import numpy as np
import cv2
import gradio as gr
from PIL import Image
def detect_faces(image):
 image_np= np.array(image)
 gray_image= cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
 face_cascade= cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
 faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.74, minNeighbors=5, minSize=(30, 30))
 for (x, y, w, h) in faces:
     cv2.rectangle(image_np, (x, y), (x+w, y+h), (0, 255, 0), 9)
 return image_np

iface= gr.Interface(fn=detect_faces,inputs="image",outputs="image",title="Face Detection",description="Upload an image, and the model will detect faces and draw bounding boxes around them.",)
iface.launch()