CS4624: Multimedia, Hypertext, and Information Access

Permanent URI for this collection

https://hdl.handle.net/10919/18655

This collection contains the final projects of the students in in the course Computer Science 4624: Multimedia, Hypertext, and Information Access, at Virginia Tech. This course, taught by Professor Ed Fox, is part of the Human-Computer Interaction track, the Knowledge, Information, and Data track, and the Media/Creative Computing track. The curriculum introduces the architectures, concepts, data, hardware, methods, models, software, standards, structures, technologies, and issues involved with: networked multimedia (e.g., image, audio, video) information, access and systems; hypertext and hypermedia; electronic publishing; virtual reality. Coverage includes text processing, search, retrieval, browsing, time-based performance, synchronization, quality of service, video conferencing and authoring.

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Optimizing LLMs for Higher Education
Helvey, Janna; Mhatre, Sahil; Singh, Sahaj; Marraccini, Anthony; Sheikh Ayaan (2025-05-08)
The objective of this project is to tailor large language models (LLMs) to higher education for the ultimate aim of expanding access to educational materials for marginalized communities. Around the world, many potential students are excluded from higher education by socioeconomic or geographic barriers. We are building on the past work of our client, Nick York, who did research on "Broadening the Availability of Computer Science Education to Underrepresented Groups Using AI." We are building on this vision by incorporating a Retrieval-Augmented Generation (RAG) model that enables LLMs to leverage external documents—such as quizzes and PDFs—dynamically as contextual input. This enables the model to produce more precise, relevant, and meaningful educational responses. We are evaluating the effectiveness of models such as Mistral-7B, OpenAI's GPT-4.0 and Meta's Tiny LLaMa, actively working on prompt engineering techniques and multi-model validation techniques. We have established a workable RAG pipeline with different models, and have refined prompt design to better facilitate ongoing, high-level educational discussion and personalized learning feedback.
Traffic Visualization Dashboard
Patel, Pranav; Cao, Peter; Kim, Esther; Mullapudi, Syam; Lim, Eugene (2025-05)
The Traffic Visualization Dashboard project, led by the Virginia Tech Transportation Institute (VTTI), is a project created last semester by a team of students that seeks to improve the accessibility and user-friendliness of the Integration Traffic Simulation Tool. The aim of last semester’s project is to improve the usability of the simulator by creating a comprehensive visualization dashboard. This dashboard would take complex raw data and transform it into intuitive, meaningful visualizations, allowing users to gain insights into traffic behavior and patterns.
Road Network Partitioning
Glenn, Liam; Ritter, Brooke; Sonawane, Swanand; Chatelain, Cliff; Zhu, Wavin (2025-05)
This is a new project that works on building a web application that started in the Spring semester of 2025. Road network partitioning is essential for efficiently managing and analyzing large-scale transportation networks. It involves dividing a road system into smaller, more manageable segments or regions based on different factors, such as traffic flow, geographic location, and connectivity. It helps distribute traffic load evenly, reduce congestion in busy areas, enable faster and more effective routing for navigation systems, and support city planners in designing better road networks. In the Spring of 2025, our team began designing a web application that handles the partitioning of different road networks efficiently and seamlessly. Our system will incorporate multiple partitioning algorithms, allowing users to run simulations on various datasets and compare different partitioning methods through a history feature. Furthermore, we will also have a feature that lets the users run simulations using their own partitioning algorithm. The application will feature a user authentication system, an interface for creating new partitions, and a history page to track previous results. While the project is currently in the design and implementation phase, our goal is to provide a scalable and intuitive solution for traffic analysts, urban planners, and researchers. Future enhancements will include additional partitioning techniques and a more refined, user-friendly interface to further improve usability and performance.
