PredictaStock AI

Enterprise-Grade Grocery Stockout Prediction & Inventory Management System

An AI-powered SaaS platform designed to eliminate supply chain inefficiencies by predicting inventory stockouts before they happen. Built with a modern microservices architecture spanning a highly-responsive React frontend, a robust FastAPI backend, and proprietary machine learning models.

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✨ Key Features

• 🧠 Machine Learning Forecasting: Proprietary ML models that predict stock levels based on historical sales data and reorder thresholds.
• 📊 Real-time Dashboard: Global visibility into cross-store inventory metrics, financial overviews, and at-risk stock.
• ⚡ Instant Alerts: Automated notification system flagging critical items approaching stockout status.
• ☁️ Bulk Data Ingestion: Upload CSV inventory datasets in seconds and let the AI process optimal reorder timings.
• 💎 Enterprise UI/UX: Clean, dark-mode prioritized interface featuring glassmorphic design and fully interactive components.

🛠 Tech Stack

Frontend

• Framework: React + Vite
• Styling: Tailwind CSS, Framer Motion
• Icons: Lucide React
• Data Fetching: TanStack React Query

Backend

• API Framework: FastAPI (Python)
• Database: PostgreSQL (via SQLAlchemy ORM)
• Authentication: JWT (JSON Web Tokens)

Machine Learning

• Environment: Python Data Science Stack (ml/ directory)
• Capabilities: Time-series forecasting and inventory risk analysis.

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📂 Project Structure

PredictaStock-AI/
├── backend/            # FastAPI application (Routes, Models, Schemas, Database logic)
├── frontend/           # React + Vite application (Pages, Components, Assets)
├── ml/                 # Machine learning models, training scripts, and inference pipelines
├── datasets/           # Sample datasets for training and testing ML models
├── docker/             # Dockerfiles and container configurations
├── docs/               # Technical documentation
└── docker-compose.yml  # Orchestration file for running the entire stack via Docker

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🚀 Getting Started

To run the application locally for development, you will need to start both the backend server and the frontend development environment.

1. Prerequisites

• Node.js (v18+)
• Python (v3.9+)
• PostgreSQL (running locally or via Docker)

2. Backend Setup

The backend runs on FastAPI and uses a PostgreSQL database.

# Navigate to the backend directory
cd backend

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Start the API server with hot-reload
uvicorn app.main:app --reload

The backend API will be available at http://localhost:8000. You can view the interactive API docs at http://localhost:8000/docs.

3. Frontend Setup

The frontend is a Vite-powered React application.

# Open a new terminal and navigate to the frontend directory
cd frontend

# Install dependencies
npm install

# Start the development server
npm run dev

The frontend will be accessible at http://localhost:5173.

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🐳 Running with Docker (Quickstart)

If you prefer to run the entire stack (Database, Backend, and Frontend) simultaneously without installing Python or Node.js locally, you can use Docker Compose.

# From the root directory
docker-compose up -d --build

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🔒 Security & Deployment

For deployment to production environments:

1. Ensure VITE_API_URL is correctly configured in your frontend .env.
2. Secure your PostgreSQL database with strong credentials.
3. Use a managed hosting provider for the frontend (e.g., Vercel, Cloudflare Pages) and backend (e.g., Render, AWS EC2).

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