import gradio as gr
import cv2
import numpy as np

# Load face detector (OpenCV's built-in classifier)
face_cascade = cv2.CascadeClassifier(
    cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
)

def process_frame(frame):
    """
    Process each webcam frame: detect faces and overlay masks.
    """
    if frame is None:
        return None

    # Convert to grayscale for face detection
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

    # Apply mask to each detected face
    for (x, y, w, h) in faces:
        # Zorro-style mask (black band across eyes)
        mask_y = y + int(h * 0.35)
        mask_height = int(h * 0.3)
        cv2.rectangle(frame, (x, mask_y), (x + w, mask_y + mask_height), (0, 0, 0), -1)

        # Eye cutouts for the mask (so you can see!)
        eye_y = mask_y + int(mask_height * 0.5)
        cv2.circle(frame, (x + int(w * 0.3), eye_y), int(w * 0.12), (255, 255, 255), -1)
        cv2.circle(frame, (x + int(w * 0.7), eye_y), int(w * 0.12), (255, 255, 255), -1)

        # Optional: Add a "Z" mark
        cv2.putText(frame, "Z", (x + w - 20, y + h - 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)

    return frame

# Create the Gradio interface
demo = gr.Interface(
    fn=process_frame,
    inputs=gr.Image(sources=["webcam"], streaming=True),
    outputs=gr.Image(type="numpy"),
    live=True,
    title="🎭 Zorro Mask Sandbox",
    description="Point your webcam at your face. The app detects faces and adds a Zorro-style mask in real-time!"
)

demo.launch()