute

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to the overall democratization of grievance management. Ultimately, the
paper provides a comprehensive review of the advancements in AI for
public grievance management, highlighting its potential to improve
citizen engagement and governance processes.

7.Title: The Role of NLP in Petition Analysis and Public Sentiment

Detection

Author: Kevin S. Lee, Jessica Wang

Year: 2023

Description:
The paper, The Role of NLP in Petition Analysis and Public Sentiment
Detection, explores the application of Natural Language Processing
(NLP) algorithms in analyzing public petitions to detect sentiment,
prioritize issues, and categorize petition content effectively. The authors
argue that the growing volume of digital petitions demands an automated,
data-driven approach to ensure timely and accurate responses. The study
demonstrates how NLP techniques, such as sentiment analysis and topic
modeling, can be leveraged to identify recurring themes, emotional tones,
and public concerns in petitions.

The research highlights the potential of sentiment analysis to evaluate the

urgency and emotional intensity of petitions, allowing authorities to
prioritize urgent matters and address critical issues promptly.
Furthermore, the paper emphasizes the importance of categorizing

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petitions into predefined topics to streamline the decision-making
process, ensuring that the right department or official handles the relevant
issues. By automating these tasks, the system can reduce administrative
burden and enhance public participation in governance, promoting
transparency and accountability. This study lays the groundwork for
future implementations of NLP-driven tools in petition management
systems, providing a foundation for improving civic engagement.

8.Title: AI-Driven Digital Governance: Improving Petition Systems

through Automation
Author: Carla Rodriguez, Peter C. Scott

Year: 2024

Description: The paper explores the revolutionary role of Artificial

Intelligence (AI) in transforming digital governance, particularly focusing
on the automation of petition systems. By leveraging Natural Language
Processing (NLP), the study highlights how AI can automate the
processing and management of petitions, significantly reducing manual
effort and human intervention. The authors discuss how NLP algorithms
can be used to analyze large volumes of petition data, automatically
categorize petitions, extract relevant details, and even identify key issues
or trends. This automation improves the efficiency of decision-making by
providing quicker insights and enabling the prioritization of critical issues
based on sentiment analysis and urgency. Furthermore, the paper
addresses the transparency and accountability that AI-driven systems can
provide in petition management, allowing stakeholders and the public to
track the progress of petitions in real-time. The integration of AI into
petition systems not only optimizes the workflow but also ensures a more

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responsive and inclusive approach to public participation. Ultimately, this
paper advocates for the adoption of AI technologies to streamline
governance processes, making them more efficient, transparent, and
democratic.

9.Title: Public Petition Systems: Leveraging AI for Effective

Management and Resolution Tracking

Author: Jason T. Hall, Sophia Collins

Year: 2020

Description:
In their 2020 study, Public Petition Systems: Leveraging AI for Effective
Management and Resolution Tracking, Jason T. Hall and Sophia Collins
investigate the role of Artificial Intelligence (AI) in modernizing and
enhancing the efficiency of public petition systems. The paper
specifically focuses on the application of Natural Language Processing
(NLP) techniques to automate the tracking, categorization, and resolution
of public grievances. By using NLP, the system is able to analyze large
volumes of petitions, extract key information, and identify recurring
issues or themes. This automation significantly reduces the manual effort
required to process petitions, ensuring that urgent concerns are flagged
and addressed promptly. Furthermore, the authors highlight how AI can
prioritize petitions based on sentiment analysis and public engagement,

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ensuring that issues with the greatest public interest are handled first. The
study also explores the transparency and accountability provided by AI-
driven petition systems, allowing users to track the resolution status of
their grievances in real-time. Overall, the paper demonstrates the
transformative potential of AI and NLP in improving public petition
systems, ensuring better governance and responsiveness to citizen
concerns.

10.Title: Enhancing Public Participation through AI-Driven Petition

Management Platforms

Author: Elena Rivera, Mark F. Thompson

Year: 2024

Description: This paper explores the significant role of AI-powered

petition management platforms in enhancing public participation by
ensuring greater transparency, accountability, and efficiency. The authors
investigate how Natural Language Processing (NLP) techniques are
utilized to analyze and categorize public petitions and grievances,
automating the process of managing large volumes of data. By employing
NLP, the system can identify key issues, assess the sentiment behind
petitions, and prioritize urgent concerns based on relevance and public
interest. This allows for the swift resolution of critical issues, ensuring
that important matters are addressed in a timely manner.

Furthermore, the paper highlights how AI can provide real-time tracking

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of petition statuses, fostering transparency in the decision-making
process. Citizens can track the progress of their petitions, thus ensuring
accountability in how grievances are handled. The study emphasizes the
role of AI in improving democratic participation by offering citizens an
accessible platform to voice concerns and engage with governance
systems. The paper also discusses challenges and future implications of
integrating AI into public petition management systems to foster deeper
civic engagement and strengthen democratic processes.

CHAPTER 3
SYSTEM ANALYSIS

Problem Definition:

In contemporary governance systems, managing public petitions

effectively is a significant challenge. Traditional methods of petition
management are often slow, inefficient, and prone to errors, leading to
delayed responses, lack of transparency, and public dissatisfaction.
Additionally, with the growing volume of petitions, manually
categorizing, prioritizing, and addressing them becomes increasingly
difficult. As a result, citizens' grievances may be ignored or lost in the
system, hindering accountability and the responsiveness of decision-
makers.

Furthermore, there is a lack of intelligent mechanisms to analyze and

categorize petition content, making it challenging for authorities to
identify recurring issues or urgent concerns. The absence of proper user
engagement tracking and sentiment analysis leads to inefficient
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prioritization, often causing critical issues to be overlooked.

This project aims to address these challenges by implementing an AI-

driven petition management system that utilizes Natural Language
Processing (NLP) to automate the categorization, analysis, and
prioritization of petitions. By improving transparency, reducing
redundancy, and promoting democratic participation, this system seeks to
create an efficient, transparent, and accountable platform for grievance
redressal in digital governance.

Existing System:

Currently, most petition management systems are manual or use basic

digital platforms that allow users to submit and track petitions. These
existing systems primarily rely on form-based submissions, where
citizens can provide their concerns, which are then forwarded to relevant
departments or officials for action. While these systems offer basic
functionalities like petition creation, submission tracking, and user
authentication, they are limited in terms of automation, efficiency, and
real-time data analysis.

