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NTNU Drinks Project

Course Information

This project is part of the Internet Programming course taught by Professor Ling-Jung Wu. The course focuses on both theoretical and practical aspects of how clients connect to servers, including Echo Servers and Web Servers. Students will learn the fundamentals of internet programming, set up web servers, and develop applications involving client-server interactions. By the end of the course, students are expected to build their own network-based services utilizing the concepts covered in class.

Course Objectives

  1. Understanding Client-Server Communication: Learn how clients interact with web and echo servers.
  2. Hands-On Web Server Deployment: Gain experience in setting up web servers and managing requests.
  3. Internet Programming Implementation: Write server-side and client-side applications.
  4. Developing a Functional Network Service: Apply the knowledge gained in the course to develop a real-world network service by the end of the semester.

Overview

The NTNU Drinks project aims to solve the problem of overwhelming drink choices by providing users with a personalized beverage selection experience. This project integrates several functionalities into a web-based platform:

  • Personalized Drink Preference List: Users can save and manage their favorite drinks.
  • Allergen Filter: Helps users avoid drinks containing specific allergens.
  • Google Maps Integration: Displays nearby beverage stores with relevant information.

Website Link

NTNU Drinks

Features

  • User-Friendly Interface: A simple and interactive design for seamless navigation.
  • Customization: Users can create and update their personal drink lists.
  • Health & Safety: Allergen filtering to prevent unwanted ingredients.
  • Location-Based Services: Integration with Google Maps for store discovery.

Technologies Used

  • HTML
  • CSS

Installation Guide

Steps to Run Locally

  1. Download or clone the repository.
  2. Open index.html in a web browser.

Future Improvements

  • User Authentication: Implement login and account management.
  • Drink Recommendations: AI-powered suggestions based on preferences.
  • Order Integration: Connect with delivery services for online ordering.

About

This project aims to solve the problem of overwhelming drink choices by providing users with a personalized beverage selection experience. This project integrates several functionalities into a web-based platform: Personalized Drink Preference List, Allergen Filter, and Google Maps Integration.

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