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Transcript 

PART - 1

0:00

today we will take chargeipity to the next level you will learn step by step

0:06

how to build your own customized version of charging PT by combining the power of

0:12

python scripting with Advanced charge EPT prompts this will help you do things

0:18

you never thought about even GPT 4 and the latest charge epd plugins can do

0:25

this free course is designed in a way that total beginners can follow up and

0:30

advanced users can learn something new so it's for anyone who wants to take their skills the next level this course

0:37

is divided into two main parts in part one we will learn python scripting in a

0:43

new way you will see in a few minutes you'll become a python developer and in part two we will apply scripting with

0:51

Advanced prompts to build a real world example something that will blow your mind the course has time stamps so you

0:58

can jump between such questions but I prefer not to skip anything even if you

1:03

already know some Concepts please some patients stick with the course because I will share with you a lot of tips and

1:09

tricks that will save you a lot of time achieving a lot of tasks before we dive in maybe you are wondering why should I

Why should you learn Python today?

1:17

learn python coding or scripting and charge EPT and language models can do

1:22

this now you can ask charging to write a python script and we'll do this in seconds so why you should learn this I

1:29

think there are two simple reasons to do this number one is even though charge

1:35

EPT can write python code or any programming language and maybe in the near future it will become better than

1:42

many programmers if you want to use this code and your own applications or to do

1:48

something you need to understand it so this is why you need to learn basic python scripting to understand the codes

1:54

at least the second reason is by learning this skill you will notice a

2:00

mind shift that will help you see a lot of opportunities to develop a lot of things you never thought about before

2:06

this skill will help you a lot in different fields even in machine learning and AI in the analysis and

2:13

building custom Solutions in custom scenarios like in our case building custom charge GPT versions and so on

2:20

believe me this will help you a lot in your career in your life so I think that's enough let's start with step

Step-1 Install Visual Studio Code

2:27

number one and download the application that allows us to write python code you

2:33

know we are writing python so we need an application to write python inside and

2:38

one of the best is Visual Studio code simply go to Google and search for

2:44

visual studio code and click on the download link here and then simply

2:50

select your operating system in my case it's Windows just click here download it

2:55

super simple and install the application I don't want to waste time now just calling the wizard it's simple and then

3:02

open the application and here we are you will see something like this this is called Visual Studio code the

3:08

application where we write our Python scripts step number one done step number

Steo-2 Install Python

3:14

two we need to install python on our computer on our PC so again go here and

3:20

search for python download anything and go and go to the first link on Google

3:27

then simply click on download python if you have Mac OS or Linux or something and change the operating system here in

3:34

my case it's windows I click on download python I already did this again it's

3:39

simple just install it just make sure while installing to tick the check box that adds python to environmental

3:47

variables this will help you save a lot of time make sure to do this and we are ready and to ensure that python is

3:53

working on your PC just go here to terminal new terminal and simply write python Dash capital V enter and you can

4:02

see now we have python 3.11 so python is installed and we are ready to start

4:08

coding and learning python scripting as I mentioned this course is somehow different I structured it in a way to

We Will Learn Fast With chatGPT

4:16

Learn Python as fast as possible to let all the concepts needed in few minutes with the help of chargept and AI let me

4:24

explain here I am inside chargpt look at this simple prompt I prepared for you I

4:30

will paste it here you can see here act as a computer science teacher create tutorial for me blah blah blah the

4:37

language is python the topic is and we have here two empty square brackets so

4:43

what we can do simply is use this prompt anytime in charge EPT to learn and review any concept and python you want

4:50

let's say you want to explain and get some examples about variables in Python don't worry now I will tell you what

4:56

this variables in a little bit but just an example now variable enter and now charging PT will explain

5:03

the concept for you and give you some examples so anytime if you want to

5:08

review something you can simply just change the topic here and review it and get the examples directly inside charge

