streamlit_app.py

import cv2
import os
from tensorflow.keras.models import load_model
import numpy as np
import streamlit as st



face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_eye.xml")
    
def drowsiness_detection(model_path, alarm_sound='alarm.wav'):
    
    model = load_model(os.path.join("models", "model.h5"))


    lbl=['Close', 'Open']

    path = os.getcwd()
    cap = cv2.VideoCapture(0)
    font = cv2.FONT_HERSHEY_COMPLEX_SMALL
    score = 0

    while(True):
        ret, frame = cap.read()
        height,width = frame.shape[:2]

        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        faces = face_cascade.detectMultiScale(gray,minNeighbors = 3,scaleFactor = 1.1,minSize=(25,25))
        eyes = eye_cascade.detectMultiScale(gray,minNeighbors = 1,scaleFactor = 1.1)

        cv2.rectangle(frame, (0,height-50) , (200,height) , (0,0,0) , thickness=cv2.FILLED )

        for (x,y,w,h) in faces:
            cv2.rectangle(frame, (x,y) , (x+w,y+h) , (255,0,0) , 3 )

        for (x,y,w,h) in eyes:

            eye = frame[y:y+h,x:x+w]
            #eye = cv2.cvtColor(eye,cv2.COLOR_BGR2GRAY)
            eye = cv2.resize(eye,(80,80))
            eye = eye/255
            eye = eye.reshape(80,80,3)
            eye = np.expand_dims(eye,axis=0)
            prediction = model.predict(eye)
            # print(prediction)
        #Condition for Close
            if prediction[0][0]>0.30:
                cv2.putText(frame,"Closed",(10,height-20), font, 1,(255,255,255),1,cv2.LINE_AA)
                cv2.putText(frame,'Score:'+str(score),(100,height-20), font, 1,(255,255,255),1,cv2.LINE_AA)
                score=score+1
                #print("Close Eyes")
                if(score > 20):
                    try:
                        sound.play()
                    except:  # isplaying = False
                        pass

            #Condition for Open
            elif prediction[0][1] > 0.70:
                score = score - 1
                if (score < 0):
                    score = 0
                cv2.putText(frame,"Open",(10,height-20), font, 1,(255,255,255),1,cv2.LINE_AA)
                #print("Open Eyes")
                cv2.putText(frame,'Score:'+str(score),(100,height-20), font, 1,(255,255,255),1,cv2.LINE_AA)

        cv2.imshow('frame',frame)
    
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
        
    cap.release()
    cv2.destroyAllWindows()


# Streamlit app
def main():
    st.title("Emotion Recognition App")
    
    # Click the button to start the webcam
    if st.button("Start Webcam"):
        drowsiness_detection(model_path = os.path.join('models', 'model.h5'))
        

# Run the Streamlit app
if __name__ == '__main__':
    main()