Code Structure:

Import Libraries:

streamlit: Used for building the web app interface.
opencv-python: Used for capturing images from the webcam and performing image processing.
numpy: Required for image processing with OpenCV.
pyngrok: Used for connecting the app to the web (particularly useful in Colab or local environments).
face-recognition: Library for face detection and recognition.
pillow: Used for handling image processing, like saving captured frames.

App Structure:

App Title and Menu:

st.title("FacePulse: Facial Recognition Attendance System"): Displays the app title.
menu = ["Register", "Train", "Attendance", "About"]: A sidebar with 4 options (Register, Train, Attendance, About).
Depending on the user’s selection, different functionality is displayed.

Register New User:

The user can enter their ID and Name to register.
When "Take Images" is clicked, it triggers the webcam, captures 10 images, and saves them locally. These images are stored to be used for training the facial recognition model.

Train the Model:

After registering users, this section allows the app to train a facial recognition model.
It uses the captured images to learn and recognize each registered face.
Clicking "Train Images" triggers the training process, which involves scanning the directory of saved images, encoding the face data, and preparing it for recognition.

Take Attendance:

The "Attendance" section uses the webcam to detect faces and compare them with the trained model to recognize users.
When a user’s face is recognized, their attendance is recorded, and a success message is displayed.

About Section:

Provides a brief description of the FacePulse system and how it works.