/tv-maze

TV Maze App is a movie suggesting app that gets data from the tv maze API and displays the data along with the specific film still (images). It also allows you to add comments and likes to the specific shows displayed.

Primary LanguageJavaScriptMIT LicenseMIT

TV Maze

JavaScript Capstone Project

📗 Table of Contents

📖 [TV Maze App]

[TV Maze App] gets data from the tv maze API and displays the data along with show images. It also allows you to add comments to the specific shows displayed. All data is preserved thanks to the external TV MAZE API.

🛠 Built With

Tech Stack

Client
Technologies
Server
Database

Key Features

  • [Medium-fidelity Wireframes]
  • [Desktop Version]
  • [Mobile Version]
  • [Dynamic Design]

🎥 Video Explanation

Click here for the video explanation about how the app works

🚀 Live Demo

Coming Soon

💻 Getting Started

Prerequisites

Understand HTML/CSS and JavaScript

Install

In order to run this project you need to have the following:

  • A code editor (preferably VSCode)
  • A browser
  • Node.js (to run javascript files locally)
  • LiveServer (to load javascript modules)

Setup

To get a local copy up and running follow these simple example steps:

  • Open git bash on local computer
  • clone the repo with:
git clone  <https://github.com/0sugo/tv-maze.git>
  • run cd tv-maze to enter the project folder
  • run npm i to install all dependencies
  • run npm run build to bundle the project with webpack, and
  • run npm start to launch the application

👥 Authors

👤 JOSECK OSUGO

👤 ROSE MUTAI

🤝 Contributing

Contributions, issues, and feature requests are welcome!

Feel free to check the issues page.

🔭 Future Features

[Feature-1]

  • Add more styling,animation and transormations to improve the feel of the site

[Feature-2]

  • Add search functionality

⭐️ Show your support

If you like this project, kindly leave a comment below and share it with someone who enjoys coding! Coding is all about continuous learning and allowing yourself to be a beginner. Keep going!

🙏 Acknowledgments

I'm thankful to Microverse for providing a study platform which guided me through this project and to my coding partners at Microverse for the collaborative effort.

📝 License

This project is MIT licensed.

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