CS4624 SP2025 Search ETD Elements
Sun, Michael; Iqbal, Hamza; Hester, Joshua; Kim, Jiwon; Huynh, Marcus (Virginia Tech, 2025-05-08)
ETDs (Electronic Theses and Dissertations) are documents that are written by hard-working individuals, containing information about significant discoveries and achievements regarding their work. For our project, we continued building the system off of previous teams’ (mainly 2022 and 2023) objectives. This includes scaling the application from 200k ETDs to 500k ETDs, increasing indexing speed and search/retrieval speed, and adding additional web components to support figure and table captions. A critical component of this project is Elasticsearch, a search engine built on Apache Lucene, which is used to store and search these ETDs, figures, and captions.
Automated Assessment of Students’ Short Written Answers Using NLP and Large Language Models (LLMs)
Assadzadeh, Arian; Attia, Demiana; Ghanta, Sanjana; Phan, Tai; Walker, Trey (Virginia Tech, 2025-05-09)
This project proposes the design and implementation of an automated web-based system for automated formative assessment of student’s short written answer questions using Natural Language Processing (NLP) and Large Language Models (LLMs). The proposed solution addresses the ever-growing challenge in modern education where educators face increasing class sizes, different types of approaches and student abilities, and limited time to give personalized individual feedback for each student. By leveraging recent advances within the area of machine learning, this project’s goal is to offer a scalable, efficient, and meaningful tool that can assist both students and instructors in the feedback process. The tool will feature user authentication, answer uploads, grading via rubric, examples, or concepts, and results displayed in a structured interface like a table.
Crisis Events Template Generation and Information Extraction using LLM
Hess, Nicholas; Small, Jackson; Gresge, Gibbs; Ryu, Alex; Tummeti, Aneesh (2025-05)
In an age where real-time access to reliable information is critical, crisis events such as hurricanes, earthquakes, and public safety incidents often generate a deluge of fragmented, unstructured data spread across numerous online sources. This makes it difficult for emergency responders, researchers, and decision-makers to quickly extract accurate, actionable insights. Our capstone project, Crisis Events Template Generation and Information Extraction using LLM, addresses this challenge by developing a web application that automates the process of summarizing crisis events and extracting key information from a set of user-provided webpages. The core functionality of the application is powered by a Large Language Model (LLM), which enables both the generation of structured templates for different types of crises (e.g., natural disasters, mass casualty events) and the automatic filling of those templates with relevant facts derived from online content. Users select a crisis category and upload URLs related to a specific event; the system then scrapes and cleans the text using BeautifulSoup [8], passes the content to the LLM for inference, and returns a populated template highlighting key data points such as dates, locations, intensities, and impacts. The project combines a modern web technology stack: a responsive frontend built with Next.js [1] and Tailwind CSS [2], secure authentication via GitHub OAuth [10] using NextAuth.js [3], and a backend implemented with Flask and MongoDB. The backend handles LLM-based processing and web scraping, exposing endpoints that communicate with the frontend via JSON. Docker is used to containerize and streamline deployment [6]. To date, our team has successfully built a functioning prototype with support for initial template generation, web scraping from multiple URLs, and text-to-slot mapping using LLMs. We’ve implemented role-based access control, enabling admins to manage templates while allowing end-users to generate and store summaries. We have also addressed key technical hurdles such as asynchronous scraping performance. Looking ahead, we plan to enhance the LLM prompting strategy, increase template flexibility, and broaden the range of supported crisis event types. Additionally, we will polish the user interface, improve error handling, and complete user and developer documentation. By project’s end, our goal is to deliver a scalable, user-friendly solution that enables quick and structured extraction of critical crisis event information paving the way for faster, data-informed responses to emergencies.
