Kakarot-2000

@bot.command(name='play_song', help='To play song')
async def play(ctx,url):
    
    if not ctx.message.author.name=="Rohan Krishna" :
         await ctx.send('NOT AUTHORISED!')
         return
    try :
        server = ctx.message.guild
        voice_channel = server.voice_client

Kakarot-2000

@bot.command(name='play_song', help='To play song')
async def play(ctx,url):
    try :
        server = ctx.message.guild
        voice_channel = server.voice_client

        async with ctx.typing():
            filename = await YTDLSource.from_url(url, loop=bot.loop)
            voice_channel.play(discord.FFmpegPCMAudio(executable="ffmpeg.exe", source=filename))
        await ctx.send('**Now playing:** {}'.format(filename))

Kakarot-2000

if __name__ == "__main__" :
    bot.run(DISCORD_TOKEN)

Kakarot-2000

@bot.event
async def on_ready():
    for guild in bot.guilds:
        for channel in guild.text_channels :
            if str(channel) == "general" :
                await channel.send('Bot Activated..')
                await channel.send(file=discord.File('add_gif_file_name_here.png'))
        print('Active in {}\n Member Count : {}'.format(guild.name,guild.member_count))

Kakarot-2000

@bot.command(help = "Prints details of Server")
async def where_am_i(ctx):
    owner=str(ctx.guild.owner)
    region = str(ctx.guild.region)
    guild_id = str(ctx.guild.id)
    memberCount = str(ctx.guild.member_count)
    icon = str(ctx.guild.icon_url)
    desc=ctx.guild.description
    
    embed = discord.Embed(

Kakarot-2000

discord==1.0.1
discord.py==1.6.0
python-dotenv==0.15.0
youtube-dl==2021.2.10

Kakarot-2000

import tensorflow as tf
import numpy as np
import pandas as pd
import cv2
import keras
from tensorflow.keras.applications.vgg16 import VGG16,preprocess_input
from tensorflow.keras.applications import ResNet50

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'):

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

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',