CITS3403-Project

UWA ID	Name	GitHub Username
23976415	Daniyal Qureshi	daniyalq114
23904324	Adam Ahmadavi	aradavi63
23360799	Sam Jackson	samjjacko
21480994	Cam Luketina-Clarke	Drummonddo

LugiAnalytics - a Pokémon Battle Analysis Tool

LugiAnalytics is a web-based tool designed specifically for competitive Pokémon players. The site allows users to:

Input their Showdown username, their team (via a Pokepaste link), and links to battle replays. Automatically extract key data from battle replays—including win/loss results, opponent team details, ELO changes, move usage, and overall team performance. Visualise performance metrics using interactive charts and summaries, helping players review their match history and optimize team strategies.

Features

User Input: Enter your Showdown username. Provide battle replay links for analysis.

Sharing: Share your data with other users.

Data Extraction: A HTML parser processes the replay data to extract game results and performance metrics.

Visual Analysis: Charts and summaries display win/loss ratios, ELO fluctuations, move usage statistics, and more.

Technologies Used: Frontend: HTML, CSS, Bootstrap, JavaScript Backend: Flask (for routing and processing) Database: SQLite (via SQLAlchemy) APIs and Parsing: Custom HTML parser for battle replay data Version Control: Git and GitHub for collaborative development

Running the site

1. Navigate to the directory you'd like to store LugiAnalytics, for instance:

cd Desktop/dev/

2. Clone the repository:

git clone https://github.com/daniyalq114/CITS3403-Project.git

3. Navigate to the project directory:

cd CITS3403-Project/

4. Construct a virtual environment:

python3 -m venv lugi-venv

5. Activate the environment:

   Platform	Shell	Command
   POSIX	bash/zsh	source lugi-venv/bin/activate
   	fish	source lugi-venv/bin/activate.fish
   	csh/tcsh	source lugi-venv/bin/activate.csh
   	pwsh	source lugi-venv/bin/Activate.ps1
   Windows	cmd.exe	source lugi-venv\Scripts\activate.bat
   	Powershell	source lugi-venv\Scripts\Activate.ps1

6. Install dependencies

pip install -r requirements.txt

7. Set up the database

mkdir -p instance # create instance folder if required
flask db init # initialise database
flask db migrate # set up the database to follow schema in models.py
flask db upgrade # apply the migration to the database

8. Set the Flask application environment variable:

   Platform	Shell	Command
   POSIX	bash/zsh	export FLASK_APP=app.py
   Windows	Powershell	$env:FLASK_APP = "app.py"

9. Set the Flask secret key (replace 'your-very-secret-key' with a real random string when deploying):

   Platform	Shell	Command
   POSIX	bash/zsh	export SECRET_KEY='your-very-secret-key'
   Windows	Powershell	$env:SECRET_KEY = "your-very-secret-key"

10. Run LugiAnalytics

flask run

11. Open the returned url, e.g. for * Running on http://127.0.0.1:5000 open http://127.0.0.1:5000 in your browser.

When you're done, exit the virtual environment with deactivate

Running the tests

To run unit tests, run the following command in the root directory:

PYTHONPATH=$PYTHONPATH:. python3 -m unittest tests/unittests.py -v

To run selenium tests, run the following command in the root directory:

PYTHONPATH=$PYTHONPATH:. python3 -m unittest tests/systemtests.py -v

Credits

AI Assistance
Portions of this project were developed with the assistance of AI tools for code suggestions, debugging, and documentation refinement:

• OpenAI's ChatGPT
• Anthropic's Claude
• GitHub Copilot

Images
Pokémon images are sourced from Bulbapedia Archives.
© Game Freak / The Pokémon Company. Usage under Bulbagarden Archives License.

Libraries and Tools

• Google Material Design Icons (MDI) – License
• Google Fonts – License
• Google Charts JS – License
• GLSL Canvas JS – License
• Bootstrap CSS & JS – License