ETDs Knowledge Graph Building
Fredericks, Sidney; Budd, Dashiell; Zheng, Samantha; Kim, Junsoo (2025-05)
ETDs are Electronic Theses and Dissertation, and this project aims to enhance the storage, accessibility, and exploration of Virginia Tech's ETDs by transforming traditional metadata into a searchable knowledge graph. Recognizing the limitations of flat or relational storage for representing rich academic relationships, we developed a dual-database architecture using Virtuoso (RDF/SPARQL) and Neo4j (property graph/Cypher) to model key entities such as authors, advisors, departments, and disciplines. A Streamlit-based web interface provides an intuitive search experience across both databases, enabling users to explore semantic connections by keyword, year, and entity type. The backend includes a Python-based data pipeline that transforms flat CSV data into normalized graph structures, optimized for batch loading at scale. This framework demonstrates a scalable, future-proof approach to managing large volumes of academic content, supporting more meaningful discovery and long-term preservation of institutional knowledge.
Crisis Events Digital Library
Sundstrom, Jake; Moore, Thomas Gregory; Coates, Tristan; Martinez-Diaz, Alejandro (Jake Sundstrom, 2025-05-06)
The Crisis Events Web pages Digital Library is a locally hosted web application designed to collect, organize, and visualize user-submitted information about crisis events across the United States. Each event is modeled as a digital object containing structured metadata—location (latitude/longitude and municipality), date and time, event types such as wildfire, tornado, and flood, casualty figures, economic impact estimates, and a descriptive narrative. The system allows the users to take events of similar crises such as two cases of fires or two cases of tornadoes and view side-by-side charts and tables that illustrate differences in incident date, duration, death toll, property damage, and other key metrics. On the frontend, the application uses React with React Router and Tailwind CSS to deliver a responsive and easy to use interface. The backend was made using Spring Boot as it supports data storage, API services, and user authentication. A key feature of the system is the ability to allow users to upload their urls regarding the crisis events, as well as the data regarding the event mentioned prior, and the database will store it for later viewing and comparing. This project was completed as part of the CS 4624 Spring 2025 curriculum at Virginia Tech, with the goal of supporting future academic research, enhancing public awareness of historical and contemporary crises, and contributing to the digital archiving of major U.S. events. The platform showcases the integration of multimedia access, information retrieval, and interactive visualization in a real-world application.
Building Web App for Automated Vehicles Energy and Emissions Estimations
Crumpacker, Katelyn; White, Trevor; Rankin, David; Paladugu, Harsha (2025-05)
As transportation moves toward greater sustainability, accurately understanding and comparing vehicle energy consumption and emissions is increasingly essential. The transportation sector is one of the largest contributors to greenhouse gas emissions, highlighting the need for smarter, cleaner solutions. Data collection in this sector plays a critical role in sustainability efforts by enabling detailed analysis of vehicle emissions, energy use, and traffic behavior so that more efficient transportation systems can be developed. To support these efforts, our project delivers an intuitive web application built on energy consumption models for various vehicle types, including Internal Combustion Engine Vehicles (ICEVs), Battery Electric Vehicles (BEVs), Hybrid Electric Vehicles (HEVs), and Hydrogen Fuel Cell Vehicles (HFCVs). ICEVs rely on traditional gasoline-powered engines, while BEVs operate entirely on rechargeable batteries. HEVs combine internal combustion engines with electric motors powered by regenerative braking and engine energy. HFCVs generate electricity through a chemical reaction in a fuel cell, emitting only water vapor. Users can input speed and acceleration profiles to generate immediate visual comparisons of estimated energy consumption and particle emissions across these vehicle types. This enables informed comparisons based on multiple sustainability metrics. The platform is designed to serve a range of users, from researchers to drivers seeking to make environmentally conscious vehicle choices. Expanding on prior work, this project significantly enhances the user experience with an upgraded interface, improved responsiveness for large datasets, and advanced data visualizations. Additional features include streamlined file upload/download capabilities, better database integration, vehicle-specific input (make, model, and year), support for particulate matter analysis, and flexible data filtering for targeted comparisons. By offering a comprehensive and accessible tool, the platform empowers users to engage with meaningful transportation data and make decisions that support long-term environmental goals.
