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

st.title("AIMLJan24 - Face Recognition")

# Load images for face recognition
Images = []   
classnames = []  
directory = "photos"
myList = os.listdir(directory)

st.write("Photographs found in folder : ")
for cls in myList:
    if os.path.splitext(cls)[1] in [".jpg", ".jpeg"]:
        img_path = os.path.join(directory, cls)
        curImg = cv2.imread(img_path)
        Images.append(curImg)
        st.write(os.path.splitext(cls)[0])
        classnames.append(os.path.splitext(cls)[0])

# 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")

def add_attendance(name):
    username = name
    current_datetime = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    print(current_datetime)
    
    if not os.path.isdir('Attendance'):
        os.makedirs('Attendance')

    if f'Attendance-{current_datetime}.csv' not in os.listdir('Attendance'):
        with open(f'Attendance/Attendance-{current_datetime}.csv', 'w') as f:
            f.write('Name,Time')
   

if file_name is not None:
    col1, col2 = st.columns(2)

    test_image = Image.open(file_name)
    image = np.asarray(test_image)

    imgS = cv2.resize(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)

    # List to store recognized names for all faces in the image
    recognized_names = []

    # Checking if faces are detected
    if len(encodesCurFrame) > 0:
        for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
            # Assuming that encodeListknown is defined and populated in your code
            matches = face_recognition.compare_faces(encodeListknown, encodeFace)
            faceDis = face_recognition.face_distance(encodeListknown, encodeFace)
            
            # Initialize name as Unknown
            name = "Unknown"

            # Check if there's a match with known faces
            if True in matches:
                matchIndex = np.argmin(faceDis)
                name = classnames[matchIndex].upper()
                
            # Append recognized name to the list
            recognized_names.append(name)

            # Draw rectangle and label on the image
            y1, x2, y2, x1 = faceLoc
            y1, x2, y2, x1 = (y1 * 4), (x2 * 4), (y2 * 4) ,(x1 * 4)
            
            # Make a copy of the image array before drawing on it
            image_copy = image.copy()
            
            cv2.rectangle(image_copy, (x1, y1), (x2, y2), (0, 255, 0), 2)
            cv2.rectangle(image_copy, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
            cv2.putText(image_copy, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)

            # Update the database
            add_attendance(name)
            
        # Display the image with recognized names
        st.image(image_copy, use_column_width=True, output_format="PNG")

        # Display recognized names
        st.write("Recognized Names:")
        for i, name in enumerate(recognized_names):
            st.write(f"Face {i+1}: {name}")
    else:
        st.warning("No faces detected in the image. Face recognition failed.")