5:14

EPT I will stop it for now create a new chat and let's start with the first

Variables & Lists

5:19

concept in Python scripting which is variables so what is a variable to make

5:26

things simple for you it's simply like think about it as a box a container that

5:32

you can put data inside that's it the data can be taxed can be images can be

5:39

numbers anything any type of data can be put inside this box which is the

5:45

variable it holds any variable data and this variable in your computer is stored

5:51

in the memory in the ram let's do a simple example I will go here to visual studio I will click on open folder I

5:59

will create a new folder Learn Python whatever open and select folder and here

6:07

we are click now on new file select python file I will save it inside this

6:13

folder and I will call it app.py py stands for the python

6:19

extension so click on Save and now we have a file inside our folder and we are ready to start writing python code how

6:27

to define a variable in Python how to create a variable how to create this box and insert that inside the first step is

6:35

to give a name to your container to your variable to your box any name you want for example my first box that's it

6:44

equals what do you want to put inside this box inside this variable for

6:49

example for now I will put a number like number five we defined a variable that's

6:56

it let's define a new variable and I will put inside it and other number like my second box is equal to again five

7:07

that's simple so now we have two containers containing two numbers let's make this six let's define a new

7:15

variable and now I will put inside it some text my third box is equal and to

7:22

add text now we can't simply write the text like this we need to put inside a

7:28

code and now we have a third variable with text inside it's that simple

7:34

usually we give friendly names for our variables to understand what's inside it

7:39

so here I will say my first number and here I will say my second

7:45

number and here I will say my name in this way the variable will point you

7:51

what's inside it a number text a name anything I tried my best to explain the

7:57

ID and the easiest way I can so this is the first concept in the programming which is variables now this variable can

8:04

hold one thing in our case one number or one tax what if you want to build a big

8:10

box of variables containing multiple numbers or multiple texts or multiple

8:16

names whatever multiple things in Python we have something called a list so

8:22

let's define a list let's create a list but this time I will use the help of

8:27

Judge PT I want to teach you how to fish How to Learn Python so I will open

8:32

charge Equity again I will write this simple comment and I will go here and

8:37

say lists the topic is lists enter just wait a little bit now and charge GPT

8:44

will be your AI teacher a list is a collection of values stored in a single

8:50

variable so it's a variable that holds multiple values and this is how you can

8:55

define a list let's get it I will copy this code copy go here and paste it so

9:01

this is how you can define a list I will call it list of numbers so this is a list of

9:10

numbers containing multiple numbers I will copy the same now and say list of

9:15

names and now I will change this to text so in this way we have one list

9:22

containing multiple values multiple tags we call text in programming strings so

9:28

this is a string this is a string and there's a string so you learned now the

9:33

first two concepts and programming on python which is variables and lists

Conditions

9:38

let's now move on to something called conditions or what we call if

9:44

conditional statements what's a condition it's simply telling your

9:49

computer or the python script to run a specific code in case the condition is

9:56

met that's simple I will give you a direct example now with the help of charging PT again let's go back and copy

10:03

the same prompt paste it and I would say here con

10:09

conditions enter and here is the basic example of a condition statement I will

10:14

copy it I will go here I will paste the same code and you can see we are saying

10:19

if it's pure English if this variable is called num we don't have it here I will

10:25

just replace with my own variable if my first number is greater than 0 I will

10:31

print on the screen so this is the piece of code that we will execute or run if

10:37

the condition is met right the number is positive else if it's not greater than 0

10:42

right is negative I will delete everything for now I will keep only this

10:49

piece of code and now if I execute it must try the number is positive because

10:55

the variable is positive let's test it I will open here a new terminal and simply

11:01

say to run the code just say Python and the name of the file which is app or

11:07

app.py app dot py so this is how you can run the python script enter and you can

11:13

see now it Row the number is positive so this is what we call a conditional statement you just set the condition you

11:21

add this colon here and you write the code that you want to write or you want to execute or run if the condition is

