import cv2
import gradio as gr
import numpy as np

# Load Haar cascade
face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")

def detect_faces(image, scale):
    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    faces = face_cascade.detectMultiScale(gray, scale, 4)
    for (x, y, w, h) in faces:
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
    return image

iface = gr.Interface(
    fn=detect_faces,
    inputs=[
        gr.Image(type="numpy", label="Upload Image"),
        gr.Slider(1.00, 2.00, value=1.1, step=0.01, label="Scale Factor"),
    ],
    outputs=gr.Image(type="numpy", label="Detected Faces"),
    title="Face Detection with Haar Cascade",
    description="Adjust the scale factor using the slider to improve face detection accuracy or speed."
)

iface.launch()