In many traditional systems, petitions are categorized manually, which

can lead to delays and errors in prioritization. Public users typically have
access to view petitions but with limited ability to track real-time
progress or assess the urgency of issues raised. While some systems
incorporate simple filtering options, they do not leverage advanced
technologies like Natural Language Processing (NLP) for content
analysis or sentiment detection.
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Existing petition platforms also struggle to analyze large volumes of
petition data effectively. Issues like redundancy, misclassification, and
delayed responses are common, as the systems lack the capability to
prioritize petitions based on urgency or public sentiment. Furthermore,
engagement metrics such as petition popularity, user sentiments, or
interaction history are not factored into the decision-making process,
limiting the system's effectiveness in ensuring that critical issues are
addressed swiftly.

Moreover, transparency and accountability features in these systems are

often underdeveloped. Public users may not always have clear visibility
into the progression of their petitions, leading to a lack of trust in the
system..

As a result, these existing systems fail to maximize the potential of

modern technology in driving more efficient, transparent, and user-centric
petition management processes.

DISADVANATGE :
 Manual Categorization and Processing
 Limited Automation
 Ineffective Sentiment Analysis
 Lack of Real-Time Updates
 Limited User Engagement Features

PROPOSED SYSTEM:

The proposed system aims to revolutionize petition management by

leveraging modern technologies to streamline the entire process, from
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submission to resolution. The core objective of the system is to enhance
transparency and accountability in governance while improving the
efficiency and accuracy of petition handling. The system will allow users
to easily create, view, and manage petitions online, with features that
categorize petitions based on departments and issues. This classification
will ensure that petitions are directed to the relevant authorities or
departments for quicker action.

A key feature of the system is its robust user authentication mechanism,

which will validate users' identity using essential details such as name,
address, and location. Public users will have access to different levels of
visibility depending on their roles, ensuring selective access to petition
details and actions.

To enhance the processing of petitions, the system will integrate Natural

Language Processing (NLP) algorithms. These algorithms will analyze
the content of petitions, identifying recurring issues and categorizing
them based on urgency and importance. Petitions will be prioritized, and
similar petitions will be grouped together, optimizing decision-making
and ensuring that critical concerns are addressed promptly.

The system will also rank petitions based on user engagement and
sentiment analysis, with the most actively discussed or emotionally
charged petitions receiving quicker attention. Additionally, it will offer
real-time insights into petition statuses, ensuring transparency in the
resolution process.

Ultimately, the proposed system will reduce redundancy in the petition

process, increase public participation, and provide a more efficient and
accountable platform for grievance redressal. It will foster democratic
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participation by providing citizens with a transparent and responsive
system, ensuring that every petition is handled with the attention it
deserves.

ADVANTAGES
 Enhanced Transparency
 Efficient Petition Management
 User Authentication & Access Control
 User Authentication & Access Control
 Sentiment & Engagement-Based Ranking

CHAPTER 4
DSYSTEM REQUIREMENT

HARDWARE REQUIREMENT:-

PROCESS : INTEL® CORE™ I9-14900K 3.20 GHZ

RAM : 16 GB
HARD DISK : 1 TB

SOFTWARE REQUIREMENT:-

FRONT END - HTML,CSS

BACK END - PYTHON
FRAMEWORK - FLASK

PYTHON TECHNOLOGY:
Python is an interpreter, high-level, general-purpose
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 programming language. It supports multiple programming
paradigms, including procedural, object-oriented, and
functional programming. Python is often described as a
"batteries included" language due to its comprehensive standard
library.

PYTHON PROGRAMING LANGUAGE:

Python is a multi-paradigm programming language. Object-

oriented programming and structured
programming are fully supported, and many
of its features support functional
programming and aspect-oriented
programming including by Meta
programming and met objects (magic
methods). Many other paradigms are
supported via extensions, including design by
contract and logic programming. Python
packages with a wide range of functionality,
including:
 Easy to Learn and Use

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  Expressive Language
 Interpreted Language
 Cross-platform Language
 Free and Open Source
 Object-Oriented Language
 Extensible
 Large Standard Library
 GUI Programming Support
 Integrated

Python uses dynamic typing and a combination of

reference counting and a cycle-detecting garbage collector for
memory management. It also features dynamic name resolution
(late binding), which binds method and variable names during
program execution. Rather than having all of its functionality
built into its core, Python was designed to be highly extensible.
This compact modularity has made it particularly popular as a
means of adding programmable interfaces to existing
applications.

Van Rossum's vision of a small core language with a

large standard library and easily extensible interpreter stemmed
from his frustrations with ABC, which espoused the opposite
approach.

Python is meant to be an easily readable language. Its

formatting is visually uncluttered, and it often uses English
keywords where other languages use punctuation. Unlike many
other languages, it does not use curly brackets to delimit blocks,

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and semicolons after statements are optional. It has fewer
syntactic exceptions and special cases than C or Pascal.
Python strives for a simpler, less-cluttered syntax and
grammar while giving developers a choice in their coding
methodology. In contrast to Perl's "there is more than one way
to do it" motto, Python embraces a "there should be one and
preferably only one obvious way to do it" design philosophy.
Alex Martelli, a Fellow at the Python Software Foundation and
Python book author, writes that "To describe something as
'clever' is not considered a compliment in the Python culture."

Python's developers strive to avoid premature

optimization, and reject patches to non-critical parts of the
Python reference implementation that would offer marginal
increases in speed at the cost of clarity. When speed is
important, a Python programmer can move
time-critical functions to extension modules written in
languages such as C, or use PyPy, a just-in-time compiler.
Python is also available, which translates a Python script into C
and makes direct C-level API calls into the Python interpreter.
An important goal of Python's developers is keeping it fun to
use. This is reflected in the language's name a tribute to the
British comedy group Monty Python and in occasionally
playful approaches to tutorials and reference materials, such as
examples that refer to spam and eggs (from a famous Monty
Python sketch) instead of the standard foo and bar.

Python uses duck typing and has typed objects but

untiled variable names. Type constraints are not checked at
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compile time; rather, operations on an object may fail,
signifying that the given object is not of a suitable type.
Despite being dynamically typed, Python is strongly typed,
forbidding operations that are not well-defined rather than
silently attempting to make.

THE PYTHON PLATFORM:

The platform module in Python is used to access the
underlying platform's data, such as, hardware, operating system,
and interpreter version information. The platform module
includes tools to see the platform's hardware, operating system,
and interpreter version information where the program is
running.

There are four functions for getting information about the

current Python interpreter. python version() and
python_version_tuple() return different forms of the interpreter
version with major, minor, and patch level components.
python_compiler() reports on the compiler used to build the
interpreter. And python_build() gives a version string for the
build of the interpreter. Platform() returns string containing a
general purpose platform identifier. The function accepts two
optional Boolean arguments. If aliased is true, the names in the
return value are converted from a formal name to their more
common form.
When terse is true, returns a minimal value with some
parts dropped.
HTML :
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HTML (Hypertext Markup Language) is the core language used
to create and structure content on the web. It
forms the foundation of every website and web
application by providing a structured framework
to define text, images, links, forms, multimedia
elements, and other web content. HTML is not a
programming language but rather a markup
language that uses tags to structure and display
content in a readable format for browsers.

