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A Network Tour of Kitchen Science: Innovative Ingredient Replacer using Network Science

Network Tour of Data Science, Fall 2019, EPFL

Context

Have you ever invited vegetarian / vegan friends over for diner and didn't know how to replace the meat in your favorite dish? Or you had to prepare food for dinner with your in-laws and a crucial ingredient suddently has disapeared from your kitchen? Or you wanted your significant other to taste the only recipe you know well but they are allergic to one of the ingredients? This project is for you!

In the context of the EPFL Network Tour of Data Science course (Fall 2019), we proposed to create a tool to remove certain ingredients from a recipe and output the best ingredients to replace them, and suggest ingredients to add to make your recipes even tastier. Our data comes from the Recipe1M+ dataset -- containing over 50,000 recipes from various cooking websites, including ingredients, nutrition facts, preparation instructions, and health scores -- as well as the USDA Nutritional Info dataset.

Live Demo

Quick and minimalistic demo UI to show our final results.

Home Chicken replacement suggestions Smoothie suggestions

Project structure

How to get started

Requirements

To run python scripts (we recommend using venv):

pip3 install -r requirements.txt # install dependencies if needed, using python3

To install/activate conda environment:

conda create --name ntds_kitchen_science --file conda_requirements.txt
jupyter-notebook

Download missing dataset file

Because of github size limitations, we have uploaded the full dataset needed for notebook (1) execution to a public Google Drive. The file can be automatically downloaded to the right folder using the given python script download_dataset.py.

python3 download_dataset.py

The required file will then be downloaded to data/recipes_with_nutritional_info_fixed_qty.json.

Playing with the notebooks!

All notebooks may be executed independently. However, we recommend to follow the order (from 1 to 7) to ensure that all of the necessary files are created in the first notebooks. Notebooks are saved with their execution to save you some time and to direct visualization from github repository.

Authors

License

This project is licensed under the MIT License - see the LICENSE file for details