Group 16: Web App for Merging Traffic Data
Dey, Srinjoy; Marterella, Blake; Horowitz, Jake; Szilvasi, Ryan; Smith, Ashton (2025-05)
Every day, thousands of drivers utilize highways and interstates covering the vast corners of Virginia. At Virginia Tech, many faculty, staff, and students, both undergraduate and graduate, rely on these roads for daily commutes. Due to the length and frequency of use of these road segments, crashes are common and often place individuals in harmful situations. Crash prediction models can be instrumental in reducing risk, but their effectiveness relies on the availability of clean, well-structured datasets. This project introduces a web-based application that enables users to upload three different traffic-related datasets—Linear Referencing System (LRS), Average Daily Traffic (ADT), and crash reports—and merge them based on the spatial geometry of roads and crash locations. The merged data is then visualized on an interactive map, allowing users to explore crash-prone areas with year-based filtering. The system is built using PostgreSQL with PostGIS, Flask, and GeoPandas, and it supports spatial joins, geometric filtering, and dataset export. By providing tools for dataset creation and visualization, this application aids both researchers and drivers in making informed, data-driven decisions about road safety and travel planning. With this goal, hopefully driving will become safer for all throughout the entire state of Virginia.
Building an Intelligent QA/Chatbot for Transportation with LangChain and Open Source LLMs
Do, Ethan; Sunkarapalli, Aneesh; Patil, Aarush; Akalwadi, Neil; Ghaleb, Rami (Virginia Tech, 2025-05-02)
This project focuses on building an intelligent question-answering (QA) chatbot to assist transportation engineers who frequently use complex traffic simulation software. The chatbot helps users extract information from simulation manuals by allowing them to ask natural language questions and receive context-aware answers. It integrates LangChain for pipeline management, ChromaDB for vector-based document retrieval, and open-source Large Language Models (LLMs) for generating responses. Using a Retrieval-Augmented Generation (RAG) approach, the system improves answer accuracy by pulling relevant content from domain-specific manuals. The chatbot is deployed as a web application with features such as persistent conversation histories, organized collections of interactions, and secure user authentication. This report outlines the team’s development process, including document preprocessing and chunking, integration of open-source tools, and the milestones reached. It also discusses challenges such as maintaining conversation context and improving the user interface.
Crisis Events One-Class Text Classification
Jonnavithula, Prabhath; Sanghi, Nekunj; Holder, Gabriel; Srinivas, Anav; Yirdaw, Menase (Virginia Tech, 2025-05-07)
This project aims to design and develop a one-class text classification system tailored to process crisis-related web pages to gain data insights at a high precision. Unlike traditional binary classifiers, our approach addresses the practical challenge of classifying documents when only examples of one class - i.e., the crisis event and related articles are available - and the negative class is undefined or highly variable. One-class classification (OCC) offers a more effective solution for this problem by treating non-crisis content as outliers or anomalies. The final deliverable will be an integrated web application that allows users to input URLs related to a crisis event. The backend will scrape, clean, and preprocess webpage content using tools such as requests and BeautifulSoup. The core machine learning engine, implemented using both traditional OCC algorithms (One-Class SVM) and advanced deep learning methods (specifically the DOCC method with PyTorch), will evaluate each page for relevance. Results will be presented through a React-based user interface, supported by a FastAPI backend and SQLite database for persistent storage and retrieval. Our pipeline consists of data collection, preprocessing, model training, evaluation and visualization, all integrated into a web app, developed through end-to-end testing. After finalizing the technology stack and dividing roles, we have currently implemented the first version of our front-end and ML model. This project not only serves a practical societal need by identifying and surfacing timely crisis information but also deepens our understanding of anomaly detection and full-stack application development in a real-world setting.
