import streamlit as st
import cv2
import numpy as np
import dlib
from scipy.spatial import distance as dist
from imutils import face_utils

# Constants 
EYE_AR_THRESH = 0.3
EYE_AR_CONSEC_FRAMES = 30
YAWN_THRESH = 20 

# Global variables
COUNTER = 0  # Global COUNTER defined here

# Functions
def eye_aspect_ratio(eye):
    A = dist.euclidean(eye[1], eye[5])
    B = dist.euclidean(eye[2], eye[4])
    C = dist.euclidean(eye[0], eye[3])
    ear = (A + B) / (2.0 * C)
    return ear

def final_ear(shape):
    (lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
    (rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
    leftEye = shape[lStart:lEnd]
    rightEye = shape[rStart:rEnd]
    leftEAR = eye_aspect_ratio(leftEye)
    rightEAR = eye_aspect_ratio(rightEye)
    ear = (leftEAR + rightEAR) / 2.0
    return (ear, leftEye, rightEye)

def lip_distance(shape):
    top_lip = shape[50:53]
    top_lip = np.concatenate((top_lip, shape[61:64]))
    low_lip = shape[56:59]
    low_lip = np.concatenate((low_lip, shape[65:68]))
    top_mean = np.mean(top_lip, axis=0)
    low_mean = np.mean(low_lip, axis=0)
    distance = abs(top_mean[1] - low_mean[1])
    return distance

def process_frame(frame, detector, predictor):
    """Process the frame and detect drowsiness and yawning."""
    global COUNTER  # Declare COUNTER as global here
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    rects = detector.detectMultiScale(
        gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE
    )

    for (x, y, w, h) in rects:
        rect = dlib.rectangle(int(x), int(y), int(x + w), int(y + h))
        shape = predictor(gray, rect)
        shape = face_utils.shape_to_np(shape) 

        ear, leftEye, rightEye = final_ear(shape)
        distance = lip_distance(shape)

        leftEyeHull = cv2.convexHull(leftEye)
        rightEyeHull = cv2.convexHull(rightEye)
        cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)
        cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)

        lip = shape[48:60]
        cv2.drawContours(frame, [lip], -1, (0, 255, 0), 1)

        if ear < EYE_AR_THRESH:
            COUNTER += 1
            if COUNTER >= EYE_AR_CONSEC_FRAMES:
                cv2.putText(frame, "DROWSINESS", (10, 30),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        else:
            COUNTER = 0

        if distance > YAWN_THRESH:
            cv2.putText(frame, "YAWN", (10, 60),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)

        cv2.putText(frame, f"EAR: {ear:.2f}", (300, 30),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
        cv2.putText(frame, f"YAWN: {distance:.2f}", (300, 60),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)

    return frame

# Load detector and predictor
detector = cv2.CascadeClassifier('./haarcascade_frontalface_default.xml')
predictor = dlib.shape_predictor('./shape_predictor_68_face_landmarks.dat') 

# Streamlit UI
st.title("Sleep Detection using OpenCV")
st.markdown("**Check the box below to start the camera:**")

run = st.checkbox("Run Camera")

# Video capture
if run:
    cap = cv2.VideoCapture(0)
    FRAME_WINDOW = st.image([])
    
    while True:
        ret, frame = cap.read()
        if not ret:
            st.error("Failed to open webcam.")
            break

        frame = cv2.resize(frame, (450, 300))
        frame = process_frame(frame, detector, predictor)  # Process frame here
        FRAME_WINDOW.image(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
else:
    st.info("Check 'Run Camera' to start detection.")