SmartStock AI: Predictive Inventory Management System

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🚀 Project Overview

SmartStock AI is an intelligent inventory management system that uses machine learning and computer vision to help warehouses predict, monitor, and optimize their stock levels.
It brings together real-time stock insights, AI-based forecasting, and object detection for a smarter and more automated inventory workflow.

🔍 Key Features

• 📦 Predictive Inventory Forecasting – Anticipates stock needs based on past trends.
• 🧠 AI-Powered Object Detection – Uses YOLOv8 to identify items in uploaded shelf images.
• 📊 Interactive Dashboard – Built with Streamlit for seamless visualization and control.
• 📷 Image-Based Shelf Scanning – Upload shelf images to automatically detect missing items.
• 🔄 Modular Design – Combines dynamic stock prediction and visual analysis in one app.
• ⚡ Real-Time Insights – Detect anomalies, track stock levels, and visualize performance instantly.

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📂 Folder Structure

SMARTSTOCKAI/
├── data/
│   ├── anomalies.csv
│   ├── forecast_pre.csv
│   ├── inventory_data.csv
│   └── product_details.csv
├── forecasts/
├── images/
├── models/
│   ├── anomaly_detector.pkl
│   └── forecast_model.pkl
├── scripts/
│   ├── __pycache__/
│   ├── generate_sample_data.py
│   ├── predict_api.py
│   ├── preprocess.py
│   ├── train_anomaly.py
│   └── train_prophet.py
├── app.py
├── README.md
├── shelf_image.py
├── shelf_monitor.py
├── smartstock_vision_app.py
├── SmartStockAI.ipynb
├── yolov8n.pt
├─ requirements.txt
└─ README.md

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🧠 Tech Stack

• Language: Python 3.10+
• Frontend: Streamlit (interactive web app)
• ML & Forecasting: Scikit-learn, NumPy, Pandas
• Computer Vision: OpenCV, Ultralytics YOLOv8
• Visualization: Matplotlib, Seaborn, Plotly
• Environment: Virtualenv / Conda

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⚙️ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/harishy0406/SmartStockAI.git
cd SmartStockAI

2️⃣ Create Virtual Environment

python -m venv venv
# Activate it
venv\Scripts\activate       # (Windows)
source venv/bin/activate    # (Linux/Mac)

3️⃣ Install Required Libraries

pip install -r requirements.txt

4️⃣ Run the Streamlit App

• For SmartStockAI Dashboard

streamlit run app.py

• For Shelf Image Detection

streamlit run app_upload.py

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📸 Example Outputs

🖼️ 1. SmartStock AI Dynamic Dashboard

🤖 2. Shelf Detection Results

📈 3. Forecast Visualization

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🌟 Future Enhancements

• 🔔 Automated Low-Stock Alerts via Email or SMS
• ☁️ Cloud Dashboard for multi-warehouse integration
• 📹 Live Shelf Monitoring using IP cameras
• 🤖 Vision Transformer Integration (ViT) for improved recognition
• 🧾 ERP API Integration for seamless business workflows

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🤝 Contribution

Want to make SmartStock AI even smarter? Fork the repo, improve features, and create a pull request! Let’s redefine warehouse intelligence — together 🚀

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