MUEats — Miami University Meal Planner

Flask + MongoDB + Selenium web scraper for Miami University dining.

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Quick Start

pip install -r requirements.txt
python app.py
# → http://localhost:5000

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Making the Scraper Work (Required for Live Data)

The scraper drives a real browser to pull today's menu from dineoncampus.com/MiamiUniversity. It needs Firefox.

Insure FireFox is installed

# macOS
brew install firefox

# Ubuntu/Debian
sudo apt install firefox

# Windows — download from https://www.mozilla.org/firefox/

geckodriver is downloaded automatically by webdriver-manager.

Running the scraper

Automatic — the app checks on startup if today's data is loaded. If not, it scrapes automatically in the background. It also re-scrapes every night at midnight so data is always current.

Manual from dashboard — go to the Scraper page in the sidebar → pick today's date → click Run Scraper Now.

Manual from command line:

# Scrape today (default)
python scraper_main.py

# Scrape a specific date
python scraper_main.py 2026-04-30

# Run headless (no browser window)
python scraper_main.py --headless

# Via the API
curl -X POST http://localhost:5000/api/scraper/run \
     -H "Content-Type: application/json" \
     -d '{"date":"2026-04-30"}'

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MongoDB Setup

Runs without MongoDB by default (in-memory storage). To enable MongoDB persistence:

1. Copy .env.example to .env and set:

   USE_MONGO=true
   MONGO_URI=mongodb://localhost:27017/
   

2. All MongoDB queries are pre-written in app.py as comments — just uncomment.

Collections

Collection	Holds
users	Email, name, hashed password, macro goals
meals	Dining hall items + macros + scraped date
meal_log	Per-user daily meal selections

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API Reference

Method	Endpoint	Description
GET	/	Login page
GET	/dashboard	Dashboard UI
POST	/api/auth/login	Login (Miami email only)
POST	/api/auth/signup	Create account (Miami email only)
POST	/api/auth/logout	Log out
GET	/api/user	Current user + goals + consumed today
PATCH	/api/goals	Update macro goals
PATCH	/api/body	Update body info (height/weight/age)
GET	/api/recommendations	BMR/TDEE-based macro recommendations
GET	/api/meals	All meals, scored + filtered
GET	/api/restaurants	List of dining hall names
GET	/api/periods	List of meal periods
GET	/api/suggestions	Top green-tier matches for user
GET	/api/meal_log	Today's logged meals
POST	/api/meal_log	Log a meal
DELETE	/api/meal_log/<id>	Remove a logged meal
POST	/api/scraper/run	Trigger scraper for a date
GET	/api/scraper/status	Scraper data status + item counts
GET	/api/stats	Live progress stats

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Tier Scoring

Each meal is scored 0–100 against 1/3 of the user's daily goals:

Macro	Weight
Protein	40%
Calories	30%
Carbs	20%
Fat	10%

• Green ≥ 80% — Best Match
• Yellow 50–79% — Moderate
• Red < 50% — Low Match

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Collaborators and Contributions

• Sage Tipton - Web Scraping
• Dru Frazier - Flask + Mongo
• Donovan Davis - Front End