Meal Planner App

The Meal Planner app is a Flask-based application that uses chatgpt to generate meal plans based on user preferences and dietary restrictions. The app also allows users to add new personal information, such as their height, weight, and activity level, to create personalized meal plans.

Technology

The Meal Planner app is built using the following technologies:

Python 3.11
Flask 2.2.2
Celery 5.2.7
Redis 4.5.1
JSON

Getting Started

To get started with the Meal Planner app, follow these steps:

Clone the repository to your local machine using the command:

  git clone https://github.com/MDGrey33/nutritionAI.git

Create a virtual environment using Python 3.11:

  python3.11 -m venv venv

Activate the virtual environment:

  source venv/bin/activate

Install the required packages by running:

  pip install -r requirements.txt

Start the Redis container by running:

  docker run --name some-redis -p 6379:6379 -d redis

Start the Flask server by running:

  flask --debug run --port=5001

Start the Celery worker by running:

  celery -A app.infra.app_celery.celery worker --loglevel=info

You can now access the app by going to http://localhost:5001.

find the postman collection in nutrition.postman_collection.json in the root directory.

Main Functionality

The Meal Planner app has the following main functionality:

Allows users to add new personal information, such as their height, weight, and activity level, to create personalized meal plans.
Uses chatgpt to generate meal plans based on user preferences and dietary restrictions.
Allows users to generate new meal plans.
Allows users to generate recipes for the meals in the plans.

Contributing

We welcome contributions to the Meal Planner app! To contribute, please fork this repository and submit a pull request with your changes.

Here are some areas that we are particularly interested in improving:

Error handling and input validation
File management and object-oriented programming
Parallelizing the generate_recipes function
Creating a mobile app to view meals and submit them to health apps
Implementing Google authentication
Training the Ada model for use in generating meal plans (instead of relying on the Davinci model)

If you are interested in training the Ada model, please reach out to us for more information on how to get started. Thank you for your interest in the Meal Planner app!

Todo list

Implement error handling
Implement input validation
split generate recipe into multiple parallel celery tasks
Pull file management to one package
pull meal from json to object
pull meal plan to object
pull recipe to object
Make mobile app to
1. Show meals
2. Submit to health app
Implement google authentication for

MDGrey33 / nutritionAI