Loops

11:28

met that concept is done let's move on now to concept number four what we call

11:34

a loop l-o-oop you see these O's remember a

11:39

loop is simply repeating a specific piece of code a lot of times maybe three

11:45

maybe five and maybe one billion times in some cases so how to create a loop in

11:52

Python let me first show you a weird example I want to print on the screen

11:58

numbers from 1 to 10. so I can say print one then I will copy the same code print

12:05

2 and so on so here I have repeated the same function or the same code 10 times

12:13

to print numbers from 1 to 10. whenever you see something like this you should

12:19

think about a loop we can replace this with a simple Loop that counts from 1 to 10. how to create a loop how to write a

12:26

loop in Python let's see again you can always use chargpt to help you learn

12:32

python so copy the code again paste it and say here instead of conditions say

12:39

Loops enter it will Define Loops repeat a block of code multiple times it's that

12:46

simple so our prompt will give you three examples one easy example one medium

12:52

example and one somehow hot example it's not very hard but somehow Advanced so the first example of Loops is to Loop

13:00

inside a list remember list a list containing multiple variables you can do this by saying for name and this list

13:07

names print name and to print the output like that super simple medium example

13:13

calculating the sum of numbers inside a list forget about it I want to get this one I will copy I will delete all these

13:21

weird sentences I will say here delete the variable also and for I in range

13:26

from 1 to 10 simply print I look at this I will save

13:33

a new terminal run the code and you can see now it writes from one to nine y

13:39

nine not ten simply because encoding in Python especially when you run in a Range you are going from the first index

13:46

or number to the last minus one so if you want to go to 10 we say simply 11

13:52

and clear run again and here we are one to ten so we are saying here simply is

13:59

telling python on our computer to go inside the range from 1 to 11 and print

14:05

the number I so I is going from one then two then three then four then five and

14:11

so on we are looping let's do another example I will Define a list of numbers

14:17

I will say less of numbers is equal to one two three four and five that's

14:24

simple and now I want a loop inside this list and print the numbers I will say

14:31

for number and list of numbers simply print

14:38

number so pretty easy we are saying for each number inside this list just print the number terminal new terminal

14:45

goal and one to five so I will printed all the values inside this list this

14:52

concept number four which is Loops let's now move on to concept number five which

Functions

14:58

has functions and methods let's delete everything here I will go back to charge

15:03

EPT now I will do a small change to this prompt look at this I think you will

15:08

love it a new chart paste and at the end of this prompt I will add this simple

15:14

statement and make sure to explain all this for a seven years old child so in

15:20

this way charging PT will help you learn coding and Python and scripting as if

15:26

you are a seven years old child I will go here and say functions

15:31

enter and look now at the example a function is a block of code that

15:37

performs a specific task we use functions to break up large programs into smaller manageable pieces this

15:44

makes our code easier to read how to create a function and you can see now explanation for a seven years old a

15:51

function is like a recipe just like we use a recipe to make our favorite dish we can use a function to perform a

15:58

specific task in our code you can see now the examples is for little children's so if you are finding

16:04

something difficult to understand you can simply tell charge APD to explain it as if you are a child and you will see

16:10

the interesting and funny examples that will help you understand complex topics and easy and simple way so how to create

16:18

a function I will copy this code go back here and paste it so my explanation

16:25

what's the function it's simply a piece of code that you can run anywhere in

16:30

your code so this is a function we say def for Define we are defining a

16:36

function and this is the name of the function and we add these two parentheses this is how we can create a

16:42

function and this function simply prints this string or text hello world let me

16:49

show you this if you open now the terminal and run the script

16:54

nothing will happen we have a function but we didn't use it to use it we just

16:59

we just simply copy the name of the function like this we call it and now it

17:06

printed the output let me now combine what we learn in a simple example so

17:12

close this I will Define a simple function that calculates the sum of two

17:18

numbers sum and now inside the function we can Define what we call a parameters

17:24

something that we pass this function we give to this function and the function can use them I will say num1 and num2 so