1.An HTML document is made up of several key components:

1. Document Structure:
An HTML document is structured with two main parts: the
<head> and <body>.

o The <!DOCTYPE html> declaration at the beginning of

an HTML document informs the browser about the
version of HTML being used.

o The <html> tag is the root element that encapsulates all

HTML content.

o The <head> section contains metadata about the

document, such as the title (<title>), links to external
resources like stylesheets and scripts, and other meta tags
for search engines or viewport settings.

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 o The <body> section contains the visible content of the
webpage, including text, images, videos, links, and
interactive elements.

2. TagsandElements:
HTML is built using a series of tags that represent different
content types or elements on the webpage. Tags are enclosed in
angle brackets and typically come in pairs (an opening tag and a
closing tag).

o For instance, <h1> is used to define a top-level heading,

and the corresponding closing tag </h1> marks the end of
that heading.

o <p> is used to define paragraphs, and <a> is used for

creating hyperlinks.

o Other tags include <img> for displaying images, <ul> for

unordered lists, and <ol> for ordered lists.

3. Attributes:
Tags in HTML can have attributes, which provide additional
information about the element or modify its behavior.
Attributes are written inside the opening tag and are usually
defined in name-value pairs.
For example:

o The <img> tag uses the src attribute to specify the image
source: <img src="image.jpg" alt="Description of the
image">.

o The <a> tag uses the href attribute to specify the URL for
the link: <a href="https://example.com">Click here</a>.
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 o Other attributes like id, class, style, and alt help define
styling, identification, and behavior for elements.

4. FormsInputs:
HTML allows the creation of forms with interactive input
elements such as text fields, checkboxes, radio buttons,
dropdown menus, and buttons.

These elements allow users to interact with a website and submit data. The
<form> tag wraps the entire form, and inside it, various input
elements such as <input>, <textarea>, <select>, and <button>
are used to gather user input.

5. Semantics:
In modern HTML, semantic tags have become a key feature,
which improve the readability and accessibility of code for both
humans and search engines. These tags describe the content
they contain, such as:

o <header> for the top section of a webpage

o <footer> for the bottom section

o <article> for a piece of content that can stand alone

o <section> for grouping related content

o <nav> for defining navigation links

These semantic tags improve SEO (Search Engine Optimization)

and make websites more accessible for screen
readers.

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6. MultimediaandEmbedding:
HTML also supports the embedding of multimedia elements
like images, audio, and video.

o The <img> tag is used for displaying images, and it

requires the src attribute to specify the image file path.

o The <audio> and <video> tags allow the inclusion of

media files directly within the HTML document. These
elements can also include attributes like controls,
autoplay, and loop to control the media playback.

7. LinksandNavigation:
HTML provides the <a> tag to create hyperlinks that allow
users to navigate between different pages or external websites.
Links can also point to sections within the same webpage using
anchor tags with specific id attributes.

8. Tables:
HTML supports the creation of tables for organizing data. The
<table> tag defines the table, <tr> is used for table rows, <th>
for table headers, and <td> for table data cells.

9. ExternalResources:
HTML documents can link to external resources, including
stylesheets (CSS), JavaScript files, and fonts.

o The <link> tag is used to link an external CSS file to an

HTML document, enhancing the visual presentation.

o The <script> tag is used to link JavaScript files for

dynamic interactions.

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Importance of HTML in Web Development
HTML is essential for the development of websites and web
applications. It acts as the skeleton of any
webpage, and without it, content would not be
displayed in a structured and readable manner.
HTML forms the foundation upon which other
web technologies like CSS (for styling) and
JavaScript (for interactivity) are built. Together,
these technologies enable developers to create
rich, interactive, and dynamic web experiences.
In summary, HTML is a fundamental technology in web
development, providing a simple yet powerful
way to structure, organize, and display content
on the internet.

Understanding HTML is crucial for anyone interested in web

design and development, as it serves as the
foundation for building modern, interactive
websites.
CSS :
CSS (Cascading Style Sheets) is a powerful stylesheet language
that defines the look and feel of a web page. It is
used in conjunction with HTML or XML to
control the layout, design, and formatting of a
website or web application. CSS separates
content (HTML) from its presentation, making
web design more efficient, manageable, and
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 flexible. By using CSS, web developers can
ensure that the content structure remains
independent of the styling, allowing for easier
updates and improved maintenance.
CSS works by applying style rules to elements on a webpage.
These rules are typically defined by selectors,
properties, and values. A CSS rule consists of:
1. Selectors: These target specific HTML elements on which the
styles will be applied. Selectors can be applied based on
element types (like <h1>, <p>), classes (e.g., .class-name), IDs
(e.g., #id-name), and more.

2. Properties: The aspects of the element that need to be styled,

such as color, font size, margin, padding, width, height, etc.

3. Values: These specify the actual styling applied to the property,

such as "red" for color or "20px" for font size.

Key Features and Uses of CSS:

1. Layout Control: CSS gives developers full control over the
layout of web pages. It defines how the elements are arranged
on the screen, whether they should be stacked vertically or
positioned side by side. CSS allows for precise control over
positioning, including options for absolute, relative, and fixed
positioning. The use of Flexbox and CSS Grid layouts allows
for responsive, adaptable, and complex grid systems.

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2. Typography: CSS allows developers to define font types, sizes,
colors, spacing, and styles for text elements on a webpage. This
feature provides flexibility to customize and standardize the
typography for better readability and visual appeal. Fonts can
be loaded from external sources like Google Fonts to ensure
consistency across devices and browsers.

3. Coloring and Backgrounds: With CSS, you can set colors for
text, backgrounds, borders, and elements using various methods
like HEX, RGB, HSL, and named colors. Additionally, CSS
enables the use of gradients, background images, and patterns
for more dynamic and visually attractive designs.

4. Responsive Design: One of the most powerful aspects of CSS is

the ability to create responsive web designs. Using media
queries, developers can define different styles for various
screen sizes and device types (such as desktops, tablets, and
smartphones). This ensures that the layout and content are
optimized for any viewing environment, enhancing the user
experience.

5. Animations and Transitions: CSS also supports animations and

transitions, which allow elements to change from one state to
another in a visually appealing manner. Animations can be
triggered by user interactions or automatically, creating smooth
transitions for elements like buttons, menus, or images. This is
particularly important for enhancing user interfaces with
interactive feedback and dynamic content.

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6. Box Model: CSS uses a box model to control how elements are
displayed and sized. The box model consists of the content area,
padding, border, and margin. These properties can be adjusted
to control the spacing and layout of elements on the page,
providing fine-grained control over how elements interact with
each other.

