Repository for performing analysis and visualising our scraping results
After cloning this repository, there are two options to deploy the streamlit application: via Docker or directly on your machine.
Create a venv in the folder of the repository using:
python -m venv venv
To activate the venv, enter the following command:
venv\Scripts\activate if you're on Windows
source venv/bin/activate on Unix
Afterwards, install all requirements:
pip install -r requirements_linux.txt on Unix
pip install -r requirements_windows.txt on Windows
Also, make sure you have set the following environment variables:
- MONGO_HOST (hostname of the MongoDB instance where the data is fetched, the standard port 27017 is assumed)
- MONGO_INITDB_ROOT_USERNAME (username for the MongoDB instance)
- MONGO_INITDB_ROOT_PASSWORT (password for the MongoDB instance)
Then, the application can be deployed on the host machine with the following command:
streamlit run app.py
A window in your standard browser pops up displaying the dashboard. The website is available under http://localhost:8501/
First you have to build docker image with the command docker build -t analysis .
Afterwards, you can start the container with the command
docker run -e MONGO_HOST=<mongodb_host> -e MONGO_INITDB_ROOT_USERNAME=<username> -e MONGODB_INITDB_ROOT_PASSWORD=<password> -p 8501:8501 analysis
The streamlit dashboard is then accessible under <hostname>:8501.
All analyses are implemented in the classes contained in the analyzers module. They all correspond to the data source. To make the analysis available to the user, it has to be registered in the constant ANALYSES_BY_DATA_SOURCE. Here you give the name of the method as the key and a description for the user as the value.