import streamlit as st
from PIL import Image
import face_recognition
import cv2
import numpy as np
import os

def load_images(directory):
    images = []
    classnames = []
    file_list = os.listdir(directory)

    st.write("Photographs found in folder : ")
    for file in file_list:
        if os.path.splitext(file)[1] in [".jpg", ".jpeg"]:
            img_path = os.path.join(directory, file)
            cur_img = cv2.imread(img_path)
            images.append(cur_img)
            st.write(os.path.splitext(file)[0])
            classnames.append(os.path.splitext(file)[0])

    return images, classnames

def recognize_faces(test_image, known_encodings, class_names):
    imgS = cv2.resize(test_image, (0, 0), None, 0.25, 0.25)
    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
    facesCurFrame = face_recognition.face_locations(imgS)
    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)

    name = "Unknown"  # Default name for unknown faces
    match_found = False  # Flag to track if a match is found

    # Checking if faces are detected
    if len(encodesCurFrame) > 0:
        for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
            matches = face_recognition.compare_faces(known_encodings, encodeFace)
            faceDis = face_recognition.face_distance(known_encodings, encodeFace)
            matchIndex = np.argmin(faceDis)

            if matches[matchIndex]:
                name = class_names[matchIndex].upper()
                match_found = True  # Set the flag to True
                
            y1, x2, y2, x1 = faceLoc
            y1, x2, y2, x1 = (y1 * 4), (x2 * 4), (y2 * 4) ,(x1 * 4)
            cv2.rectangle(test_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
            cv2.rectangle(test_image, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
            cv2.putText(test_image, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
            
    return test_image

st.title("AIMLJan24 - Face Recognition")

# Load images for face recognition
directory = "photos"
Images, classnames = load_images(directory)

# Load images for face recognition
encodeListknown = [face_recognition.face_encodings(img)[0] for img in Images]

# camera to take photo of user in question
file_name = st.file_uploader("Upload image")

if file_name is not None:
    test_image = np.array(Image.open(file_name))
    image_with_recognition = recognize_faces(test_image, encodeListknown, classnames)
    st.image(image_with_recognition, use_column_width=True, output_format="PNG")