Mohammad Hafiz bin Ismail (mypapit) mypapit

## casaos-ptrerodactyle-panel.sh
#https://github.com/bigbeartechworld/big-bear-casaos/tree/master/Apps/pterodactyl-panel

bash -c "$(wget -qLO - https://raw.githubusercontent.com/bigbeartechworld/big-bear-scripts/master/start-pterodactyl-panel/run.sh)"

## install-pytorch-cuda.py
#conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia.

import torch

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

# Assuming that we are on a CUDA machine, this should print a CUDA device:

print(device)

## nginxlogip.sh
#!/bin/bash

# Check if a log file was specified
if [ "$#" -ne 1 ]; then
    echo "Usage: $0 access.log"
    exit 1
fi


LOG_FILE="$1"

## generate_mykad.py
import random

#this is to generate fake mykad numbers

def generate_mykad_number():
    # Generate date of birth (YYMMDD)
    year = str(random.randint(50, 99)) # assuming a range for adult ages
    month = str(random.randint(1, 12)).zfill(2)
    day = str(random.randint(1, 28)).zfill(2) # using 28 to avoid issues with February
    dob = year + month + day

## gist:c30ca56d06ed7002b7fe4848e661edc7
sudo certbot certonly --webroot -w /path/to/your/web/root -d fakap.net

## ojs3-proxy-sites-available.conf
# Mohammad Hafiz bin Ismail <mypapit@gmail.com>
# blog.mypapit.net
# this is for Ubuntu/Debian Apache server config

#/etc/apache2/sites-available/your-ojs.conf


<VirtualHost *:9080>
        ServerAdmin ojs@example.com
        ServerName ojs-site.example.com

## kerascv-retinanet1.py
from keras_cv import RetinaNet

# Define global configuration parameters
backbone = "resnet50"
weights = "imagenet"
num_classes = 20
batch_size = 2

# Load the custom Pascal VOC dataset
dataset = RetinaNet.load_pascal_voc("path/to/dataset", batch_size=batch_size)

## cgpt-classify-attn.py
from keras.layers import Input, Conv2D, BatchNormalization, Activation, MaxPooling2D, Flatten, Dense, Dropout, Reshape, Permute, multiply, Lambda
from keras.models import Model
from keras import backend as K

# Define the attention mechanism layer
def attention(x):
    f = Conv2D(filters=64, kernel_size=1, strides=1, padding='same')(x)
    g = Conv2D(filters=64, kernel_size=1, strides=1, padding='same')(x)
    h = Conv2D(filters=256, kernel_size=1, strides=1, padding='same')(x)


## Chatgpt-headlines.py
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM
from keras.callbacks import ModelCheckpoint
from keras.utils import np_utils

# Load and preprocess the news headlines dataset
filename = "news_headlines.txt"
text = open(filename).read()
text = text.lower()

## robot-3d.html
<html>
  <head>
    <style>
      canvas {
        width: 100%;
        height: 100%;
      }
    </style>
  </head>
  <body>
	#https://github.com/bigbeartechworld/big-bear-casaos/tree/master/Apps/pterodactyl-panel

	bash -c "$(wget -qLO - https://raw.githubusercontent.com/bigbeartechworld/big-bear-scripts/master/start-pterodactyl-panel/run.sh)"
	#conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia.

	import torch

	device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

	# Assuming that we are on a CUDA machine, this should print a CUDA device:

	print(device)
	#!/bin/bash

	# Check if a log file was specified
	if [ "$#" -ne 1 ]; then
	echo "Usage: $0 access.log"
	exit 1
	fi


	LOG_FILE="$1"
	import random

	#this is to generate fake mykad numbers

	def generate_mykad_number():
	# Generate date of birth (YYMMDD)
	year = str(random.randint(50, 99)) # assuming a range for adult ages
	month = str(random.randint(1, 12)).zfill(2)
	day = str(random.randint(1, 28)).zfill(2) # using 28 to avoid issues with February
	dob = year + month + day
	# Mohammad Hafiz bin Ismail <mypapit@gmail.com>
	# blog.mypapit.net
	# this is for Ubuntu/Debian Apache server config

	#/etc/apache2/sites-available/your-ojs.conf


	<VirtualHost *:9080>
	ServerAdmin ojs@example.com
	ServerName ojs-site.example.com
	from keras_cv import RetinaNet

	# Define global configuration parameters
	backbone = "resnet50"
	weights = "imagenet"
	num_classes = 20
	batch_size = 2

	# Load the custom Pascal VOC dataset
	dataset = RetinaNet.load_pascal_voc("path/to/dataset", batch_size=batch_size)
	from keras.layers import Input, Conv2D, BatchNormalization, Activation, MaxPooling2D, Flatten, Dense, Dropout, Reshape, Permute, multiply, Lambda
	from keras.models import Model
	from keras import backend as K

	# Define the attention mechanism layer
	def attention(x):
	f = Conv2D(filters=64, kernel_size=1, strides=1, padding='same')(x)
	g = Conv2D(filters=64, kernel_size=1, strides=1, padding='same')(x)
	h = Conv2D(filters=256, kernel_size=1, strides=1, padding='same')(x)
	import numpy as np
	from keras.models import Sequential
	from keras.layers import Dense, Dropout, LSTM
	from keras.callbacks import ModelCheckpoint
	from keras.utils import np_utils

	# Load and preprocess the news headlines dataset
	filename = "news_headlines.txt"
	text = open(filename).read()
	text = text.lower()
	<html>
	<head>
	<style>
	canvas {
	width: 100%;
	height: 100%;
	}
	</style>
	</head>
	<body>