app.py

import gradio as gr
import cv2
import numpy as np

def detect_faces(image):
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.1, 4)

for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)

return image

demo = gr.Interface(fn=detect_faces, inputs=gr.Image(type="numpy"), outputs="image")

if __name__ == "__main__":
demo.launch()