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


st.title("Project_1 - Face Recognition")

# create list of encoding of all images in photos folder
# Load images for face recognition
Images = []   # List to store Images
classnames = []  # List to store 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", ".jfif"]:
        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("Take a picture")  #st.file_uploader("Upload image  ")
def update_data(name):

    url = "https://final-predict-CV.glitch.me/adduserdata1"  #?rollno=222&name="+name
    data = {'name': name}
    response = requests.post(url , data=data )
    
    if response.status_code == 200:
        st.success("Your attendance is marked.Have a good day!")
    else:
        st.warning("Sorry attendance is not marked")

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)

    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):
            # Assuming that encodeListknown is defined and populated in your code
            matches = face_recognition.compare_faces(encodeListknown, encodeFace)
            faceDis = face_recognition.face_distance(encodeListknown, encodeFace)
            matchIndex = np.argmin(faceDis)

            if matches[matchIndex]:
                name = classnames[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)
            
            # 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
            update_data(name)
            
            
        st.image(image_copy, use_column_width=True, output_format="PNG")
    else:
        st.warning("No faces detected in the image. Face recognition failed.")

    # image = Image.open(file_name)
    # col1.image(image, use_column_width=True)