7. Cascading and Specificity: The term "cascading" refers to how

CSS rules are applied when there are conflicting styles. CSS
applies styles in a cascading manner, where multiple stylesheets
or rules can affect an element, but the more specific rule takes
precedence. For example, an inline style will override external
and internal styles, while a class-based style is overridden by an
ID-based style.

Benefits of Using CSS:

1. Separation of Content and Presentation: By using CSS,
developers can separate the content (HTML) from the
presentation (styles). This separation leads to cleaner code,
easier maintenance, and the ability to make changes to the
design without affecting the HTML structure. It also improves
accessibility, as the same content can be presented differently
on various platforms or devices.

2. Consistency Across Pages: With CSS, developers can apply the

same styling across multiple pages of a website by linking a
single external stylesheet. This ensures a consistent look and
feel throughout the entire site, which is crucial for branding and
user experience.

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 3. Faster Load Times: By centralizing styles in a single external
file, CSS reduces the amount of code in HTML files, leading to
faster page load times. Additionally, browsers cache CSS files,
which improves the performance of websites on subsequent
visits.

4. Customization and Flexibility: CSS offers a high degree of

customization. Developers can define custom styles for
elements based on user interactions (like hover effects) or
conditions (like print styles). CSS preprocessors like Sass or
LESS allow developers to write more efficient, maintainable,
and modular CSS.

FLASK FRAMEWORK

Flask is a lightweight, yet powerful web framework for Python that is

designed to build web applications quickly and efficiently. It is classified
as a micro-framework, meaning it provides the necessary tools and
features for web development without enforcing a strict structure or
requiring extensive dependencies. Flask is based on Werkzeug, a WSGI
(Web Server Gateway Interface) utility, and Jinja2, a templating engine
that allows dynamic content rendering in web pages. Due to its
simplicity, flexibility, and modularity, Flask is widely used for
developing web applications, APIs, and microservices.

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Key Features of Flask

1. Minimalistic and Lightweight – Flask is designed to keep the core

framework simple, providing only essential features like routing,
request handling, and templating. Developers can extend Flask’s
functionality by integrating third-party libraries as needed.
2. Built-in Development Server & Debugger – Flask comes with a
built-in development server that allows developers to test their
applications in real-time. It also includes an interactive debugger
that helps in identifying and fixing issues efficiently.
3. Routing System – Flask provides a powerful URL routing system,
allowing developers to map specific URLs to corresponding
Python functions. This makes it easier to handle user requests
dynamically.
4. Jinja2 Template Engine – Flask uses Jinja2, a fast and secure
template engine, to render dynamic content in HTML files. It
supports template inheritance, filters, and expressions, making it
easy to separate business logic from UI design.

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 5. RESTful API Development – Flask is widely used for developing
RESTful APIs, allowing communication between different
applications. It provides built-in support for handling JSON data,
request parsing, and response formatting.
6. Extensibility and Modularity – Flask supports a wide range of
extensions, such as Flask-SQLAlchemy (for database
management), Flask-Login (for authentication), Flask-WTF (for
form handling), and Flask-RESTful (for API development).
Developers can customize and extend Flask to suit project
requirements.
7. Session and Cookies Management – Flask provides built-in support
for handling user sessions and cookies, enabling the development
of secure and personalized web applications.
8. Middleware and Plug-in Support – Flask can integrate middleware
components that enhance request/response processing. It also
allows plug-in integration for additional functionalities such as
caching, authentication, and logging.
9. Database Support – Flask itself does not come with a built-in
database system, but it can be integrated with SQL and NoSQL
databases using Flask-SQLAlchemy, Flask-PyMongo, or other
database extensions.
10.Scalability and Performance – Due to its modular architecture,
Flask is highly scalable, making it an excellent choice for small-
scale applications, APIs, and microservices. It performs well and
can be deployed on cloud services like AWS, Google Cloud, and
Heroku.

Why Use Flask?

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  Ideal for beginners and experienced developers due to its simple
learning curve.
 Suitable for small to medium-sized applications and APIs.
 Offers full control over application components, unlike full-stack
frameworks such as Django.
 Easy integration with databases, front-end frameworks, and third-
party services.
 Used by companies like Netflix, Uber, Reddit, and LinkedIn for
building microservices and APIs.

Conclusion

Flask is a powerful yet minimalistic web framework that provides

essential tools for web development while maintaining flexibility. Its
extensible architecture, ease of use, and support for RESTful APIs make
it a preferred choice for developers building modern web applications,
microservices, and backend systems. Whether you are developing a
simple website, a complex web application, or an AI-powered API, Flask
offers the right balance of simplicity and functionality to meet your needs

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 CHAPTER 5
SYSTEM DESIGN

ARCHITECTURE DIAGRAM

1. User Authentication and Authorization Module

This foundational module secures the system by managing user identities,

roles, and permissions. It captures and verifies essential user details such
as name, address, email, phone number, and geographic location. Role-
based access control (RBAC) is implemented to differentiate between
public users, government officials, administrators, and moderators. Each
user is granted specific permissions:

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  Public users can create and track petitions.
 Reviewers can verify and respond to petitions.
 Admins can manage all modules and user access.

This module also includes login/logout, password management, and

integration with national ID systems or third-party authentication (e.g.,
Aadhaar, OAuth). It ensures data privacy and system integrity, laying the
groundwork for accountability and secure participation.

2. Petition Creation and Submission Module

This module provides a user-friendly interface for drafting and submitting

petitions. Users can input structured data (category, department, location,
etc.) and unstructured content (detailed description of the issue).

Key features include:

 Form validation to ensure completeness and correctness

 Attachment support for documents, photos, or videos
 Draft saving for incomplete petitions
 Multilingual support to reach a wider audience

Once submitted, the petition enters the verification workflow and is

stored in the database with a unique petition ID. This module ensures
consistent and quality input from users, forming the basis for automated
processing by subsequent modules.

3. Natural Language Processing (NLP) Module

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At the heart of the system, this module uses NLP algorithms to analyze
the textual content of petitions. Key capabilities include:

 Entity Recognition to extract keywords like names, locations, or

issue types
 Topic Modeling and Clustering to group similar petitions
 Sentiment Analysis to determine emotional tone (e.g., urgency,
frustration)
 Duplicate Detection to identify and merge similar or redundant
petitions

The insights generated by this module are passed on to categorization,

prioritization, and analytics modules, reducing human workload and
enabling intelligent automation of petition handling.

