Project Info
Inspiration
We wanted to create a tool that not only makes studying productive but also rewarding. Inspired by the Tamagotchi style, StudyWithKat enhances the learning experience to be interactive and motivating, allowing students to incentivize their learning.
What it does
StudyWithKat is a Tamagotchi-style education app that transforms studying into a game. Users can earn coins by completing learning inspired tasks such as creating flashcards, taking quizzes, and using the focus mode. The coins can be used to take care of your interactive virtual cat, which reflects the user’s effort towards studying with Kat.
How we built it
We built StudyWithKat using HTML, CSS, and JavaScript to create the frontend. The backend was built with Flask to send and receive information from Firebase, bridging our frontend to our database. Firebase was used for data storage and authentication, while the ChatGPT API is used for creating flashcard generation from file imports, quizzes, and daily tasks.
Challenges we ran into
We encountered challenges connecting our HTML frontend to our Firebase database. This obstacle slowed our progress and ultimately prevented us from implementing some of the additional features we wanted to include. This experience provided us with insight into database integration and helped us understand the importance of preparing for potential connectivity issues.
Accomplishments we're proud of
While developing StudyWithKat, we took pride in planning early and monitoring tasks with a well-organized task list. Having a clear structure allowed us to make progress promptly. Completing the frontend felt like a major accomplishment, as it was one crucial component to establishing the app.
What we learned
As our codebase became more complex, we realized the importance of outlining our plans for the frontend and backend components. Keeping active communication between both sides was vital to ensuring that features were implemented effectively. This taught us that efficient collaboration and planning are just as crucial as writing code when it comes to developing a functional app.
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Analysis
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Metric
No commits on this project resolved to a GitHub account.
Technology
- CSSIn code
- FlaskIn code
- HTMLIn code
- JavaScriptIn code
- OpenAIIn code
- PythonIn code
- FirebaseClaimed
6 of 7 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
123 KB
Source files
20
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
xushengou/2x2
40 files · 1.3 MB · @ 94b40a1
Structure
Interface
5 files · 13%Screens, components and styles rendered to the user.
Application logic
1 file · 3%Domain rules, services and shared utilities.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- JavaScript54%
- HTML19%
- Python14%
- CSS13%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
requirements.txt
pypi · 7- firebase-admin
- Flask
- flask-cors
- gunicorn
- openai
- python-dotenv
- requests
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
This project’s features have not been analysed yet.
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