StudySpots

A café discovery prototype for choosing a place to study, work, or meet friends. A React interface pairs amenity filters and café detail pages with a Flask REST API and a small SQLite demo dataset.

Features

• Search by café name, city, or address.
• Filter for Wi-Fi, outlets, quiet spaces, parking, groups, cozy spaces, and late hours.
• Browse featured cafés and detail pages with photos, amenities, ratings, and reviews.
• Save favorites in browser storage and submit reviews to the API.
• Switch between a card grid and an illustrative map panel.

Architecture

flowchart LR
  UI[React + Vite + Tailwind] --> API[Flask REST API]
  API --> DB[(SQLite)]
  Seed[seed.py demo records] --> DB
  UI --> Favorites[Browser localStorage]

Loading

Run locally

git clone https://github.com/OmElMon/StudySpots.git
cd StudySpots/backend
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python seed.py
python app.py

On Windows, activate with .venv\Scripts\activate instead. The API listens on port 5001. The seed command deletes and replaces all cafés and reviews in the local database.

In a second terminal:

cd StudySpots/frontend
npm ci
npm run dev

Open localhost:5173. Set VITE_API_BASE to change the default API base, http://localhost:5001/api.

API

Method	Route	Purpose
GET	/api/health	Service health
GET	/api/cafes	Search and filter
GET	/api/cafes/featured	Featured records
GET	/api/cafes/<id>	Details with reviews
GET / POST	/api/cafes/<id>/reviews	Read / add reviews

curl "http://localhost:5001/api/cafes?city=Seattle&quiet=true&wifi=true&outlets=true"

The late-hours filter key is openLate. Filters are combined by the backend and results are sorted by study score and hangout rating.

Validation and limits

The repository provides npm run build and npm run preview in frontend/, but has no automated test suite or GitHub Actions runs at the time of this documentation audit. Installation and end-to-end operation have not been rerun for this README update.

The dataset contains six demo café records with stored study scores. Scores are not calculated dynamically. The map positions markers by array index rather than geographic coordinates; café coordinates are available for a future map integration. “Near me” bypasses the city filter and does not use browser geolocation. Reviews have no user authentication or moderation, and numeric rating validation needs further work before public use.

No live deployment or license file is included in the repository.