app.py

import numpy as np
import cv2
import gradio as gr

# Load Haar Cascade classifier
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")

# Face Detection Function
def detect_faces(image_np, slider):
    # Convert image to numpy array
    img = np.array(image_np)

    # Convert to grayscale
    gray_image = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

    # Detect faces
    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=slider, minNeighbors=5, minSize=(30, 30))

    # Draw rectangles on original image
    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)

    return img, f"Faces detected: {len(faces)}"

# Create Gradio Interface
iface = gr.Interface(
    fn=detect_faces,
    inputs=[
        gr.Image(type="numpy", label="Upload Image"),
        gr.Slider(minimum=1.1, maximum=2.0, step=0.1, label="Adjust the scale factor.")
    ],
    outputs=[
        gr.Image(label="Detected Faces"),
        gr.Label(label="Face Count")
    ],
    title="Face Detection",
    description="Upload an image, and the model will detect faces and draw bounding boxes around them."
)

iface.launch()