Cafe Bridget-Olivia

A full-stack web application helping students find study-friendly coffee shops near Northeastern University with data-driven recommendations based on real reviews and ratings.

Features

• Smart Cafe Discovery: Searches cafes within customizable radius using Google Places API
• Review Analysis: Extracts and analyzes real user reviews to identify study-friendly features
• Detailed Cafe Profiles: Ratings, price levels, hours, and authentic user feedback
• Location-Based Search: Centered on Northeastern University campus with 2km default radius
• Data Extraction Pipeline: Automated system for gathering and processing cafe data

Built With

• Frontend: React
• Backend: Python
• APIs: Google Places API (Nearby Search & Place Details)
• Data Processing: Python requests, JSON storage
• Environment Management: python-dotenv

How It Works

1. Data Collection: Python script queries Google Places API for cafes near NEU
2. Detail Extraction: Fetches comprehensive info including reviews, ratings, and hours
3. Review Processing: Analyzes user reviews to identify study-friendly attributes
4. Storage: Saves structured cafe data to JSON for frontend consumption
5. Visualization: React interface displays cafes with relevant study metrics

Getting Started

Prerequisites

• Node.js v16+
• Python 3.8+
• Google Places API key

Installation

# Clone the repository
git clone https://github.com/hill-ol/cafe-bridget-olivia.git
cd cafe-bridget-olivia

# Install frontend dependencies
npm install

# Install backend dependencies
pip install -r requirements.txt

# Run the application
npm run dev          # Frontend
python app.py        # Backend

API Setup

1. Get a Google Places API key from Google Cloud Console
2. Enable Places API and Places Details API
3. Add key to .env file as shown above

Future Enhancements

• Real-time check-in system to track cafe occupancy
• Aesthetic matching algorithm based on review sentiment analysis
• Study-friendly filters (WiFi, outlets, noise level)
• User-generated ratings and tips
• Mobile-responsive design
• Expanded search area beyond Northeastern

Data Sample

The application currently tracks cafes with:

• Average 5+ reviews per location
• Rating data from 1-5 stars
• Price level indicators
• Operating hours
• User-written reviews for context

Author

Bridget Crampton LinkedIn | crampton.b@northeastern.edu

Olivia Hill
LinkedIn | hill.ol@northeastern.edu