A Digital Library for CS4624 Projects
Robertson, Ty; Clemons, Benjamin; Barnes, Austin; Davis, Joshua; Zada, Mushtaq Nabi (2025-05)
CS4624 is one of many capstone courses a student within the CS curriculum is able to take. The multimedia and hypertext course delves into the diverse range of multimedia content such as images, audio, video, as well as any information retrieval and access relating to it. With this course comes the capstone project, a semester-long project given to us students to allow a display of mastery within our discipline. It has so far been a pleasure to work with Dr. Farag’s guidance with the project. An insight into real-world applications as well as a diverse approach to different problems has allowed us to grow as both people and developers. The current discovery portal for CS4624 student projects serves as a platform for students working within the course to submit and hold their projects. Details included within the pages on the discovery portal consist of abstract, date, author, presentation, final report, source code, and collections. Additionally, the discovery portal contains filtering features to allow users to specifically search for any project dependent on recent submissions, issue semester, author, title, subject, content type, and department. The discovery portal also provides three separate home views depending on the user's permissions. Current supported user roles are admin, client, and student. This allows teachers to easily access desired projects as well as a safe holding for semester long projects that students worked hard on. Rather than discarding prior work, we have extended and enhanced it by integrating backend support in Python/Flask to interface with Docker and dynamically assign ports for each running container. With this comes the purpose of our project this semester. After reviewing the previous semester’s project as well as discussing with our client and professor Dr. Mohammad Farag, it was concluded that the current largest issue with the discovery portal was the lack of an easy way to run projects. As it stood, to run a project from the discovery portal, the user would have to manually download the code and configure codebases themselves. This is an issue for all types of users. Students want an easier way to run previous semesters’ projects, and to view other projects from the same semester. The client desired a way to run projects within the discovery portal without the need for manual configuration. After analyzing the discovery portal, we concluded that the most important feature for the discovery portal would be a “run” button on each project page. This would allow any user to have a one-click solution for running a project’s code, instead of manually having to download and configure codebases. Clicking the run button would deploy a docker instance to run on a dedicated port, unless an image of the specific project was already running. This provides the user with an easy way to run any project they desire and interact with it in the discovery portal. Upon completion of the project, we hope to have provided future CS4624 students and staff an easier and more efficient tool to complete their projects with.
Building Web App for Automated Vehicles Fuel/Energy Estimation
Quinn, Courtney; Scott, Layla; Chao, Christina; Tapia, Eric; Batra, Rohin (Virginia Tech, 2024-12-16)
“Is switching from a gasoline engine to an electric or hybrid vehicle worth it?” The decision involves multiple factors: while fuel prices are high, electric vehicles also come with significant upfront costs, raising questions about cost-effectiveness and environmental impact. Consumers and researchers alike need accessible tools to evaluate such factors in the context of sustainability. As the transportation sector embraces eco-friendly vehicles, the demand grows for user-centered tools that clarify energy consumption data for both industry experts and general users. This interdisciplinary project responds to this need by creating a web-based application that integrates the Virginia Tech Transportation Institute (VTTI) models for vehicle energy consumption within a graphical user interface. By making these sophisticated models accessible, the platform empowers both researchers and non-expert users to make data-driven decisions on vehicle energy efficiency and environmental impact. Past studies, such as those by Madziel and Campisi, highlight the impact of variables like temperature, vehicle load, and driving style on electric vehicle (EV) energy usage [1]. Our platform provides an intuitive interface, enabling users to upload speed data, select a vehicle type, and view real-time energy analytics through interactive charts. The system includes support for: • Internal Combustion Engine Vehicles (ICEV) • Battery Electric Vehicles (BEV) • Hybrid Electric Vehicles (HEV) • Hydrogen Fuel Cell Vehicles (HFCV) Built using React and Python Flask, the site includes data upload, calculation results, and visualization features for all user levels. By bridging complex analytics with user-friendly design, this platform supports informed, data-driven decisions in sustainable transportation.