17:33

I want to get the sum of number one plus number two and now I will tell this

17:39

function to calculate the sum I will say sum is equal to num1 plus num2 that's

17:48

simple you can see how easy it is I think it's very clear we are just calculating the sum using this plus of

17:54

operation if you want to multiply you can add an asterisk if you want to divide you can divide and so on now I

18:00

want to get the sum we are using a simple operation and then I will tell the function to return the value so I

18:07

will say return sum and that's it now we have a function that Returns the sum of

18:13

any two numbers how we call it we say sum and we pass the numbers we want like

18:19

5 and 10. so what's the output let's see if we go here terminal new terminal run

18:26

the code and nothing why because simply it calculates the value which is 15 the

18:34

answer but we forgot to print the value on the screen so simply you can say

18:40

print which is a function by the way built in inside python somewhere and now

18:46

it will print the sum on again and here we are we have 15. so this is what we

18:51

call a function we Define a function to use it anywhere in our code we still have only one concept are you tired

Modules

18:58

let's see the last Concept in this mini course mini python course fast course is

19:04

what we call a model think about a model as a python file like this one python script that holds multiple python

19:12

functions and you can read this file from any script you want let me show you an example I think it will become very

19:18

simple so I will go here and create a new file I will call it my model

19:24

sorry model dot p y and now I will copy this function to this file that's simple

19:31

now here if we save and go and run the code again

19:37

it will give you an error this function doesn't exist you see so how to fix this

19:43

what is the function it is in this file in this model so we go here and we tell

19:48

python to import our model my model like

19:53

this as you simply say as and enter your friendly name in my case I will say MD

19:59

which stands for model and now copy this MD and paste it here and say dot now we


PART - 2

20:05

are accessing the functions inside our model let me show you this if we say MD dot you will get the sum function you

20:12

see it's simple now if I run the code terminal run it and here we are we got

20:19

the value so in this way if you have somehow large applications multiple scripts you can create your own models

20:26

or import models from other developers like you're seeing a little bit in part two and then you can use all the

20:32

functionalities inside these models so these are the main concepts of python if

20:37

you understood this you are ready to start coding you can call yourself a python scripter or a python coder since

20:43

you reach this part I think you deserve a gift what I did is I packaged a full

20:49

free python course with examples and some charge epd prompts to help you

20:54

learn more in an ocean template a free template for you you can simply go to

21:00

this link on the screen and get your template you'll find a lot of examples to help you learn python easily and

21:07

don't forget if you have any questions I'll be waiting for you in the comment section below let's now move on to part

21:13

two but before that I want to answer an important question maybe you are

21:19

wondering again why I should do this Learn Python and build custom charge EPT

Very Important

21:26

versions while charging PT build plugins if you didn't know that chargeipity

21:33

announced some new plugins where you can now search the web analyze data like by

21:39

uploading files and connect a lot of services online so chargeupti has a lot

21:44

of more powerful functionalities why we are learning this and open Ai and charge apt is building plugins I'm sure a lot

21:53

of people ask me this question even though I'm answering it now I will get this question from some people with

21:58

weird mentalities so simply the first reason to learn this is open Ai and

22:05

charging PT can't and it will never handle everything on this planet every

22:10

single scenario so in order to build your own custom solution to your own

22:16

custom problem you need to use your own apis your own data and sometimes in some

22:23

cases you need to use something that charging PT doesn't build a plugin for so you need to learn this the second

22:29

thing is I think that in the near future when charging paterial is the plug ends

22:35

we may have a Marketplace for GPT plugins maybe charge a PT or open AI

22:41

will allow you to create your own plugins and sell them like in WordPress and Shopify and other services where you

22:48

can build custom plugins and users can use or buy and so on so learning this

22:54

skill will help you build custom charge epd plugins the third reason is the

23:00

built-in plug and the charge EPT that will come soon or closed you don't know

23:05

how they work what they are doing in the back end so maybe the output or the results of these plugins are not good

