Kakarot-2000

def getLink(number):
    link='https://www.google.com/search?q='+'geeksforgeeks puzzle '+number
    response=requests.get(link)
    
    ls=[]
    links=[]
    try:
        soup=bs4.BeautifulSoup(response.text,features="html.parser")
        
        for tag in soup.find_all('a'):

Kakarot-2000

import smtplib, ssl
import bs4,webbrowser
import requests

Kakarot-2000

model = model_from_json(open("fer.json", "r").read())  #load model  

model.load_weights('fer.h5')                            #load weights  

face_haar_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

camera = cv2.VideoCapture(0)

app = Flask(__name__)

Kakarot-2000

from flask import Flask, render_template, Response
import cv2
import numpy as np
from tensorflow.keras.models import model_from_json  
from tensorflow.keras.preprocessing import image  

Kakarot-2000

flask
tensorflow
opencv-python
numpy

Kakarot-2000

fer_json = model1.to_json()  
with open("fer.json", "w") as json_file:  
    json_file.write(fer_json)  
model1.save_weights("fer.h5")

Kakarot-2000

x_train=x_train.astype('float32')
x_test=x_test.astype('float32')

#intializing callbacks
early_stopping=keras.callbacks.EarlyStopping(patience=15,restore_best_weights=True)

model1.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])

model1.fit(x_train,y_train,
           batch_size=64,

Kakarot-2000

model1=keras.models.Sequential()

# Block-1
model1.add(keras.layers.Conv2D(filters=32, kernel_size=(3,3), padding='same', 
                     kernel_initializer='he_normal',
                     activation="elu", 
                     input_shape=(48,48,1)))
model1.add(keras.layers.BatchNormalization())

model1.add(keras.layers.Conv2D(filters=32, kernel_size=(3,3), padding='same', 

Kakarot-2000

#data augmentation
x_train=x_train.reshape((x_train.shape[0],48,48,1))
x_test=x_test.reshape((x_test.shape[0],48,48,1))
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1./255,
                                   rotation_range=60,
                                   shear_range=0.5,
                                   zoom_range=0.5,
                                   width_shift_range=0.5,
                                   height_shift_range=0.5,

Kakarot-2000

df1 = pd.read_csv("fer2013.csv") #make sure the file is in the root location

# Preprocessing
x_train=[]
x_test=[]
y_train=[]
y_test=[]
for i,row in df1.iterrows():
    k=row['pixels'].split(" ")
    if(row['Usage']=='Training'):