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A system that suggests personalized recipes based on user preferences, dietary restrictions, and available ingredients. Based on personalized and non personalized recommendation based on cosine,jaccard similarities, item-item collaborative filtering

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varshanipreddy/fridge2table

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Fridge2Table

A simple recipe recommender :)

Installation

Project Setup

  1. Clone the Repository
  2. Install Python3
  3. Go to Project Folder and run virtual env: virtualenv .venv, source .venv/bin/activate
  4. Install Django in virtual env: pip install --upgrade pip, pip install django
  5. Install requirements: pip install -r requirements.txt
  6. Follow the Database Setup Instruction
  7. To create migrations: python manage.py makemigrations, python manage.py migrate
  8. To make static files load, run : python manage.py collectstatic
  9. To run the application: python manage.py runserver

Postgresql Database Setup

  1. Install Postgresql and setup your postgres user.
  2. Run application pgAdmin
  3. Create a Database : fridge-2-table under Databases
  4. Enter all the DB Details in the .env file or settings.py file
  5. If migrations give error, trying changing password in the .env file to the one that you set when you started the pgAdmin application.

Core

  1. Place you csvs in main/core/csv
  2. Modify scripts in main/core
  3. Code work done in main/core/work

Deployment

  1. Create a DO account and make a droplet
  2. Install the repository as usual
  3. Configure nginx and gunicorn to serve

Logging

  1. To enable logging, uncomment the Logging code in env file and set DEBUG = True
  2. To use logger to log details, add this lines to the top of your file:
    import logging
    logger = logging.getLogger(__name__)
  3. To use it, write logger.debug('Add what you want to print')

Design

Design

Result

result

About

A system that suggests personalized recipes based on user preferences, dietary restrictions, and available ingredients. Based on personalized and non personalized recommendation based on cosine,jaccard similarities, item-item collaborative filtering

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