app.py

import gradio as gr
import numpy as np
import cv2
from PIL import Image

def detect_faces(image , slider ) :
    # detect faces
    # convert image in to numpy array
    image_np = np.array(image)
    
    # convert image into gray
    gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
    
    # use detectmultiscale function to detect faces using haar cascade
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=slider, minNeighbors=5, minSize=(30, 30))
    
    # draw rectangle along detected faces
    for (x, y, w, h) in faces:
        cv2.rectangle(image_np, (x, y), (x+w, y+h), (255, 0, 0), 5)
    return image_np

# slider = gr.Slider(1, 2, step=.1, label="Adjust the ScaleFactor", value=1.5)

iface = gr.Interface( fn=detect_faces,inputs=["image",gr.Slider(1, 2, step=.1, label="Adjust the ScaleFactor", value=1.5)],outputs="image",title="Face Detection using Haar Cascade Classifier ",description="Upload an image,and the model will detect faces and draw bounding boxes around them.",)

iface.launch()