app.py

from flask import *
from PIL import Image

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

############################################
# import matplotlib.pyplot as plt
# 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
# bytes_data=None
from flask_socketio import SocketIO,emit
import base64
##################################################3

# import gradio as gr




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

def base64_to_image(base64_string):
    # Extract the base64 encoded binary data from the input string
    base64_data = base64_string.split(",")[1]
    # Decode the base64 data to bytes
    image_bytes = base64.b64decode(base64_data)
    # Convert the bytes to numpy array
    image_array = np.frombuffer(image_bytes, dtype=np.uint8)
    # Decode the numpy array as an image using OpenCV
    image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
    return image

@socket.on("connect")
def test_connect():
    print("Connected")
    emit("my response", {"data": "Connected"})

@socket.on("image")
def receive_image(image):
    # Decode the base64-encoded image data
    image = base64_to_image(image)
    image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA)
    # emit("processed_image", image)
    # Make the image a numpy array and reshape it to the models input shape.
    image = np.asarray(image, dtype=np.float32).reshape(1, 224, 224, 3)
    image = (image / 127.5) - 1
    # Predicts the model
    prediction = model.predict(image)
    index = np.argmax(prediction)
    class_name = class_names[index]
    confidence_score = prediction[0][index]
    emit("result",{"name":str(class_name),"score":str(confidence_score)})
# flag1 = True

# @app.route('/at')
# def testme():
#     global flag1
#     # return "i am in testme"
#     while flag1 is True:
        
#         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)
#         if bytes_data is None:
#             flag1 = False
#             st.stop()

# 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)

#     # Function to run face recognition
#     def run_face_recognition():
#         video_capture = cv2.VideoCapture(0)
#         s = True

#         existing_names = set(row[0] for row in csv.reader(csv_file))  # Collect existing names from the CSV file   
        

#         while s:
#             _, frame = video_capture.read()
#             small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
#             rgb_small_frame = small_frame[:, :, ::-1]
            
#             face_locations = face_recognition.face_locations(rgb_small_frame)
#             face_encodings = face_recognition.face_encodings(small_frame, face_locations)
#             face_names = []

#             for face_encoding in face_encodings:
#                 matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
#                 name = ""
#                 face_distance = face_recognition.face_distance(known_face_encodings, face_encoding)
#                 best_match_index = np.argmin(face_distance)
#                 if matches[best_match_index]:
#                     name = known_faces_names[best_match_index]

#                 face_names.append(name)

               
#                 for name in face_names:
#                     if name in known_faces_names and name in students and name not in existing_names:
#                         students.remove(name)
#                         print(students)
#                         print(f"Attendance recorded for {name}")
#                         current_time = now.strftime("%H-%M-%S")
#                         csv_writer.writerow([name, current_time, "Present"])
#                         existing_names.add(name)  # Add the name to the set of existing names
                        
#                         s = False  # Set s to False to exit the loop after recording attendance
#                         break  # Break the loop once attendance has been recorded for a name

#             cv2.imshow("Attendance System", frame)
#             if cv2.waitKey(1) & 0xFF == ord('q'):
#                 break

#         video_capture.release()
#         cv2.destroyAllWindows()
#         csv_file.close()

#     # 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)