A brief description of your project, its purpose, and goals.
The project was tested on a Fedora 41 machine, and the following instructions are for a Linux environment. The project should work on other operating systems as well, but the installation process might differ.
To install the required Python packages, run the following command in your terminal:
pip install -r requirements.txtMake sure you are using python version 3.12.8, we recommend using a virtual environment or a conda environment to avoid conflicts with other projects.
Data imputation was performed via spark for added scalability. The spark environment was set up based on the https://github.com/cnoam/spark-course repository. Our version is attached in the privately provided OneDrive link alongside the data. After running the docker container, you can assign it as the remote Jupyter kernel in your IDE and use it to run the data imputation notebook.
Alternatively, you can try to run the notebook on Databricks, but that would require tweaking the code to properly read the data, which you would have to upload to the Databricks workspace.
If you want to run the app without any other code, you can install the docker image from the following commands:
sudo docker build -t datalab-app .After the image is built, you can run the app with the following command:
sudo docker run --rm -p 8501:8501 datalab-appAs per the project requirements, the data is not publicly available. The data will be provided in a privately shared OneDrive link. In the link each data folder will be named in a format data-{path}, where the path is where to put the data folder in the project (and rename it to just "data").
To run the app from the main directory of the repository, run the following command:
streamlit run "Description Improver"/desc-improver-app/app.py