---

Grass - Grocery Assistant

A fullstack grocery assistant application with integrated AI reccomendations and order processing.

---

---

Code Structure

Our system is based on the three-tier architecture model. The Presentation Layer consists of HTML, CSS, and React. The Logic Layer is implemented with Next.js and Python. Finally, the Data Layer is stored in a NoSQL MongoDB database. For the time being, the web page is being hosted on the users’ computer. The database stores user information along with their individual cart, pantry, and grocery items. Python is used in the backend for the machine learning algorithm for the recipe curator feature. Next.js is used for fetching data and routing. We will be using the Javascript Mongoose library to insert and retrieve data from the MongoDB database.

---

---

Application Requirements

Steps to run the code:

git clone "https://github.com/tkudupudi2/grass.git"
cd grass

2. Create a new file: .env
3. Paste the following into .env:

MONGODB_URI=mongodb+srv://admin:testtest123@cluster0.tamvy.mongodb.net/grocery-assistant?retryWrites=true&w=majority
JWT_SECRET=Grass

4. Run npm install to install dependencies and npm run dev
5. Open the application on a web browser: http://localhost:3000/