23:11

for you you want something better something more accurate some something different again you need to develop your

23:17

own custom solution to your own custom problem where these plugins can solve

23:22

and number four and I think it's the most important by learning these skills and these techniques you will change the

23:29

way you think you're widen your view and this will help you discover new ideas opportunities so please shift your

Part-2

23:37

mindset and invest in yourself learning these skills and improving yourself will

23:42

change your life forever let's now move on to the second part in our course and

23:47

build something special something mind-blowing so what we will build let

23:53

me show you the output what you will build now with me if you follow up and then dive in and start scripting and

24:00

building this custom version of charging PT so this is our custom application for

What we will build

24:06

today it's an advanced chargpt version allows you to analyze live crypto prices

24:13

it's simple we have this one button here if we click on it now it will get live

24:19

Bitcoin prices and analyze them and will give you in-depth analysis for these prices it will give you price overview

24:26

for the latest data moving averages RSI macd advice and suggestion if you want

24:34

to buy or not and so on please big disclaimer don't use this prototype to

24:39

buy or sell Bitcoin it's a simple prototype and we are learning prompt engineering here and Python and not

24:46

Bitcoin trading maybe in different courses you will learn something about trading but for now we are talking about

24:51

prompt engineering charge apt and python so focus on the topic but this example

24:57

just to show you the power of connecting charge EPT with python scripting with

25:03

Advanced prompts you can get live data and then analyze this data okay so this

25:09

is the application that we will build right now in few minutes let's close it and go back to or work and start coding

Let’s Start

25:17

our custom charge GPT version the first step is to understand how to connect

Connect Python With ChatGPT API

25:23

python with chargpt API if you don't know what's an API think about it like a

25:30

connector a broker that allows you as a developer to connect with services for

25:36

example if you are on Twitter and you want to post a tweet usually you open

25:42

Twitter and you click on you write your post and click on tweet you use the interface what if you want to post on

25:49

Twitter using your own application like building social media automation software you need something that allows

25:55

you to access services so Twitter will give you an API that allows you to

26:00

access Twitter the same for charge apt if you want to use charging PT as any user you go here charging PT website and

26:08

you start entering your prompts yes what if you want to access the power of

26:13

chargeability inside your own custom applications like in our case and on Python scripts you need to use chargpt

26:21

API what you do simply is you import you remember models are going to import open

26:29

AI what's the open AI it's the company behind chat GPT now for the first time

26:36

you need to install openai so what you do is go here to terminal again and you

26:42

write this comment pip or pip install open AI enter it will download

26:49

and install all the packages and models everything and install on your PC the

26:56

second step is to set the authentication API key you can simply set it like this

27:02

open ai.api key and set the key where do you find the key in your account just go

27:07

to open AR if you don't have an account sign up I don't want to waste now time signing up simply go to account then

27:14

view API keys and you'll find your keys here and you can click on create a new security key copy it and paste it here

27:22

now just small node it's not the best way to enter API and secret keys and

27:28

applications it's better to use something called OS environmental variables but for now to keep things simple for you I'm using this as a

27:35

simple clear text or string so we set the key now we are ready to connect with

27:40

the API and communicate with chargpt let me show you the simple function I created for you that you can use anytime

27:48

to communicate with chargpt so I will paste it and here we are it's called basic generation you can call it

27:54

whatever you want and you remember the parameters we learned in the first part this is the user prompt it's your prompt

28:00

and here we are simply creating a new variable and we are filling this

28:06

variable from the charge GPT API by using this open AI model and calling

28:11

this function then the create function and passing the model we want which is Jeep GPT 3.5 turbo the AI model we want

28:20

and here is the messages this is the role which is the user and this is the content which is your prom the user prom