Crash Rate Prediction from Traffic Volume Data using AI
Chan, Travis; Hossain, Syeda; Le, Jonathan; Asam, Arham; Khadka, Devanshu (Virginia Tech, 2024-12)
In today's fast-paced, technology-driven world, we're generating more transportation data than ever before. This data offers opportunities to making roads safer and more efficient, but is often hard to take advantage of. Our client, Dr. Mohamed Farag is a researcher in the Center for Sustainable Mobility (CSM) at the Virginia Tech Transportation Institute, a research institute whose work contributes to the advancement of the transportation industry. To address this challenge, we have developed a user-friendly web application that harnesses machine learning to predict crash rates based on traffic volume data. We have developed a web application that allows users to use machine learning models to predict crash rates for roads. It is comprised of four main components: a frontend interface, a backend server, an API, and offline machine learning model development using Google Colaboratory. Administrators have additional privileges, such as managing machine learning models through the Model Management section. They can upload new models, specify model details like names and attributes, and monitor existing models via the Model List page, which displays all models along with their creation dates and statuses. We've implemented secure user authentication on the frontend using JWT tokens for login and sign-up processes. The Home Page presents users with a tabular view of past predictions, allowing them to see the date, model used, and results, as well as the option to add new predictions. Our backend architecture features a Next.js server for the web backend and a FastAPI server for the machine learning backend. The web backend handles user authentication, prediction collections, and model management, while interfacing with the FastAPI ML backend to generate predictions. To ensure quality and reliability, we've conducted extensive testing and evaluation, including machine learning model testing, model evaluation, and client assessments. We recognize that further work is needed to finalize the product. This report outlines our plans for the remainder of the semester and proposes ideas for future enhancements beyond the current project scope—all aimed at making our roads safer through data-driven insights.
Frontend-Crisis Events Digital Library
Hall, James; Seth, Ananya; Patel, Ansh (Virginia Tech, 2024-12-11)
The Frontend for Crisis Events Digital Library project addresses the need for a streamlined, consolidated interface to assist users in understanding and analyzing crisis events. Current tools for crisis event analysis exist as separate applications, including Text Summarization, Knowledge Graph Generation, News Coverage Analysis, and Information Extraction. These applications are widely used by responders, analysts, and the public, who often face difficulties navigating through multiple platforms to obtain comprehensive insights. By unifying these tools within a single web interface, the project aims to reduce complexity, save time, and improve accessibility for users. The project will link this frontend interface with an integrated backend that processes and retrieves data efficiently across applications. This front-end solution will be documented extensively, covering its architecture, development process, API integration, and usage guidelines to ensure ease of adoption, scalability, and maintainability. Ultimately, this project enhances users' ability to gather, interpret, and act upon crisis data, fostering a better understanding and response to such events.
Traffic Visualization Dashboard
Worsley, Gabriel; Noneman, Brett; Borghese, Matt; Xinchen, Liao; Xi, Chen (2024-12)
The Traffic Visualization Dashboard project is an initiative by the Virginia Tech Transportation Institute (VTTI) to enhance the usability of the Integration Traffic Simulation Tool. This powerful tool simulates traffic flow, but the complexity of its raw data can make it difficult for users to interpret and derive valuable insights. The primary aim of this project is to transform this raw data into intuitive and meaningful visualizations through a comprehensive dashboard. The dashboard’s key functionalities include user authentication and secure access, data collection upload, and dynamic visualization of traffic environments.
