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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('/at')
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)
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)
if bytes_data is None:
st.stop()
# Call the function to run face recognition
run_face_recognition()
return redirect(url_for('show_table'))
@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)
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