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Project Info

Tailwind

Devpost

Inspiration

Have you ever spent hours if not days studying material for an exam only for the exam to be nothing like the study material. We have countless times and we wanted to come up with a solution to provide the perfect study material and extra practice directly based on the professors lectures.

What it does

Tailwind creates study material directly based on professor's materials using RAG and AI to prepare students for exams. Students can submit their professors lecture notes, number of questions, and specific topics they want covered, and Tailwind creates practice problems from this information and then analyzes the answers to see how correct they are and gives feedback on the user's answers.

How we built it

We made this using a flask, html and css website which calls gemini to extract text from pdfs or images and then breaks that text into chunks which we use chromadb (a RAG framework) to query and get the most related information to a specific question, we then call gemini again to generate questions regarding the topics, and then collect answers from the user. We call gemini one last time to provide feedback on the responses and answer any followup questions the user may have.

Challenges we ran into

We had issues with data formatting, indexing, flask, html, and debugging. Neither of us were very familiar with html integration with Flask so we had issues trying to figure out how things translated between the languages of html and python. We also had limited experience with the gemini api so understanding how it worked and making it do what we wanted was a little difficult. When using the gemini api, it took a long time to generate questions and answers so debugging it was difficult as we had to wait a long time when trying to figure out what we did wrong.

Accomplishments we're proud of

Finishing with a fully functional AI powered tool that can help with studying for all people. Becoming much better at HTML and flask. Gaining experience integrating AI into web development

What we learned

We got more familiar with Web Development, using AI like Gemini, and database management.

What's next

We are going to add a special user interface for True/False and multiple choice questions. We are also going to improve the AI model of the chatbot used to answer further questions AI answer feedback. We also want to optimize our use of gemini so we get faster responses as our current ones are a little slow.

Analysis

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Metric

Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • CSSIn code
  • HTMLIn code
  • PythonIn code
  • FlaskClaimed
  • Google GeminiClaimed

3 of 5 appear in the indexed code. 2 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

24 KB

Source files

8

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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