4. Petition Categorization and Routing Module

This module classifies each petition into predefined categories or issue

types (e.g., sanitation, public safety, education) using a combination of
NLP output and user-selected tags. It then routes the petition to the
relevant department, geographic office, or authority, based on:

 Topic
 Location
 Jurisdictional hierarchy

This ensures that petitions are handled by the appropriate personnel,

minimizing delay and mismanagement. It can also auto-assign priority
levels or notify specific users for rapid response when needed.

5. Petition Review and Verification Module

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Once routed, petitions are reviewed for validity, completeness, and
compliance with policy. Reviewers (typically admin or government staff)

 Approve or reject submissions

 Request additional details
 Flag duplicates or invalid content
 Add internal comments and notes

The verification process ensures that only genuine, well-documented

petitions enter the grievance workflow. Petitions that pass this stage are
forwarded to the resolution phase with a “verified” status.

6. Prioritization and Impact Ranking Module

This module uses a combination of AI algorithms, sentiment scores,

urgency metrics, and public engagement levels (e.g., likes, shares,
signatures) to rank petitions. The aim is to bring high-impact or time-
sensitive issues to the forefront.

Ranking factors include:

 Volume of public support

 Severity or emotional weight (via sentiment analysis)
 Relevance to current events
 Policy impact potential

Authorities use this ranked list to allocate resources, schedule reviews,

and respond to petitions with higher urgency, improving responsiveness
and fairness.

7. Grievance Management and Issue Resolution Module

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This is the action engine of the platform. Once petitions are verified and
prioritized, this module:

 Assigns them to responsible officers or departments

 Tracks actions and communications
 Updates petition status (e.g., Under Review, In Progress, Resolved)
 Sends automated notifications to petitioners and officials

The system supports escalation logic if responses are delayed, and logs
every step of the resolution process for audit and transparency. It may
also include a case timeline view and allow officials to upload response
documents or proof of action taken.

8. Public Interaction and Engagement Module

Designed to promote civic participation, this module allows users to:

 View public petitions

 Sign or support petitions
 Comment and discuss
 Share via social media
 Follow updates on petitions they support

It helps build momentum around pressing issues and encourages

community participation in democratic processes. This module also
supports feedback channels, polls, and surveys to gather additional
insights from citizens.

9. Dashboard and Analytics Module

The analytics module offers visual, data-driven insights for administrators

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and policymakers. Features include:

 Charts and graphs showing petition trends, resolution rates, and

department performance

USE CASE DIAGRAM

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CLASS DIAGRAM

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SEQUENCE DIAGRAM

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ACTIVITY DIAGRAM

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DATA FLOW DIAGRAM
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 CHAPTER 7
SYSTEM TESTING

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 System testing is a comprehensive process used to validate the end-to-end
functionality and performance of the AI-Driven Petition Management
System. This phase ensures that all integrated modules – including
petition handling, user authentication, NLP-based analysis, and
transparency mechanisms – operate seamlessly and meet user
expectations and project requirements. The testing process includes
functional, non-functional, security, and usability aspects to ensure a
reliable and scalable system.
1. Functional Testing

Functional testing verifies that the system performs its intended functions
accurately.

 Petition Creation & Submission:

Tested by simulating multiple user roles (citizen, admin). Petitions were

submitted with mandatory fields (name, address, location, category,
content). Verified that petitions are stored and acknowledged correctly.

 Petition Viewing & Categorization:

Ensured petitions are correctly displayed under appropriate categories and

departments. Checked if filtering and search functionalities return
relevant results.

 User Role Verification:

PAGE \* MERGEFORMAT 2
Confirmed that public users can only view petitions marked public or related to their
jurisdiction, while admins can access all data.

Users with elevated privileges (admins, moderators) were tested for additional
actions like updating statuses or flagging petitions.

2. Natural Language Processing (NLP) Module Testing

The Natural Language Processing (NLP) module is a fundamental

component of the Petition Management System, responsible for
automatically interpreting and analyzing textual petition data. This
module plays a crucial role in enhancing efficiency, reducing manual
workload, and ensuring that important issues are addressed promptly. The
goal of NLP testing is to validate the accuracy and effectiveness of
various AI-driven language analysis features integrated into the system.

Keyword Extraction

This functionality is designed to identify and highlight the main topics

and concerns in a petition. During testing, a wide range of petitions with
varying formats, writing styles, and tones (formal, informal, regional
variations) were submitted. The system was able to correctly extract
relevant keywords, such as issue types (e.g., “road damage,” “electricity
outage”), departments involved, and geographical locations. These
keywords were then used to help classify and route petitions efficiently.

Grouping of Similar Petitions

To avoid redundancy and ensure organized handling of grievances, the

system includes a grouping mechanism that detects petitions with similar
content. Testing involved submitting petitions with slightly varied
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 wording but discussing the same issue. The NLP module effectively
clustered such petitions and suggested merging or linking them under a
common case. This feature helps authorities respond to collective issues
more efficiently while improving transparency.

Sentiment Analysis

The sentiment analysis feature was tested to ensure that the system could
detect the emotional tone of the petition — whether it expresses
frustration, urgency, satisfaction, or neutrality. A range of sample
petitions containing emotional content were analyzed, and the sentiment
scores were accurately computed. Petitions expressing strong emotions or
dissatisfaction were ranked higher in urgency. This allows decision-
makers to prioritize emotionally charged or critical grievances for quicker
action.

Priority Detection

One of the most critical tasks of the NLP module is detecting petitions
that require immediate attention. Test cases included petitions related to
emergencies (e.g., medical assistance, safety hazards, legal threats). The
system was successful in recognizing these cases as high priority based
on both keywords and sentiment. This feature ensures that time-sensitive
issues are escalated promptly, improving the responsiveness and
accountability of the governance process.

3. Performance Testing – Detailed Description

Performance testing was conducted to evaluate how efficiently the AI-

Driven Petition Management System responds under various workload

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 conditions. The primary goal was to ensure system responsiveness,
scalability, and stability during peak usage.

Load Testing involved simulating hundreds of users performing actions

such as petition submission, viewing, and filtering simultaneously. The
system demonstrated reliable performance, maintaining stability without
any crashes, lags, or data inconsistencies under expected user loads.

Stress Testing pushed the system beyond its normal operational limits to
observe how it behaves under extreme conditions. The system showed
resilience up to a certain threshold, gracefully handling slowdowns and
maintaining core functionality without complete failure, which helps in
identifying maximum capacity limits.

Response Time was a key metric, especially during NLP operations like
keyword extraction and sentiment analysis. The average response time for
submitting and processing petitions remained consistently below 2
seconds, confirming the system’s readiness for real-time user interaction
in a live environment.

