pranshuj73

from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from time import sleep

class Tweeter:
	def login(self):
		username = "your username/email will go here"
		password = "and your password shall go here"
		
		# opens up the browser window

pranshuj73

# everything here is a part of login function of Tweeter class
		# Now we need to find the field where we want to type in the username/email
		uname_field = driver.find_element_by_name("session[username_or_email]")
		# typing the username
    	# Note: it's important to press tab using Keys.TAB after typing the username
    	# to switch to the password field (or we can add extra lines of code to click on it)
		uname_field.send_keys(username, Keys.TAB)
    
    	# selecting the password field
		pass_field = driver.find_element_by_name("session[password]")

pranshuj73

# This function is a part of Tweeter class which will allow us to tweet
	def tweet(self):
		tweet_text = input("Enter your tweet here:	")

		driver = self.driver
		sleep(3)

		# finds the compose tweet button
		tweet_btn = driver.find_element_by_xpath("//a[@aria-label=\"Tweet\"]")
		tweet_btn.click()

pranshuj73

from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from time import sleep

class Tweeter:
	@classmethod
	def login(self):
		username = "your username/email will go here"
		password = "and your password shall go here"
		

pranshuj73

from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import time

class Tweeter:
	def __init__(self, username, password):
		self.username = username
		self.password = password
		self.driver = webdriver.Chrome() # opens up the browser window

pranshuj73

classes = {
    0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'
}

pranshuj73

# this is for the transforms
train_trfm = transforms.Compose(
    [
        transforms.ToPILImage(),
        transforms.Grayscale(num_output_channels=1),
        transforms.RandomCrop(48, padding=4, padding_mode='reflect'),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.5), (0.5), inplace=True)
    ])

pranshuj73

# Creating the class for our dataset for the FER
class FERDataset(Dataset):
    
    def __init__(self, images, labels, transforms):
        self.X = images
        self.y = labels
        self.transforms = transforms
        
    def __len__(self):
        return len(self.X)

pranshuj73

batch_num = 400

train_dl = DataLoader(train_data, batch_num, shuffle=True, num_workers=4, pin_memory=True)
val_dl = DataLoader(val_data, batch_num*2, num_workers=4, pin_memory=True)

pranshuj73

def accuracy(outputs, labels):
    _, preds = torch.max(outputs, dim=1)
    return torch.tensor(torch.sum(preds==labels).item()/len(preds))

class FERBase(nn.Module):
    
    # this takes is batch from training dl
    def training_step(self, batch):
        images, labels = batch
        out = self(images)                     # calls the training model and generates predictions