Structure

The project is divided into:

• Client: It contains the implementation of dynamic dashboards using libraries such as Streamlit, Plotly, etc.

• Server: Api that is consumed to obtain the necessary resources to supply the client. Api that is consumed to obtain the necessary resources to supply the customer. The Api is implemented using the FastApi python framework

• Data: This directory contains the csv file with the statistics for each Euro 2020 match and a Jupyter Notebook in which the data is processed and cleaned and then stored in a database.

Installation

First you will need to have Python installed, you can find some guides to help you Guides

Clone the repository

git clone https://github.com/stevenmontoya/scout-soccer-project.git

Use the command below to install the project dependencies

cd scout-project
pip install -r requirements.txt

Install Jupyter

cd scout-project
conda install -c conda-forge jupyterlab

Run Locally

Run Jupyter

Go to the data directory

cd data
jupyter notebook

In Jupyter you can run all cells to process and perform data cleansing. They can also be saved in a database by configuring environment variables.

Be patient, data processing may take a few minutes.

Run Client

Start the Streamlit server

Go to the client directory

cd client

Start the Streamlit server

streamlit run main.py

Run Server

cd server
cd src

Start the Streamlit server

uvicorn main:app --reload

Environment Variables

To run this project, you will need to add the following environment variables to your .env file

FOOTBALL_DATA_API_TOKEN

MONGODB_URL

• To add some interesting data, requests are made to the following api football-data, you can get an api token for free

• You can add a connection url to Mongodb (on-premises or in the cloud)

Screenshots

Dashboard

Navigation

Teams

Compare

License

MIT