4. Security Testing – Description

Security testing is a critical phase in ensuring that the AI-Driven Petition

Management System protects sensitive user information and resists
malicious threats. This phase focused on validating the system’s ability to
handle secure authentication and authorization for different user roles
such as public users, administrators, and moderators. Login mechanisms
were tested for robustness against brute-force attacks, and session
handling was verified for proper timeout and re-authentication policies.

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 Data protection measures were examined to ensure that user details,
petition content, and location information are stored securely, utilizing
encryption techniques and secure transmission protocols. Direct access to
backend databases and system files was restricted to prevent data leakage.

Additionally, the system underwent rigorous vulnerability testing for

common web-based attacks including SQL injection, Cross-Site Scripting
(XSS), and Cross-Site Request Forgery (CSRF). The system successfully
mitigated these threats using input sanitization, secure coding practices,
and token-based validation. These security measures collectively uphold
data privacy, integrity, and trustworthiness.

5. Usability Testing

Usability testing assessed the interface and user experience of the

platform.

 User Interface (UI) Consistency:

Verified that the layout, icons, and navigation were consistent across different
modules. Ensured user-friendly design for both technical and non-
technical users.

 Feedback & Error Messages:

Tested how the system responds to invalid inputs or incomplete forms. Clear and
helpful error messages were displayed.

 Accessibility:
Evaluated whether the system is usable for people with disabilities
(basic color contrast, font size, etc.).

PAGE \* MERGEFORMAT 2
6. Integration Testing

Integration testing validated the data flow and interaction between

different modules.

 User Module ↔ Petition Module:

Confirmed that authenticated users could create and view petitions, and data is
passed between modules seamlessly.

 Petition Module ↔ NLP Module:

Verified that petitions are automatically sent to the NLP engine after submission,
and classification results are correctly stored and displayed.

 Admin Dashboard ↔ Public Portal:

Changes made by admins (e.g., status updates, prioritization) were reflected in the
public-facing views.

7. Regression Testing

After code changes or feature additions, regression testing was conducted

to ensure existing functionalities remained unaffected. Automated and
manual tests were run to validate stable performance.

PAGE \* MERGEFORMAT 2
 CHAPTER 8
CONCLUSION AND FUTURE ENHANCEMENTS

CONCLUSION

The AI-Driven Petition Management System serves as a significant

advancement in the realm of digital governance, offering a
comprehensive and intelligent platform for handling public grievances.
By incorporating Natural Language Processing (NLP) algorithms, the
system can intelligently analyze large volumes of petition data to identify
recurring themes, prioritize urgent matters, and eliminate duplicate or
redundant entries. This automated approach not only accelerates the
grievance redressal process but also ensures that critical issues receive
timely attention based on sentiment analysis and user engagement.

The system’s structured categorization of petitions by department and

issue type, along with secure user authentication, ensures that all inputs
are valid, traceable, and efficiently directed to the appropriate authority.
Public users are granted role-based access to promote controlled visibility
while maintaining openness and transparency in the decision-making
process.

Moreover, the platform encourages citizen participation by making the

petitioning process more accessible, engaging, and responsive. By
ranking petitions and displaying their progress, the system reinforces
accountability and public trust, enabling a more democratic and inclusive
environment for voicing concerns.

In conclusion, this AI-powered solution not only enhances the operational

PAGE \* MERGEFORMAT 2
 efficiency of petition handling but also strengthens the fundamental
values of transparency, accountability, and participatory governance.

It lays the foundation for a smarter, more responsive administrative

framework that meets the evolving expectations of digital-age citizens.

FUTURE ENHANCEMENTS

1. Multilingual NLP Support:

To make the system more inclusive, future versions can integrate multilingual NLP
capabilities, allowing users to submit petitions in various regional
languages for better accessibility and outreach.

2. AI-Powered Chatbot Assistance:

A smart chatbot can be integrated to guide users through petition submission,

provide real-time status updates, and answer frequently asked
questions, improving user experience and reducing manual
workload.

3. Predictive Analytics for Policy Insights:

Advanced AI models can be used to forecast emerging public concerns and trends
from petition data, helping policymakers proactively address issues
before they escalate.

4. Blockchain Integration for Tamper-Proof Records:

Implementing blockchain technology can ensure tamper-proof storage of petitions

and their updates, increasing trust and transparency in the system.

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 5. Real-Time Collaboration with Departments:

The system can be enhanced to enable direct communication and real-time

collaboration between departments and petitioners for faster
resolution and feedback.

6. Mobile App Accessibility:

A dedicated mobile application can be developed for more convenient access,

allowing users to submit and track petitions on the go.

7. Geo-Mapping of Petitions:

Incorporating a geo-mapping feature can visually represent the location-based

distribution of petitions, helping authorities identify regional issues
effectively.

8. Public Dashboard and Analytics Visualization:

A transparent public dashboard with data visualizations of petitions, statuses, and

response times can increase public trust and encourage civic
participation.

PAGE \* MERGEFORMAT 2
 CHAPTER 9
APPENDICES

from sqlalchemy.exc import IntegrityError

from tokenize import Comment
from distro import like
from flask import Flask, render_template, request, redirect, url_for, flash,
session
from flask_sqlalchemy import SQLAlchemy
from flask_bcrypt import Bcrypt
from flask_login import LoginManager, UserMixin, login_user,
login_required, logout_user, current_user
import os
from werkzeug.utils import secure_filename
import spacy
from datetime import datetime
from flask import Flask, request, jsonify, session

app = Flask(__name__)
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///database.db'
app.config['SECRET_KEY'] = 'secretkey'
app.config['UPLOAD_FOLDER'] = 'static/uploads'
db = SQLAlchemy(app)
bcrypt = Bcrypt(app)
login_manager = LoginManager(app)
login_manager.login_view = 'login'

nlp = spacy.load("en_core_web_sm")

# User Model
class User(UserMixin, db.Model):
id = db.Column(db.Integer, primary_key=True)
name = db.Column(db.String(100), nullable=False)
email = db.Column(db.String(100), unique=True, nullable=False)
password = db.Column(db.String(255), nullable=False)
mobile = db.Column(db.String(15), nullable=False)
dob = db.Column(db.String(20), nullable=False) # You can change to
db.Date if needed
profile_pic = db.Column(db.String(100)) # stores filename

65
class Petition(db.Model):
id = db.Column(db.Integer, primary_key=True)
user_id = db.Column(db.Integer, db.ForeignKey('user.id'),
nullable=False)
file_name = db.Column(db.String(255), nullable=False)
is_public = db.Column(db.Boolean, default=False)
category = db.Column(db.String(100), nullable=False)
department = db.Column(db.String(100))
upload_time = db.Column(db.DateTime, default=datetime.utcnow)
verified = db.Column(db.Boolean, default=False)
likes = db.relationship("Like", backref="petition", lazy="dynamic")
repeat_count = db.Column(db.Integer, default=1) # Removed
content_hash

def increment_repeat_count(self):
self.repeat_count += 1
db.session.commit()

