Recipe Recommendation App
Show all 14 photos
Jun 2018Mobile

Recipe Recommendation App

MobileFull StackAPI Integration

A cross-platform mobile app that recommends food recipes based on customer restaurant reviews, integrating Google APIs for review data and restaurant search.

2
Platforms (iOS & Android)
Google
API Integration
Group
R&D Project
Flask
Python Backend

Problem

People often enjoy dishes at restaurants but struggle to find equivalent recipes at home. There was no simple way to bridge the gap between what customers loved at a restaurant (captured in reviews) and actionable recipe recommendations tailored to their tastes.

Approach

Built a cross-platform mobile application using Ionic on top of Angular, backed by a Flask Python API. Google API services were integrated to fetch customer restaurant reviews and enable restaurant search. The backend processed review sentiment and preferences to generate recipe suggestions.

Context

A group research project assessing full-stack software development skills. The app analyses a user's restaurant review history and preferences to recommend personalised food recipes — connecting dining experiences with home cooking inspiration.

Capabilities

What we built into the product

Review Analysis

Pulls restaurant reviews via Google APIs to understand preferences

Recipe Matching

Maps dining preferences to personalised recipe suggestions

Cross-Platform

Single Ionic codebase targeting iOS and Android

Restaurant Search

Integrated Google Places for discovering dining options

Stack

IonicAngularTypeScriptPythonFlaskGoogle APIsMySQL

Notes

Architecture The frontend was built with **Ionic**, a framework on top of Angular that enables a single codebase to target both iOS and Android. This was an early exercise in cross-platform mobile development before React Native became mainstream. The Flask backend handled the core recommendation logic — ingesting review data from Google API services, processing user preferences, and returning matched recipes. MySQL stored user profiles and cached recommendation results. The project demonstrated end-to-end ownership from API integration through to polished mobile UI.

Related case studies

PizzaNow — Online Pizza Delivery

Case study: PizzaNow — CodeIgniter e-commerce app for online pizza ordering with MVC architecture and AJAX-driven cart management.

Cardiac Hypertrophy Diagnosis

Case study: Cardiac Hypertrophy Diagnosis — final-year ML research project using Random Forest, CNN, Angular, and Flask to predict heart conditions from ECG data.

IMPERIUM — Smart Energy IoT

Case study: IMPERIUM — IoT energy monitoring system using Arduino and a web dashboard to help households and SMEs reduce electricity bills.

Recipe Recommendation App | Naveen Fernando