import cv2
import numpy as np

def fill_attendance(update_message, video_path=None):
    # Load the trained model and Haar Cascade
    recognizer = cv2.face.LBPHFaceRecognizer_create()
    recognizer.read("trainer.yml")
    face_detector = cv2.CascadeClassifier("haarcascade_frontalface_alt.xml")

    # Initialize video capture
    if video_path:
        cap = cv2.VideoCapture(video_path)
    else:
        cap = cv2.VideoCapture(0)

    print("Taking attendance...")
    while True:
        ret, frame = cap.read()
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faces = face_detector.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)

        for (x, y, w, h) in faces:
            face_image = frame[y:y + h, x:x + w]
            label, confidence = recognizer.predict(face_image)

            # Here, we can map label to the student's name using a CSV file or any database
            print(f"Detected student ID: {label} with confidence: {confidence}")
            
            # For simplicity, let's just print the student ID
            update_message(f"Student {label} marked present")

            # Draw a rectangle around the face and display the student ID
            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

        cv2.imshow("Attendance", frame)

        if cv2.waitKey(1) & 0xFF == ord('q'):  # Press 'q' to exit
            break

    cap.release()
    cv2.destroyAllWindows()