The LLM-based Application for Medical Terminology is a cutting-edge software tool designed to enhance the comprehension and utilization of medical terminology by students🎓. By using the capabilities of Large Language Models (LLMs), this application aims to provide real-time insights🕛, translations🈳🔤, and explanations🧑🏫 of complex medical terms and concepts. By integrating advanced natural language processing (NLP) techniques, the application facilitates a more intuitive understanding of medical language🏥🔤, thereby improving the educational outcomes in the medical field.
Discharge of responsibility: Do not use this if you are sick, better go to a doctor :)
The primary aim of the LLM-based Application for Medical Terminology is to significantly enhance understanding by assisting healthcare professionals and students in deciphering and comprehending complex medical terminology without the reliance on extensive manual research. It serves as an invaluable educational tool, enabling medical students and professionals to expand their medical vocabulary and grasp medical concepts in a more user-friendly manner. Furthermore, the application seeks to improve accessibility of medical terminology for non-medical professionals, such as patients and their families, by simplifying medical language into terms that are easier to understand. Through these efforts, the application strives to bridge the knowledge gap in medical terminology, ensuring that both medical and non-medical individuals can communicate more effectively and with greater confidence.
Declare any dependencies in src/requirements.txt for pip installation and src/environment.yml for conda installation.
To install them, run:
pip install -r src/requirements.txt
You can run your Kedro project with:
kedro run
Have a look at the file src/tests/test_run.py for instructions on how to write your tests. You can run your tests as follows:
kedro test
To configure the coverage threshold, go to the .coveragerc file.
To generate or update the dependency requirements for your project:
python -m piptools compile --upgrade --resolver backtracking -o src/requirements.lock src/requirements.txt -v
pip install -r src/requirements.lock
This will pip-compile the contents of src/requirements.txt into a new file src/requirements.lock. You can see the output of the resolution by opening src/requirements.lock.
After this, if you'd like to update your project requirements, please update src/requirements.txt and re-run kedro build-reqs.
Note: Using
kedro jupyterorkedro ipythonto run your notebook provides these variables in scope:catalog,context,pipelinesandsession.Jupyter, JupyterLab, and IPython are already included in the project requirements by default, so once you have run
pip install -r src/requirements.txtyou will not need to take any extra steps before you use them.
To use Jupyter notebooks in your Kedro project, you need to install Jupyter:
pip install jupyter
After installing Jupyter, you can start a local notebook server:
kedro jupyter notebook
To use JupyterLab, you need to install it:
pip install jupyterlab
You can also start JupyterLab:
kedro jupyter lab
And if you want to run an IPython session:
kedro ipython
You can move notebook code over into a Kedro project structure using a mixture of cell tagging and Kedro CLI commands.
By adding the node tag to a cell and running the command below, the cell's source code will be copied over to a Python file within src/<package_name>/nodes/:
kedro jupyter convert <filepath_to_my_notebook>
Note: The name of the Python file matches the name of the original notebook.
Alternatively, you may want to transform all your notebooks in one go. Run the following command to convert all notebook files found in the project root directory and under any of its sub-folders:
kedro jupyter convert --all
To automatically strip out all output cell contents before committing to git, you can run kedro activate-nbstripout. This will add a hook in .git/config which will run nbstripout before anything is committed to git.
Note: Your output cells will be retained locally.