app.py

from flask import *
from PIL import Image

import face_recognition
import cv2
import numpy as np
import csv
from datetime import datetime

# from matplotlib import pyplot as plt # this lets you draw inline pictures in the notebooks
# import pylab # this allows you to control figure size 
# pylab.rcParams['figure.figsize'] = (10.0, 8.0) # this controls figure size in the notebook

import io
import streamlit as st







app = Flask(__name__)


# @app.route("/")
# def index():
#     #return 'hello'
#     return render_template("index.html")


####################################################
# app = Flask(__name__)

# app.config['SECRET_KEY'] = 'secret!'
# socket = SocketIO(app,async_mode="eventlet")

# @socket.on("connect")
# def test_connect():
#     print("Connected")
#     emit("my response", {"data": "Connected"})
########################################################    
@app.route('/att')
def attend():
    # Face recognition variables
    known_faces_names = ["Sarwan Sir", "Vikas","Lalit","Jasmeen","Anita Ma'am"]
    known_face_encodings = []

    # Load known face encodings
    sir_image = face_recognition.load_image_file("photos/sir.jpeg")
    sir_encoding = face_recognition.face_encodings(sir_image)[0]

    vikas_image = face_recognition.load_image_file("photos/vikas.jpg")
    vikas_encoding = face_recognition.face_encodings(vikas_image)[0]

    lalit_image = face_recognition.load_image_file("photos/lalit.jpg")
    lalit_encoding = face_recognition.face_encodings(lalit_image)[0]

    jasmine_image = face_recognition.load_image_file("photos/jasmine.jpg")
    jasmine_encoding = face_recognition.face_encodings(jasmine_image)[0]

    maam_image = face_recognition.load_image_file("photos/maam.png")
    maam_encoding = face_recognition.face_encodings(maam_image)[0]

    known_face_encodings = [sir_encoding, vikas_encoding,lalit_encoding,jasmine_encoding,maam_encoding]

    students = known_faces_names.copy()

    face_locations = []
    face_encodings = []
    face_names = []

    now = datetime.now()
    current_date = now.strftime("%Y-%m-%d")
    csv_file = open(f"{current_date}.csv", "a+", newline="")
    
    csv_writer = csv.writer(csv_file)

@app.route('/at')   
def run_face_recognition():
    bytes_data=None

    img_file_buffer=st.camera_input("Take a picture")
    if img_file_buffer is not None:
        test_image = Image.open(img_file_buffer)
        st.image(test_image, use_column_width=True)
        st.write(type(test_image))
        image = np.asarray(test_image)
        gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)

        st.image(cv2.cvtColor(gray))
    if bytes_data is None:
        st.stop()


   

@app.route('/table')
def show_table():
    # Get the current date
    current_date = datetime.now().strftime("%Y-%m-%d")
    # Read the CSV file to get attendance data
    attendance=[]
    try:
        with open(f"{current_date}.csv", newline="") as csv_file:
            csv_reader = csv.reader(csv_file)
            attendance = list(csv_reader)
    except FileNotFoundError:
        pass
    # Render the table.html template and pass the attendance data
    return render_template('attendance.html', attendance=attendance)

@app.route("/")
def home():
    return render_template('index.html')

   


if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860)