Inspiration
We noticed that there are so many talented local authors, but people often struggle to find books that match their interests. Toronto has a huge and diverse literary community, yet there’s no simple way to discover authors based on personal reading preferences. At the same time, many local writers don’t get the visibility they deserve.
So we built a tool to bridge that gap—helping readers find books they’ll love and helping local authors get the audience they deserve.
What it does
Page & Page is an interactive quiz-based recommendation platform that suggests Toronto-local authors and books based on a user’s mood, genre preferences, themes, and interests. It creates a personalized “Toronto Shelf” with curated recommendations.
How we built it
We built the frontend using React + TypeScript, styled it with TailwindCSS, and used Lucide-React for clean icons. We designed a multi-step quiz, gathered preference data, and generated recommendations using a custom logic layer prepared for AI integration (Google Gemini). Everything was managed with Git/GitHub.
Challenges we ran into
Accomplishments that we're proud of
What we learned
What's next for Page & Page
Built With
- css
- gemini
- javascript
- react
- tailwindcss
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