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 , len(faces)

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

iface = gr.Interface(  fn=detect_faces,
    inputs=["image",gr.Slider(minimum=1, maximum=2, step=.1, label="Adjust the ScaleFactor")],
    outputs=["image", gr.Label("faces count ")] ,
    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()