28:26

then when you call this we will get the charge EPT completion the response and

28:32

we can read it let's test this so we have a function just paste in your code and now we can simply run it I will say

28:39

prompt anything I will Define it I will tell charging PT to explain

28:45

Python Programming in two sentences for example so this is my prompt now I will

28:53

say I will call the function response is equal call the function and

28:58

pass the prawn I'm splitting it to make things easier for you to understand that I will print the response

29:06

that's it so what we are doing here we are calling chargpt passing this prompt

29:13

and then reading the response and printing on in our application Let's

29:18

test it go to terminal new terminal run

29:23

wait a little bit we are contacting with charge epd and here is the response Python Programming is a high level

29:29

programming language blah blah blah you can see so now we are communicating with

29:34

charging PT but with our code this is the basic example you can just copy this

29:40

the same code and use it in the way you want just change that prompt and you are communicating with chargpt now let's

29:46

move on to the next level we want to analyze Bitcoin prices so for example if

29:53

we go here and say analyze the last seven days of Bitcoin

30:02

prices Let's test this I will go here terminal I will run look at the response

30:07

as an ally language model I don't have real-time access to Market data however

30:14

based on the last available data which is maybe two years or three years ago it will give you a simple analysis so we

Get Live Bitcoin Prices

30:20

don't have access or charge apt you don't have access to real live data so

30:25

what we are going to do before calling this function we are going to give real

30:31

data real life data to charge EBT how to do this let's see so step number two is

30:37

to read live data from where to get this live data we are going to communicate

30:43

with third-party apis let's go open our browser again I will go to website called rapid aepi please Focus this

30:51

website is a Marketplace of apis so now you can access a lot of services and

30:59

things with this website I will go here and search for Bitcoin for example or

31:05

crypto so I want an API that allows me to access crypto prices I tested a lot of

31:11

apis and I found this one coin ranking which is verified and it's free you can

31:17

see it's freemium they have this basic free version with 10 000 monthly calls which is more than enough for us so this

31:24

is an API that you can access from your python code to read Bitcoin live data

31:31

prices if you go here to get coin price history and then go down here and select

31:36

the time period for example 7D for seven days and click on a test endpoint now it

31:44

will give you the last seven days history for this coin price so now we

31:50

can connect with this API to read live Bitcoin prices and what's nice about rapid API they have these code Snippets

31:58

in our case we are using python I'll click on requests and it will give you the code you can see this is the full

32:05

code it can use to read this data you can just copy this code and paste inside

32:10

your application to read the data but I want to make things easier for you I

32:15

prefer another function that reads data from this API and returns back a list of

32:22

price of Bitcoin price Let's test it let's check it so I will delete this I

32:27

will go here and paste my function and you can see it's called get Bitcoin

32:33

prices without parameters we are getting the last seven days you can see this code here let's minimized a little bit

32:39

so this is the URL you can see this code here is the code inside rapid API where

32:46

we are this code here just copied and pasted and I added some comments so you

32:52

can understand each sentence or each line of code what does it mean and you can notice we have these yellow lines on

32:59

requests and Json we need to import them in our code to use them so go back here to models again you need to import them

33:06

or set import Json and import

33:11

quests and if we go now down you can see now everything is perfect what is

33:16

request it's a model that has this request function that allows you to communicate with apis that's simple what

33:23

does Json it's simply a library or a model that allows you to read Json data what does Json data if you go back here

33:30

to Rapid API and execute this test endpoint you can see now this is the

33:36

response from the API we call this a Json it's like a text file you need to

33:41

read it to parse it and read it so this library or this model here helps you read this file easily I want to access

33:48

the history I will say Json result data history what this means if you go back

33:53

here to Rapid API you can see here we have the history we have data and then we have history and we have the prices

34:00

inside so we have data or data history then we Loop you can see the loop here

34:05

we are looping inside history to read all the prices let's go back to show you this you can see we have a lot of of