A Discovery Portal for Twitter Collections
Casery, Christina; Anderson, Quinn; Omotosho, Abdul; Patel, Kirti; Johnson, Adrian (2024-12-15)
This report documents the continuation of a project begun by previous students three years ago in 2021. About six billion Tweets have been collected in three formats, Social Feed Manager (SFM), yourTwapperKeeper (YTK), and Digital Methods Initiative Twitter Capture and Analysis Toolset (DMI-TCAT), by the Digital Library Research Laboratory (DLRL) at Virginia Tech. The overall goal of this project is to organize these Tweets into event collections and consolidate the collection information that is stored in three different schemas and databases into one web app, making the data more accessible. In Fall 2021, the Library6BTweet team designed an individual Tweet and collection-level Tweet schema. They also worked on converting Tweet data. In Spring 2022, the Twitter Collections team optimized the conversion scripts, converted Tweet data, and looked into implementing a machine learning model to categorize Tweets. In Spring 2024, the Twitter Database Discovery Portal team consolidated the collected data into a local mongo database and built a web app with minimal features that display the collected data and allows the user to search and filter the collections. The Twitter Database Discovery Portal team did not complete extracting the data from the SFM database. Our team’s goal is to build upon the past team’s contributions to finish extracting the data from the SFM database and add new features to the web app.
Building an Intelligent QA/Chatbot with LangChain and Open Source LLMs
Cross, Patrick; Syed, Mikail; Scott, Sean; Singh, Aditya; Zhang, Maokun (2024-12)
This project developed a web-application Q/A chatbot that enables users to interact with Large Language models (LLMs) through a collection format. The system implemented a Retrieval Augmented Generation (RAG) pipeline to provide context-specific responses based on either user-uploaded documents (.txt, .html, and .zip formats) or user uploaded URLs. The application features secure user authentication, multiple- instances of chat/document contexts through collections, document up- load, and standard LLM chatbot functionalities, including the ability to switch between LLMs. This report will give readers an understanding of how the application was designed and developed; how to install and use the application; how to continue development of the application; lessons learned during development; and future plans for the project.
A discovery portal for CS4624 projects
Arze, Henry; Natysin, Logan; Titi, Matthew; Underwood, Patrick (Virginia Tech, 2024-12-12)
CS4624 is one of many capstone courses a student within the CS curriculum is able to take. The multimedia and hypertext course digs into the diverse range of multimedia content such as images, audio, video, and any information retrieval and access relating to it. With this comes the capstone project which is a semester long project given to us students to allow a display of mastery within our discipline. It has been a pleasure to have Dr. Farag’s guidance with the project. An insight into real-world applications as well as a diverse approach to different problems has allowed us to grow as both people and developers. The current discovery portal for CS4624 student projects serves as a platform for students working within the course to submit and hold their projects. Details included within the pages on the discovery portal consist of abstract, date, author, presentation, final report, source code, and collections. Additionally, the discovery portal contains filtering features to allow users to specifically search for any project dependent on; recent submissions, issue semester, author, title, subject, content type, and department. This allows teachers to easily access desired projects as well as a safe holding for semester long projects that students worked hard on. With this comes the purpose of the project. After reviewing the functionality and appearance of the existing discovery portal, there were many things that needed to be improved on. The first step was reworking the backend, where we made a transition from MongoDB to MySQL. This change was necessary in order to support a more scalable and relational database, where project files proved to be large, and MongoDB had limitations in supporting such file sizes. We also transitioned from Node/Express to a Flask backend that could easily interact with the ReactJS frontend. Once our system was structured to our liking, we aimed to repair the core features. The login, project sign-up, and other features were not functioning properly, and required fixes to support distinct user roles. Linking all the existing features between the frontend and backend was essential to the continuation of this project. After analyzing the discovery portal, our main focus became expanding the features for the three user roles: admin, client, and student. Because they share functionality and view collections similarly, we created a reusable home view tailored to each user's permissions. Protected pages were introduced to secure our system, and we redesigned the frontend with Tailwind and ShadCN components for a more modern interface. This overhaul now provides CS4624 students, instructors, and clients with a more efficient, centralized platform for accessing, managing, and preserving semester-long projects, eliminating the need for Canvas or manual entries. Upon completion, we hope to provide future CS4624 students and staff with a more convenient tool to guide them in their journey of completing their capstone projects.

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