AnimeWatchList

AnimeWatchList

AnimeWatchList is a comprehensive web application developed as a school project to demonstrate full-stack development skills and relational database design. The platform allows anime enthusiasts to track their viewing progress, organize shows by genres, rate completed series, and discover new content based on their preferences.

Duration

2 months

Team

2 People

Adonias DanielDavid Newman
Role

Software Engineer

1000+ anime show entries

Anime Database Size

20+ users registered (classmates)

User Registrations

500+

Total Watchlist Entries

<100ms

Database Queries/sec

Key Features

Core functionality and capabilities that make this project unique

User authentication and profile management

Anime database with detailed information

Personal watchlist tracking (watching, completed, plan to watch)

Episode progress tracking

Rating and review system

Genre-based filtering and search

Recommendation engine based on viewing history

Statistics dashboard for viewing habits

Technology Stack

Technologies and tools used to build this project

frontend

HTML5
CSS3
JavaScript
Bootstrap
J
Jinja2

backend

Python
Flask
SQL
Flask

database

SQL
SQL

deployment

H
Heroku
Python

tools

GitHub
VS Code
SQL

System Architecture

Traditional MVC architecture using Flask framework with server-side rendering and SQLAlchemy ORM for database operations.

Flask Application

Main web application handling routes, authentication, and business logic

PythonFlaskJinja2

SQLAlchemy ORM

Database abstraction layer with models for users, anime, and watchlist data

SQLAlchemyFlask-SQLAlchemy

Authentication System

User registration, login, and session management

Flask-LoginWerkzeug

Challenges & Solutions

Key obstacles encountered during development and how they were overcome

Challenges

Designing normalized database schema for complex anime data

Implementing efficient search and filtering functionality

Creating intuitive user interface for list management

Handling large datasets for anime information

Implementing recommendation algorithm

Solutions

Designed comprehensive ERD with proper relationships

Implemented full-text search with SQLAlchemy

Created drag-and-drop interface for list management

Used pagination and lazy loading for performance

Built collaborative filtering recommendation system

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