34:11

values here so we are looping inside the history variable or the history list to

34:17

read all the prices and we created a list of price to return to the user so

34:22

this is the function that returns Bitcoin prices for the last seven days live data now we need to combine this

Combine With Advanced Prompt

34:31

function with an advanced charge GPT prompt to analyze and give you in-depth

34:37

analysis to the latest Bitcoin prices so I will paste now my Advanced chargpt

34:43

prompt it's a simple string you can see it here you're an expert crypto Trader

34:48

with more than 10 years of experience I'll provide you with a list of Bitcoin prices for the last seven days can you

34:54

provide me with a technical analysis of Bitcoin based on these prices here's what I want price of review moving

35:02

averages relative strength index advice suggestions do I buy or sell and please

35:07

be detailed as you can as much as you can and explain in a way any beginner

35:13

can understand and here is the price list and you'd pass the Bitcoin prices inside this string so I will Define this

35:22

variable Bitcoin prices and simply I will call this function to get the

35:27

prices now we have the prices we have our Advanced prompt we can call chargpt

35:33

to analyze the data I will say analysis for example I will call this function

35:39

here basic generation and then I will pass the chargpt prompt you can see how

35:46

we combine things we got the prices we have the connection with charge apt and

35:52

we have the advanced prom we combine them here together to get the analysis

35:57

based on live prices and don't forget we want to print the analysis at the end so

36:03

we can read it let's run it and test what we did let's wait a little bit and

36:09

here we are the in-depth and analysis based on our own New Life prices you can

36:16

see this is the price of review moving averages and so on do I buy or sell and

36:21

so on again don't forget this is a prototype please don't use it to buy or sell Bitcoin we are learning Python and

36:29

charge DPT Advanced prompt engineering and not Bitcoin trading so our

Create the UI

36:34

application is almost done but the question now how we can build a nice

36:40

interface that you saw at the beginning of this section easily let me show you some magic I will go here and import

36:48

a model or a package called stream lit and I will name it St for example we

36:55

need to install it so I will copy around the terminal what do we say

37:02

install streamlit enter just wait a little bit and simply now go

37:08

here and say St dot title is equal to charge EPT Advanced prompting with

37:14

python anything you want we can say Bitcoin analyzer with

37:21

charge GPT and the sub header of this page is

37:27

example sub heading or maybe

37:32

subscribe to my channel for example so I will delete this code and now I will go

37:39

to terminal new terminal and instead of saying python app I will say

37:46

dreamlit run and the file name enter and you can see now it opened the browser

37:53

and we have the heading and the subheading subscribe to my channel so I

37:58

will close it close the Powershell again and now we need to create a button with

38:03

a loading to run these functions and to do that I will change the code from this

38:09

to this again I'm using streamnet library or model to create a button and

38:17

the text in the button is analyze and then I will use a spinner to show a loading to get the Bitcoin price first

38:24

so I call the function here and when it's done it will show a success message done then it will start analyzing

38:30

Bitcoin prices using charge EBT then it will show you again done and show you

38:36

the result tax area streamlit is a very powerful library or model that allows

38:41

you to build interfaces very easily as you can see if you go to streamline.i oh you can see here a faster way to build

38:48

and share data apps it's super simple you can see the gallery a lot of examples charts building graphs a lot of

38:55

things you can build with this awesome Library so anyway you can check it if you want we finished our code let's try

39:02

running it again new terminal and run and here we are this is our application

39:09

click on allies getting Bitcoin prices we got the live price and now it's analyzing it and it will show the result

39:16

inside this text area you can see I used exactly the same method to build my own

39:22

custom Char GPT Max Edition that not only analyze Bitcoin prices it can

39:27

generate images and a lot of other stuff I show you this in detail in this video

39:32

how I created chargpt Max you can check the video here if you have any questions I'll be waiting for you in the comment

39:39

section and don't forget to get all the details the codes description below see you later


PythonGPT

How to build a ChatGPT-like clone in Python



Python Classes and Objects - OOP for Beginners


Python Simplified - YT CHANNEL

OOP Class Inheritance and Private Class Members - Python for Beginners!