# Verification Model
class Verification(db.Model):
id = db.Column(db.Integer, primary_key=True)
name = db.Column(db.String(100), nullable=False)
aadhar_no = db.Column(db.String(12), unique=True, nullable=False)
dob = db.Column(db.String(10), nullable=False)
location = db.Column(db.String(100), nullable=False)

class Like(db.Model):
id = db.Column(db.Integer, primary_key=True)
user_id = db.Column(db.Integer, nullable=False)
petition_id = db.Column(db.Integer, db.ForeignKey("petition.id"),
nullable=False)

class Comment(db.Model):
id = db.Column(db.Integer, primary_key=True)
user_id = db.Column(db.Integer, nullable=False)
petition_id = db.Column(db.Integer, db.ForeignKey("petition.id"),
nullable=False)
text = db.Column(db.Text, nullable=False)

@login_manager.user_loader
def load_user(user_id):
66
 return User.query.get(int(user_id))

@app.route('/index')
def home():
return render_template('index.html')

# User Registration
@app.route('/register', methods=['GET', 'POST'])
def register():
if request.method == 'POST':
name = request.form['name']
email = request.form['email']
mobile = request.form['mobile']
dob = request.form['dob']
password =
bcrypt.generate_password_hash(request.form['password']).decode('utf-8')

profile_pic_file = request.files['profile_pic']
profile_pic_filename = None
if profile_pic_file:
profile_pic_filename = secure_filename(profile_pic_file.filename)
profile_pic_path = os.path.join(app.config['UPLOAD_FOLDER'],
profile_pic_filename)
profile_pic_file.save(profile_pic_path)

user = User(
name=name,
email=email,
mobile=mobile,
dob=dob,
password=password,
profile_pic=profile_pic_filename
)
db.session.add(user)
db.session.commit()

flash("Registration Successful. Please Login.", "success")

return redirect(url_for('login'))

return render_template('register.html')

# User Login
@app.route('/', methods=['GET', 'POST'])
67
def login():
if request.method == 'POST':
email = request.form['email']
password = request.form['password']
user = User.query.filter_by(email=email).first()
if user and bcrypt.check_password_hash(user.password, password):
login_user(user)
return redirect(url_for('home'))
else:
flash("Invalid Credentials", "danger")
return render_template('login.html')

ADMIN_USERNAME = "admin"
ADMIN_PASSWORD = "admin123"

@app.route("/adminlogin", methods=["GET", "POST"])

def admin_login():
if request.method == "POST":
username = request.form.get("username")
password = request.form.get("password")

if username == ADMIN_USERNAME and password ==

ADMIN_PASSWORD:
session["admin_logged_in"] = True # Store session data
flash("Login successful!", "success")
return redirect(url_for("show_petitions"))

flash("Invalid username or password!", "danger")

return render_template("admin_login.html")

@app.route('/comment_petition/<int:petition_id>', methods=['POST'])
@login_required
def comment_petition(petition_id):
petition = Petition.query.get_or_404(petition_id)
comment_text = request.form.get("comment")

if not comment_text.strip():
flash("Comment cannot be empty!", "danger")
return redirect(url_for('view_petitions'))

comment = Comment(text=comment_text, user_id=current_user.id,

68
petition_id=petition_id)
db.session.add(comment)
db.session.commit()
flash("Comment added successfully!", "success")
return redirect(url_for('view_petitions'))

@app.route('/verify', methods=['GET', 'POST'])

@login_required
def verify():
if request.method == 'POST':
name = request.form['name']
aadhar_no = request.form['aadhar_no']
dob = request.form['dob']
location = request.form['location']

# Check verification
verification = Verification.query.filter_by(name=name,
aadhar_no=aadhar_no, dob=dob, location=location).first()
if verification:
# ✅ Retrieve petition data from session
petition_data = session.get("petition_data")

if petition_data:
new_petition = Petition(
file_name=petition_data.get("file_name"),
is_public=petition_data.get("is_public"),
category=petition_data.get("category"),
upload_time=datetime.utcnow(), # Add this if missing in the
model
user_id=current_user.id, # Add this if Petition model has
user_id
verified=True
)
db.session.add(new_petition)
db.session.commit()
session.pop("petition_data", None)

flash("Petition Verified & Submitted Successfully!", "success")

return redirect(url_for('home'))
else:
return redirect(url_for('home'))

else:
69
 flash("Verification Failed! Check Your Details.", "danger")

return render_template('verify.html')

@app.route('/petitions')
def view_petitions():
petitions = Petition.query.filter_by(is_public=True).all() # Ignore
'verified' for testing

if not petitions:
flash("No public petitions available!", "info")

return render_template('petitions.html', petitions=petitions)

def detect_department(content):
department_keywords = {
"Corporation": ["corporation", "municipality", "sanitation",
"garbage", "road"],
"Water Supply": ["water", "pipeline", "leak", "drain", "supply"],
"Electricity": ["electricity", "power", "current", "transformer"],
"Health": ["hospital", "clinic", "health", "medical"],
"Education": ["school", "college", "education", "student"]
}

content = content.lower()
for dept, keywords in department_keywords.items():
if any(word in content for word in keywords):
return dept
return "Other"

# Function to categorize petition content

def categorize_petition(content):
keywords = {
"urgent": ["immediate", "emergency", "critical", "severe", "urgent"],
"fast": ["quick", "rapid", "fast", "soon"],
}

category = "Normal"
70
 doc = nlp(content.lower())

for token in doc:

if token.text in keywords["urgent"]:
category = "Urgent"
break
elif token.text in keywords["fast"]:
category = "Fast"

department = detect_department(content)
return category, department

import fitz
@app.route("/upload_petition", methods=["GET", "POST"])
@login_required
def upload_petition():
if request.method == "POST":
file = request.files.get("file")
is_public = request.form.get("is_public") == "on"

if not file:
flash("No file selected!", "danger")
return redirect(url_for("upload_petition"))

file_ext = file.filename.rsplit(".", 1)[-1].lower()

file_path = os.path.join(app.config["UPLOAD_FOLDER"],
file.filename)

content = ""
try:
if file_ext == "txt":
content = file.read().decode("utf-8")
elif file_ext == "pdf":
file.save(file_path)
doc = fitz.open(file_path)
content = "\n".join([page.get_text() for page in doc])
else:
flash("Unsupported file format! Upload TXT or PDF.",
"danger")
return redirect(url_for("upload_petition"))
except Exception as e:
71
 flash(f"Error reading file: {e}", "danger")
return redirect(url_for("upload_petition"))

