NutriNavigator

NutriNavigator is a project aimed at providing nutritional recommendations by taking users age height weight disease they have, with full e-commerce features . This README will guide you through setting up the project locally.

Setting Up Environment Variables

Developers must create a config.env file inside /backend/config/ directory with the following environment variables:

PORT=your_preferred_port
MONGODB_URI="mongodb_database_url"
SALT=length_of_generated_salt
JWT_SECRET="random_string"
JWT_EXPIRE="jwt_token_expired_day"
COOKIE_EXPIRE=cookie_expire_day
SMTP_SERVICE="gmail"
SMTP_PASS="a_random_string_given_by_gmail"
SMTP_MAIL="your_mail_in_which_you_have_app_permission"
STRIPE_API_KEY=api_key_goes_here
STRIPE_SECRET_KEY=secret_key_goes_here
SMTP_HOST="smtp.gmail.com"
SMTP_PORT=465
CLOUDINARY_NAME=cloudinary_given_name
CLOUDINARY_API=your_identity_key
CLOUDINARY_API_SECRET=secret_key_goes_here
FRONT_END_URI=localhost:3000

Installing Packages

Node Server

Navigate to the root folder of NutriNavigator in your terminal and execute:

npm i

ReactJS App

Navigate to NutriNavigator/frontend folder and execute:

npm i

Ignore any warnings during installation.

Flask Server

Navigate to /NutriNavigator/mlServer folder in your terminal.

Create a folder using the command:

mkdir models

Then install the required Python packages listed in requirements.txt:

pip install -r requirements.txt

Recommender System Model Build

After installing the required packages. Navigate to /NutriNavigator/mlServer folder in terminal.

Create folder called models

Inside the /NutriNavigator/mlServer folder create a folder models to store the trained model on our food_recommendation dataset. execute :

mkdir models

After creating the folder models then run the file food_recommender.py by below command :

for windows

python food_recommender.py

for linux (with installed python version 3.X)

python3 food_recommender.py

After running the food_recommender.py check in the created models folder, a file may be created called model_pickle. Just check this out.

Running the Servers

After doing previous steps successfully, we are going to run our NutriNavigator web application.

Node Server

Navigate to the root folder and execute:

npm run dev

Python Flask Server

Navigate to /NutriNavigator/mlServer folder and execute:

For Windows:

python server.py

for linux (with installed python version 3.X)

python3 server.py

Running the ReactJS App

Navigate to /NutriNavigator/frontend folder and execute:

npm start

Accessing NutriNavigator

After successfully running the servers, you can access the NutriNavigator web application at http://localhost:"your_given_port".