🛡️ Smart Biometric Dashboard

A high-performance, two-tier facial recognition system optimized for CPU processing. This project separates the AI logic (Backend) from the User Interface (Frontend) to ensure smooth, real-time biometric verification.

🏗️ Project Architecture

• Backend (FastAPI): Handles AI processing using DeepFace and OpenCV. It runs as an independent API server, allowing for smooth operation without freezing the interface.
• Frontend (Streamlit): Provides a clean, interactive web dashboard for users to scan their faces and communicate with the backend.

🚀 Features

• CPU Optimized: Designed to run efficiently on standard machines without requiring an expensive GPU.
• Decoupled Architecture: Backend and Frontend run independently, demonstrating professional system design.
• Real-Time Matching: Instantly compares camera input against a local image database.

⚙️ Installation & Setup

1. Clone the repository:

   git clone https://github.com/YOUR_USERNAME/smart-biometric-dashboard.git
   cd smart-biometric-dashboard

(Note: Replace YOUR_USERNAME with your actual GitHub username)

2. Install dependencies:

   pip install -r requirements.txt

3. Set up the Database: Create a folder named baza in the main directory. Add clear photos of people you want to authorize. Name each file with the person's name (e.g., ALEX.jpg).

▶️ Running the Application

You need two separate terminal windows to run the complete system.

Terminal 1: Start the Backend API

uvicorn main:app --reload

The API interface will be accessible at http://127.0.0.1:8000/docs

Terminal 2: Start the Frontend Dashboard

streamlit run frontend.py

The web dashboard will automatically open in your default browser.