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🛍️ Retail360.AI: Smart Retail Face Recognition App

Retail360.AI is an intelligent retail entry system built using Streamlit and DeepFace that recognizes customers via facial recognition, greets them with personalized messages and voice, displays their past purchases, and recommends offers — all in real-time.

🚀 Features

  • 👤 Real-time Face Capture using Webcam
  • 🔍 Face Matching with Stored Customer Data (DeepFace)
  • 📄 Auto Fetch Customer Profile from MySQL
  • 💬 Personalized Greeting with Text & Voice (gTTS + pygame)
  • 🛒 Show Last 5 Purchases from Purchase History
  • 🎁 Personalized Offers based on Last Purchase Categories
  • 📊 Insights:
    • Bar Chart of Purchase Frequency (Plotly)
    • Gauge Chart of Total Spend
  • ✍️ New Customer Registration via Webcam

🗂️ Project Structure

Retail360-App/
│
├── data/                   # Csv files (e.g., customers.csv,orders.csv)
|   └── ...
├── face_images/            # Stored face images (e.g., 1_face.jpg)
│    └── ...
├── sample_outputs/         # Sample outputs for Recognition and Registration
│    └── ...
├── app.py                  # Main Streamlit app
├── requirements.txt        # Required Python packages
├── retail360degree.sql     # SQL Script
└── README.md               # Project documentation

⚙️ Tech Stack

  • Frontend: Streamlit
  • Backend: Python + MySQL
  • Face Recognition: DeepFace
  • Voice Output: gTTS + pygame
  • Charts: Plotly
  • Database: MySQL

📸 Face Image Format

Face images are stored in the face_images/ folder with the naming convention:

<customer_id>_face.jpg

Example:

1_face.jpg, 2_face.jpg, ...

🧪 Setup Instructions

1. Clone the repository

git clone https://github.com/zenthicai/Retail360.AI-Autonomous-Retail-Experience
cd Retail360-App

2. Install dependencies

pip install -r requirements.txt

3. Create MySQL Database & Tables

Create a MySQL database named retail and include required tables like:

  • customers
  • purchases
  • offers
  • purchases_data_model (view with joined data)

Note: Ensure the structure matches app.py expectations.

4. Run the App

streamlit run app.py

📢 Acknowledgements

Restricted Dataset: Personal Images

WARNING: This dataset contains personal images of individuals and is strictly for private, non-commercial use within this project only. Unauthorized use, distribution, or reproduction of these images is prohibited.