amitadate / EECS-337-NLP-Project-02

Repository for projects undertaken in the course ' Natural Language Processing ' at Northwestern University

EECS-337-NLP-Project-02

Repository for projects undertaken in the course ' Natural Language Processing ' at Northwestern University

Team Members:

1. Amit Adate @amitadate
2. Omkar Satpute @omkarstpt
3. Mayank Malik @mayankmalik01
4. Aditya Kumar @adijays17

How to run:

1. To run the code, clone the repo and navigate to Final_Submission folder
2. Create a virtual environment if needed and activate the environment
3. $ pip install -r requirements.txt
4. Run main.py (to get a menu interface)

Note - In the menu, scroll up to see results displayed or use terminal in fullscreen mode

Menu

0. ----> To view scraped data, Ingridients, Nutrition and Methods

1. ----> Transform to Healthy

2. ----> Transform to Non-Healthy

3. ----> Transform to Vegetarain

4. ----> Transform to Non-Vegetarian

5. ----> Transform to Vegan

6. ----> Transform to Chinese

7. ----> Transform to Indian

8. ----> Transform to Mexican

9. ----> Transform to Italian

Preprocessing

Firstly, we started with Exploratory Data Analsis of most common ingredients used in top recipes of the world. The code for this purpose can be found in Final_Submission-> PreProcessing. Using the PreProcessing statistics, we created the list of most common ingredients and their substitution found in healthy, non healthy, veg, non-veg recipes, Chinese, Indian, Mexican, and Italian recipes.

Ingredients, Nutritions, and Methods

Below is the list our parser recognizes:

Ingredients - name , quantity , measurement (cup, teaspoon, pinch, etc.) , descriptors

Nutritions - Protein, Carbs, Sodium into High, low, normal

Methods: Primary cooking method (e.g. sauté, broil, boil, poach, etc.) Secondary cooking method (e.g. chop, grate, stir, shake, mince, crush, squeeze, etc.) Tools – pans, graters, whisks, etc. Steps – parse the directions into a series of steps that each consist of ingredients, tools, methods, and times

The code for this section can be found in Final_Submission-> data_extractor.py

Transformations

We have given the following options to a user to choose from:

1. Transform to Healthy
2. Transform to Non-Healthy
3. Transform to Vegetarain
4. Transform to Non-Vegetarian
5. Transform to Vegan
6. Transform to Chinese (Includes Chinese Utensils)
7. Transform to Indian (Includes Indian Utensils)
8. Transform to Mexican
9. Transform to Italian

The code for this section can be found in Final_Submission-> transformer.py