Web Application for Automatic Facial Age Estimation of Black Persons(Deep learning approach)

• Programming language and frameworks: Python, Tensorflow, Ktrain, Flask, Git-Bash
• Model training and evaluation: Google colab, Vs code
• WebApp: HTML, CSS, JSON, Python, Flask
• Dataset: BlackFaces
• Offline testing: Vscode terminal and Chrome browser
• Online testing: Digital Ocean

1. You clone this repo or download the code as zip file.

   • Extract the zip file on your PC.
   • The model size is above Github file size limit. Download the model from from here. and place the model binary files in the model folder
   • Open a Terminal(Cmd/Bash) to run the App

2. It's recommended that a virtual environment is used to install the app packages. virtual env documentation

   • For Gitbash: Type the code python -m venv afae_venv to setup a virtual environment. And
   • Activate the virtual env. with source venv /afae_venv

3. Install the requirements.txt file to automatically download and setup the app dependencies(packages).

   • Use the command pip install -r requirements.txt to set up the app on your PC

4. Launch the WebApp(Offline Setup)

   • Use python app.py or flask --app run app.py

5. The terminal should display a default IP address to run WebApp in your browser.

   • Ensure the computer is connected to the internet so that the Web UI bootstrap components can be loaded for a beautiful interface.
   • Proceed to test the App

6. Register as new user or loging with initial credentials after your first login.

   • You try the Automatic Facial age estimation as many time as you want.
   • Ensure the system can detect your face(proper room lightning)

7. For live testing and Demo anywhere. -The WebApp is deployed on Heroku and it's available 24/7 online. . App not available

   PS: Incase of any bug or misinformation, kindly reach out to the developer of the WebApp via email Email us

(c) 2024. All rights reserved.