Martin Gaido martingaido

## encrypt-decrypt.sh
# Install it in .bashrc file

function encrypt(){

    which openssl &> /dev/null
    [ $? -ne 0 ]  && echo "OPENSSL is not installed." && return 1
    openssl enc -aes-256-cbc -salt -in $1 -out $2
}

function decrypt(){

## house-prices.py
## Predict House prices using linear regression
## Dataset: https://www.kaggle.com/rubenssjr/brasilian-houses-to-rent

import pandas as pd
from sklearn import preprocessing, linear_model
import numpy as np
import sklearn

### Loading Data ###
print('-' * 30); print(' Importing Data ...'); print('-' * 30)

## covidPredictor.py
# COVID-19 cases predictor (polynomial regression for non-linear prediction)
# Data Source: https://ourworldindata.org/coronavirus-source-data

# Data Example
# id,cases
# 1,1
# 2,4
# 3,6
# 4,8
# 5,10

## colorDetectionCam.py
# Color Detection/Filter using OpenCV and Webcam

import cv2
import numpy as np

cap = cv2.VideoCapture(0)

while True:
    _, frame = cap.read()
    hsv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

## speedtest.py
# pip install speedtest-cli

import speedtest

test = speedtest.Speedtest()
down = test.download()
upload = test.upload()

print(f"Download Speed: {down}")
print(f"Upload Speed: {upload}")

## HandsTracker.py
# pip install mediapipe
import cv2
import mediapipe as mp
import time

cap = cv2.VideoCapture(0)

mpHands = mp.solutions.hands
hands = mpHands.Hands() # using default paramaters of 'Hands()'
mpDraw = mp.solutions.drawing_utils

## TaskSmartContract.sol
pragma solidity ^0.8.6;

contract TaskCrud {

    struct Task {
        uint id;
        string name;
        string description;
    }


## text-generator-transformers.py
# pip install torch
# pip install "transformers==4.24.0"
# usage: python text-generator-transformers.py 'some text'

import sys
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel

arg = sys.argv

## Iris.csv

          
            Id
            SepalLengthCm
            SepalWidthCm
            PetalLengthCm
            PetalWidthCm
            Species

            
              1
              5.1
              3.5
              1.4
              0.2
              Iris-setosa

            
              2
              4.9
              3.0
              1.4
              0.2
              Iris-setosa

            
              3
              4.7
              3.2
              1.3
              0.2
              Iris-setosa

            
              4
              4.6
              3.1
              1.5
              0.2
              Iris-setosa

            
              5
              5.0
              3.6
              1.4
              0.2
              Iris-setosa

            
              6
              5.4
              3.9
              1.7
              0.4
              Iris-setosa

            
              7
              4.6
              3.4
              1.4
              0.3
              Iris-setosa

            
              8
              5.0
              3.4
              1.5
              0.2
              Iris-setosa

            
              9
              4.4
              2.9
              1.4
              0.2
              Iris-setosa

## lambdaTweetScraper.py
import json
import snscrape
import snscrape.modules.twitter as sntwitter
import snscrape.modules.facebook as snfacebbok
import snscrape.modules.instagram as sninstagram
import snscrape.modules.mastodon as snmastodon
import snscrape.modules.reddit as smreddit

def lambda_handler(event, context):
	# Install it in .bashrc file

	function encrypt(){

	which openssl &> /dev/null
	[ $? -ne 0 ] && echo "OPENSSL is not installed." && return 1
	openssl enc -aes-256-cbc -salt -in $1 -out $2
	}

	function decrypt(){
	## Predict House prices using linear regression
	## Dataset: https://www.kaggle.com/rubenssjr/brasilian-houses-to-rent

	import pandas as pd
	from sklearn import preprocessing, linear_model
	import numpy as np
	import sklearn

	### Loading Data ###
	print('-' * 30); print(' Importing Data ...'); print('-' * 30)
	# COVID-19 cases predictor (polynomial regression for non-linear prediction)
	# Data Source: https://ourworldindata.org/coronavirus-source-data

	# Data Example
	# id,cases
	# 1,1
	# 2,4
	# 3,6
	# 4,8
	# 5,10
	# Color Detection/Filter using OpenCV and Webcam

	import cv2
	import numpy as np

	cap = cv2.VideoCapture(0)

	while True:
	_, frame = cap.read()
	hsv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
	# pip install speedtest-cli

	import speedtest

	test = speedtest.Speedtest()
	down = test.download()
	upload = test.upload()

	print(f"Download Speed: {down}")
	print(f"Upload Speed: {upload}")
	# pip install mediapipe
	import cv2
	import mediapipe as mp
	import time

	cap = cv2.VideoCapture(0)

	mpHands = mp.solutions.hands
	hands = mpHands.Hands() # using default paramaters of 'Hands()'
	mpDraw = mp.solutions.drawing_utils
	pragma solidity ^0.8.6;

	contract TaskCrud {

	struct Task {
	uint id;
	string name;
	string description;
	}
	# pip install torch
	# pip install "transformers==4.24.0"
	# usage: python text-generator-transformers.py 'some text'

	import sys
	import torch
	from transformers import GPT2Tokenizer, GPT2LMHeadModel

	arg = sys.argv
Id	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	Species
1	5.1	3.5	1.4	0.2	Iris-setosa
2	4.9	3.0	1.4	0.2	Iris-setosa
3	4.7	3.2	1.3	0.2	Iris-setosa
4	4.6	3.1	1.5	0.2	Iris-setosa
5	5.0	3.6	1.4	0.2	Iris-setosa
6	5.4	3.9	1.7	0.4	Iris-setosa
7	4.6	3.4	1.4	0.3	Iris-setosa
8	5.0	3.4	1.5	0.2	Iris-setosa
9	4.4	2.9	1.4	0.2	Iris-setosa
	import json
	import snscrape
	import snscrape.modules.twitter as sntwitter
	import snscrape.modules.facebook as snfacebbok
	import snscrape.modules.instagram as sninstagram
	import snscrape.modules.mastodon as snmastodon
	import snscrape.modules.reddit as smreddit

	def lambda_handler(event, context):