if not content.strip():
flash("Uploaded file is empty or unreadable!", "danger")
return redirect(url_for("upload_petition"))

existing_petition = Petition.query.filter_by(
user_id=current_user.id,
file_name=file.filename
).first()

if existing_petition:
existing_petition.increment_repeat_count()
db.session.commit()
flash(f"Duplicate petition uploaded! Repeat count:
{existing_petition.repeat_count}", "warning")
return redirect(url_for("upload_petition"))

if file_ext == "txt":
file.save(file_path)

category, department = categorize_petition(content)

verified_status = True if is_public else False

try:
new_petition = Petition(
user_id=current_user.id,
file_name=file.filename,
is_public=is_public,
category=category,
department=department,
verified=verified_status
)
db.session.add(new_petition)
db.session.commit()
except IntegrityError:
db.session.rollback()
flash("Petition already exists or conflict in saving!", "danger")
return redirect(url_for("upload_petition"))

if verified_status:
72
 flash("Petition uploaded and publicly visible!", "success")
else:
flash("Petition uploaded successfully! Pending verification.",
"info")

return redirect(url_for("verify"))

return render_template("upload_petition.html")

from flask_mail import Mail, Message

app.config["MAIL_SERVER"] = "smtp.gmail.com"
app.config["MAIL_PORT"] = 587
app.config["MAIL_USE_TLS"] = True
app.config["MAIL_USERNAME"] = "daminmain@gmail.com"
app.config["MAIL_PASSWORD"] = "kpqtxqskedcykwjz"
mail = Mail(app)

def send_email(to, subject, body):

import smtplib
from email.message import EmailMessage

msg = EmailMessage()
msg['Subject'] = subject
msg['From'] = 'daminmain@gmail.com'
msg['To'] = to
msg.set_content(body)

with smtplib.SMTP('smtp.gmail.com', 587) as smtp:

smtp.starttls()
smtp.login('daminmain@gmail.com', 'kpqtxqskedcykwjz')
smtp.send_message(msg)

@app.route('/mark_complete/<int:petition_id>', methods=['POST'])
@login_required
def mark_complete(petition_id):
petition = Petition.query.get_or_404(petition_id)

user = db.session.get(User, petition.user_id)

if user:
try:
send_email(user.email, "Petition Completed",
73
 f"Hello {user.name},\n\nYour petition
'{petition.file_name}' has been marked as complete and removed from
the system.")
except Exception as e:
print(f"Failed to send email: {e}")

file_path = os.path.join(app.config['UPLOAD_FOLDER'],
petition.file_name)
if os.path.exists(file_path):
os.remove(file_path)

db.session.delete(petition)
db.session.commit()

flash("Petition marked as complete and removed.", "success")

return redirect(url_for('show_petitions'))

@app.route("/like_petition", methods=["POST"])
def like_petition():
petition_id = request.args.get("petition_id")
user_id = current_user.id # Make sure the user is logged in

if not petition_id:
return "Petition ID is required", 400

# Check if the user already liked this petition

existing_like = Like.query.filter_by(user_id=user_id,
petition_id=petition_id).first()
if existing_like:
return "Already liked", 400

# Add new like

like = Like(user_id=user_id, petition_id=petition_id)
db.session.add(like)
db.session.commit()

return redirect(url_for("view_petitions"))

@app.route("/add_comment/<int:petition_id>", methods=["POST"])
def add_comment(petition_id):
if "user_id" not in session:
74
 flash("You need to log in to comment.", "warning")
return redirect(url_for("login"))

comment_text = request.form.get("comment_text")
if not comment_text:
flash("Comment cannot be empty.", "danger")
return redirect(url_for("view_petitions"))

new_comment = Comment(petition_id=petition_id,
user_id=session["user_id"], text=comment_text,
user_name=session["user_name"])
db.session.add(new_comment)
db.session.commit()

@app.route("/view_petitions")
def show_petitions():
departments = db.session.query(Petition.department).distinct().all()
departments = [dept[0] for dept in departments if dept[0]] # flatten

selected_dept = request.args.get("department")

if selected_dept:
petitions = Petition.query.filter_by(department=selected_dept).all()
else:
petitions = Petition.query.all()

return render_template(
"view_petitions.html",
departments=departments,
petitions=petitions,
selected_dept=selected_dept
)

from flask import Flask, request, render_template, jsonify

import g4f

@app.route('/ch')
def ch():
return render_template('chat.html')

@app.route('/chat', methods=['POST'])
def chat():
75
 user_message = request.json.get('message')

try:
# Use g4f.ChatCompletion to get a response
response = g4f.ChatCompletion.create(
model=g4f.models.default, # or g4f.models.gpt_35_turbo
messages=[{"role": "user", "content": user_message}]
)
return jsonify({'reply': response})
except Exception as e:
return jsonify({'reply': f"Error: {str(e)}"})

# Logout
@app.route('/logout')
@login_required
def logout():
logout_user()
return redirect(url_for('login'))

if __name__ == '__main__':
with app.app_context():
db.create_all()
app.run(debug=True)

76
OUTPUT :

77
REFERENCES

 Rawat, M., & Kaushik, N. (2021). NLP based grievance

redressal system for Indian Railways. arXiv preprint
arXiv:2111.08999.
 Zhang, A. X., Hugh, G., & Bernstein, M. S. (2020). PolicyKit:
Building Governance in Online Communities. arXiv preprint
arXiv:2008.04236.
 Arana-Catania, M., Van Lier, F. A., Procter, R., Tkachenko,
N., He, Y., Zubiaga, A., & Liakata, M. (2021). Citizen
Participation and Machine Learning for a Better Democracy.
arXiv preprint arXiv:2103.00508.
 Kaur, J., Antony, K., Pujar, N., & Jha, A. (2024). Blockchain
based Decentralized Petition System. arXiv preprint
arXiv:2407.00534.
 Zhang, A. X., Hugh, G., & Bernstein, M. S. (2020). PolicyKit:
Building Governance in Online Communities. arXiv preprint
arXiv:2008.04236.
 Arana-Catania, M., Van Lier, F. A., Procter, R., Tkachenko,
N., He, Y., Zubiaga, A., & Liakata, M. (2021). Citizen
Participation and Machine Learning for a Better Democracy.
arXiv preprint arXiv:2103.00508.
 Kaur, J., Antony, K., Pujar, N., & Jha, A. (2024). Blockchain
based Decentralized Petition System. arXiv preprint
arXiv:2407.00534.
 Rawat, M., & Kaushik, N. (2021). NLP based grievance
redressal system for Indian Railways. arXiv preprint
arXiv:2111.08999.

78
 Zhang, A. X., Hugh, G., & Bernstein, M. S. (2020). PolicyKit:
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