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app.py

@@ -1,18 +1,32 @@
 import streamlit as st
 import cv2
 
+# Load the Haar Cascade XML file for face detection
+face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
 # Create a Streamlit app
-st.title("Webcam Capture")
+st.title("Face Detection")
 
 # Initialize the webcam
 cap = cv2.VideoCapture(0)
 cap.set(3, 640)
 cap.set(4, 480)
 
-# Create a button to capture images
-if st.button("Capture Image"):
+# Create a button to perform face detection
+if st.button("Detect Faces"):
     ret, img = cap.read()
     if ret:
+        # Convert the image to grayscale for face detection
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+        # Perform face detection
+        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
+
+        # Draw rectangles around detected faces
+        for (x, y, w, h) in faces:
+            cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
+
+        # Display the image with detected faces
         st.image(img, channels="BGR")
 
 # Release the webcam when the app is done