import gradio as gr
import dlib
import cv2
import numpy as np


# Load the pre-trained face detector and facial landmarks predictor
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat") # You'll need to download this file

# Function to detect eyes in the image
def detect_eyes(image):
  image = cv2.imdecode(np.frombuffer(image.read(), np.uint8), -1)
  gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

  # Detect faces in the image
  faces = detector(gray)

  # Loop through detected faces
  for face in faces:
    landmarks = predictor(gray, face)

    # Define regions of interest for left and right eyes
    left_eye_region = [(36, 37, 38, 39, 40, 41)]
    right_eye_region = [(42, 43, 44, 45, 46, 47)]

    # Draw rectangles around eyes
    for region in left_eye_region + right_eye_region:
      for i in region:
        x = landmarks.part(i).x
        y = landmarks.part(i).y
        cv2.circle(image, (x, y), 2, (0, 255, 0), -1)

  # Encode the image back to bytes
  _, buffer = cv2.imencode('.jpg', image)
  return buffer.tobytes()

# Create a Gradio interface
iface = gr.Interface(fn=detect_eyes)

# Add an image input to the interface
iface.add_input("Image", gr.inputs.Image())

# Add an image output to the interface
iface.add_output("Detected Eyes", gr.outputs.Image())

# Launch the interface
iface.launch()