text: import streamlit as st
  from PIL import Image , ImageEnhance
  import numpy as np
  import cv2
  import os
  from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
  from tensorflow.keras.preprocessing.image import img_to_array
  from tensorflow.keras.models import load_model
  import detect_mask_image
  
  def mask_image():
  	
  	global image
  	print("[INFO] loading face detector model...")
  	prototxtPath = os.path.sep.join(["face_detector", "deploy.prototxt"])
  	weightsPath = os.path.sep.join(["face_detector",
  		"res10_300x300_ssd_iter_140000.caffemodel"])
  	net = cv2.dnn.readNet(prototxtPath, weightsPath)
  
  	print("[INFO] loading face mask detector model...")
  	model = load_model("mask_detector.model")
  
  	image = cv2.imread("./images/out.jpg")
  	(h, w) = image.shape[:2]
  
  	blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300),
  		(104.0, 177.0, 123.0))
  
  	print("[INFO] computing face detections...")
  	net.setInput(blob)
  	detections = net.forward()
  
  	for i in range(0, detections.shape[2]):
  
  		confidence = detections[0, 0, i, 2]
  
  		if confidence > 0.5:
  			box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
  			(startX, startY, endX, endY) = box.astype("int")
  
  			(startX, startY) = (max(0, startX), max(0, startY))
  			(endX, endY) = (min(w - 1, endX), min(h - 1, endY))
  
  			face = image[startY:endY, startX:endX]
  			face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
  			face = cv2.resize(face, (224, 224))
  			face = img_to_array(face)
  			face = preprocess_input(face)
  			face = np.expand_dims(face, axis=0)
  
  			(mask, withoutMask) = model.predict(face)[0]
  
  			label = "Mask" if mask > withoutMask else "No Mask"
  			color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
  
  			label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
  
  			cv2.putText(image, label, (startX, startY - 10),
  				cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
  			cv2.rectangle(image, (startX, startY), (endX, endY), color, 2)
  
  mask_image()
  
  
  def mask_detection():
  
  	st.title("Face mask detection")
  	activities = ["Image" ,"Webcam"]
  	st.set_option('deprecation.showfileUploaderEncoding', False)
  	choice = st.sidebar.selectbox("Mask Detection on?",activities)
  	
  	if choice == 'Image':
  		st.subheader("Detection on image")
  		image_file = st.file_uploader("Upload Image",type=['jpg']) 
  		if image_file is not None:
  			our_image = Image.open(image_file)
  			im = our_image.save('./images/out.jpg')
  			saved_image = st.image(image_file , caption='image uploaded successfully', use_column_width=True)
  			if st.button('Process'): 
  				st.image(image)
  			
  	if choice == 'Webcam':
  		st.subheader("Detection on webcam")
  		st.text("This feature will be avilable soon")
  	
  mask_detection()
  
  
  