Team Blockbuster - Top Movies API

BU MET CS673 O1: Software Engineering, Fall 2024 - Team 6

Project Description

Team Blockbuster is creating a microservice Data Science application which will provide the ability to query and display movie data based on various user inputs using the Kaggle IMDB Top 250 Movies dataset: https://www.kaggle.com/datasets/rajugc/imdb-top-250-movies-dataset

Motivation

This application provides information for people who are curious about movie trends such as movies ratings across different countries, movie durations, or specific movie genres and categories, etc. This information can help people working in the film industry or similar industries in making important movie development choices.

Framework

This microservice based application is built using a Python Flask framework where ReactJS will be used to create a frontend web-page where the user can input data points and Python will be used to manage backend operations that generate the movie data based on the input data received. SQLite will be used as the application's database.

Overview:

• Frontend: ReactJS
• Backend: Python
• Database: SQLite

Tools Used

The following technologies will be used for our project:

• PyCharm Community (2024.2.2)
• GitHub Actions
• Python (v3.12)
• pip (v24.2)
  • Python Flask
    • Flask-Cors
  • PyTest
    • Pytest-mock
  • PyLint
  • Pandas
• Docker (v27.2.0)
  • Docker Compose (v2.29.2-desktop.2)
• Podman (5.2.2)
• nodeJS (v20.17.0)
• SQLite (v3)

Installation and Set Up

Please install the specific versions mentioned in the Tools Used section above.

PyCharm Community

Please follow download and installation instructions from the link: https://www.jetbrains.com/pycharm/download/ depending on OS.

Docker

Please follow download and installation instructions from the link: https://www.docker.com/ depending on OS.

Docker Compose

Docker Compose is part of the Docker installation process.

nodeJS

Please follow download and installation instructions from the link: https://nodejs.org/en depending on OS.

Python

Please follow download and installation instructions from the link: https://www.python.org/ depending on OS.

pip

pip should be installed with the Python downloaded from https://www.python.org/.

Python Flask

Using the Python version installed along with pip, please follow the instructions at the link: https://flask.palletsprojects.com/en/3.0.x/installation/.

Pandas Using the Python version that is installed along with, please use pip to install: Pandas pip3 install pandas

SQLite

sqlite3 is part of the Python library when Python was downloaded and installed.

For more information please refer to this link: https://docs.python.org/3/library/sqlite3.html

How to Run Software

1. Open a command line console and clone the main branch of this repository (SSH approach) in location of choice using the command below:

   git clone [email protected]:BUMETCS673/seprojects-cs673olf24team6.git

   Note: SSH approach was used for the command above. Please follow the appropriate GitHub guidelines to set up SSH keys before cloning this repository.

2. Using either file manager or a terminal window navigate to the code directory.

3. To run this application the services must be started using docker-compose 'docker-compose-up'

How to Run Tests

Frontend

1. From frontend directory
2. Podman/Docker build -t node_frontend .
3. Podman/Docker run -idt --name node_frontend node_frontend
4. Podman/Docker exec -it node_frontend /bin/bash
5. npm test

Backend

1. From backend directory
2. Podman/Docker build -t flask_backend .
3. Podman/Docker run -idt --name flask_backend flask_backend
4. Podman/Docker exec -it flask_backend /bin/bash
5. pytest tests/*.py

Database

1. From database directory
2. Podman/Docker build -t database .
3. Podman/Docker run -idt --name database database
4. Podman/Docker exec -it database /bin/bash
5. cd tests
6. pytest movies_unit_test.py

Credits

Course Instructor: Yuting Zhang

Facilitators: Steve Chin, Trevor Michelson

Tools Installation Information

• Docker: https://www.docker.com/
  • Docker Compose
• Podman
• nodeJS: https://nodejs.org/en
• Python: https://www.python.org/
  • pip
  • flask
  • pandas
  • pylint
• SQLite: https://docs.python.org/3/library/sqlite3.html

Dataset Information

• Kaggle IMDB Top 250 Movies dataset: https://www.kaggle.com/datasets/rajugc/imdb-top-250-movies-dataset,
• By Chidambara Raju G (2022)

Meet the Team

Team Leader: Joshua Shilts

Requirements Leader: Elizabeth Tyree

Design and Implementation Leader: Elizabeth Tyree

Configuration Leader: Ricky Zheng

QA Leader: Alex Flinchum

Security Leader: James Zheng

Contributors Information