main.py

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()

iface = gr.Interface(fn=detect_eyes, inputs=gr.Image('pil'), outputs=gr.Image('pil'))
iface.launch()