import streamlit as st
from PIL import Image
import face_recognition
import cv2
import numpy as np
import os
import sqlite3
from datetime import datetime
import requests

st.title("Face Recognition based attendance system")


# Load images for face recognition
Images = []   
classnames = []  
directory = "photos"
myList = os.listdir(directory)
current_datetime = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

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.camera_input("Upload image")



def add_attendance(names):
    url = "https://ai-ml-project.glitch.me/adduserdata1"  # Change this URL to your Glitch endpoint
    success_count = 0
    print(len(names))
    
    data = {'name': name}
    response = requests.get(url, data=data)
    if response.status_code == 200:
        success_count += 1
    else:
        st.warning(f"Failed to mark attendance for {name}")

    if success_count == len(names):
        st.success("Attendance marked for all recognized faces. Have a good day!")
    else:
        st.success("Attendance marked for some faces. Check warnings for details.")

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 around the face
            y1, x2, y2, x1 = faceLoc
            y1, x2, y2, x1 = (y1 * 4), (x2 * 4), (y2 * 4) ,(x1 * 4)
            image = image.copy()
            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
            cv2.putText(image, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
            
            # Store attendance in SQLite database
        print(recognized_names)
        
        # Display the image with recognized faces
        st.image(image, use_column_width=True, output_format="PNG")
        st.write("Length : {recognizes_names}")
        # Display recognized names
        st.write("Recognized Names:")
        for i, name in enumerate(recognized_names):
            st.write(f"Face {i+1}: {name}")
            add_attendance(name)
   
    else:
        st.warning("No faces detected in the image. Face recognition failed.")