InventoryAI

InventoryAI is an advanced inventory management system that leverages artificial intelligence to optimize stock levels, predict demand, and streamline operations.

Overview

InventoryAI integrates a Next.js frontend, a Node.js (Express) backend, and Python-based AI models within a monorepo structure to provide a seamless and efficient inventory management solution.

Purpose

The primary goal of InventoryAI is to assist businesses in maintaining optimal inventory levels, reducing holding costs, and preventing stockouts by utilizing AI-driven demand forecasting and real-time inventory tracking.

Functionality

• Real-Time Inventory Tracking: Monitor stock levels across multiple locations in real-time.
• AI-Powered Demand Forecasting: Predict future inventory requirements using machine learning models.
• Automated Reordering: Set up automatic purchase orders based on predefined thresholds.
• Comprehensive Reporting: Generate detailed reports on inventory performance and trends.

Features

• User-Friendly Interface: Intuitive Next.js frontend for easy navigation and management.
• Robust Backend: Scalable Node.js (Express) backend handling API requests and business logic.
• Advanced AI Models: Python-based models for accurate demand forecasting.
• Monorepo Structure: Unified codebase for streamlined development and deployment.

Demo

Check out the live demo: InventoryAI Demo

Tech Stack

• Frontend: Next.js, React, Tailwind CSS
• Backend: Node.js, Express.js, MongoDB
• AI Models: Python, TensorFlow, scikit-learn
• Package Management: pnpm
• Monorepo Tooling: Turborepo

Folder Structure

The project follows a monorepo structure managed by Turborepo:

InventoryAI/
├── apps/
│   ├── web/           # Next.js frontend application
│   └── backend/       # Node.js (Express) backend application
├── packages/
│   ├── ui/            # Shared UI components
│   ├── eslint-config/ # Shared ESLint configurations
│   └── tsconfig/      # Shared TypeScript configurations
├── models/            # Python AI models
├── .vscode/           # VSCode settings
├── package.json       # Root package.json
├── pnpm-workspace.yaml# pnpm workspace configuration
└── turbo.json         # Turborepo configuration

Setup Guide

Follow these steps to set up the project locally:

Prerequisites

• Node.js: Ensure Node.js is installed. Download Node.js

• Python: Ensure Python 3.x is installed. Download Python

• pnpm: Install pnpm globally using npm:

  npm install -g pnpm

Cloning the Repository

git clone https://github.com/dev-huddle/InventoryAI.git
cd InventoryAI

Installing Dependencies

Install the dependencies for the entire monorepo:

pnpm install

Running the Applications

Start the development servers for both frontend and backend:

1. Frontend (Next.js):

   pnpm --filter web dev

2. Backend (Express.js):

   pnpm --filter backend dev

3. AI Models (Python):

   cd models
   pip install -r requirements.txt
   python app.py

The frontend will be accessible at http://localhost:3000, the backend at http://localhost:5000, and the AI models as configured.

Contribution Guidelines

We welcome contributions! Please follow these steps:

1. Fork the Repository: Click the 'Fork' button on the GitHub page.

2. Create a Branch: Create a new branch for your feature or bugfix.

   git checkout -b feature-name

3. Make Changes: Implement your changes in the codebase.

4. Commit Changes: Commit your changes with a descriptive message.

   git commit -m "Add feature-name"

5. Push to GitHub: Push your branch to your forked repository.

   git push origin feature-name

6. Create a Pull Request: Navigate to the original repository and create a pull request from your branch.

Credits

InventoryAI is developed and maintained by Dev Huddle.