ALL 5 STAR AI.IO PAGE STUDY

How AI and IoT are Creating An Impact On Industries Today

Hello and welcome to our new site that shares with you the most powerful web platforms and tools available on the web today

All platforms, websites and tools have artificial intelligence AI and have a 5-star rating

All platforms, websites and tools are free and Pro paid

The platforms, websites and the tool's  are the best  for growing your business in 2022/3

שלום וברוכים הבאים לאתר החדש שלנו המשתף אתכם בפלטפורמות האינטרנט והכלים החזקים ביותר הקיימים היום ברשת. כל הפלטפורמות, האתרים והכלים הם בעלי בינה מלאכותית AI ובעלי דירוג של 5 כוכבים. כל הפלטפורמות, האתרים והכלים חינמיים ומקצועיים בתשלום הפלטפורמות, האתרים והכלים באתר זה הם הטובים ביותר  והמועילים ביותר להצמחת ולהגדלת העסק שלך ב-2022/3 

A Guide for AI-Enhancing Your Existing Business Application

A guide to improving your existing business application of artificial intelligence

מדריך לשיפור היישום העסקי הקיים שלך בינה מלאכותית

What is Artificial Intelligence and how does it work? What are the 3 types of AI?

What is Artificial Intelligence and how does it work? What are the 3 types of AI? The 3 types of AI are: General AI: AI that can perform all of the intellectual tasks a human can. Currently, no form of AI can think abstractly or develop creative ideas in the same ways as humans.  Narrow AI: Narrow AI commonly includes visual recognition and natural language processing (NLP) technologies. It is a powerful tool for completing routine jobs based on common knowledge, such as playing music on demand via a voice-enabled device.  Broad AI: Broad AI typically relies on exclusive data sets associated with the business in question. It is generally considered the most useful AI category for a business. Business leaders will integrate a broad AI solution with a specific business process where enterprise-specific knowledge is required.  How can artificial intelligence be used in business? AI is providing new ways for humans to engage with machines, transitioning personnel from pure digital experiences to human-like natural interactions. This is called cognitive engagement.  AI is augmenting and improving how humans absorb and process information, often in real-time. This is called cognitive insights and knowledge management. Beyond process automation, AI is facilitating knowledge-intensive business decisions, mimicking complex human intelligence. This is called cognitive automation.  What are the different artificial intelligence technologies in business? Machine learning, deep learning, robotics, computer vision, cognitive computing, artificial general intelligence, natural language processing, and knowledge reasoning are some of the most common business applications of AI.  What is the difference between artificial intelligence and machine learning and deep learning? Artificial intelligence (AI) applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions.  Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.  Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.  What are the current and future capabilities of artificial intelligence? Current capabilities of AI include examples such as personal assistants (Siri, Alexa, Google Home), smart cars (Tesla), behavioral adaptation to improve the emotional intelligence of customer support representatives, using machine learning and predictive algorithms to improve the customer’s experience, transactional AI like that of Amazon, personalized content recommendations (Netflix), voice control, and learning thermostats.  Future capabilities of AI might probably include fully autonomous cars, precision farming, future air traffic controllers, future classrooms with ambient informatics, urban systems, smart cities and so on.  To know more about the scope of artificial intelligence in your business, please connect with our expert.

מהי בינה מלאכותית וכיצד היא פועלת? מהם 3 סוגי הבינה המלאכותית?

מהי בינה מלאכותית וכיצד היא פועלת? מהם 3 סוגי הבינה המלאכותית? שלושת סוגי הבינה המלאכותית הם: בינה מלאכותית כללית: בינה מלאכותית שיכולה לבצע את כל המשימות האינטלקטואליות שאדם יכול. נכון לעכשיו, שום צורה של AI לא יכולה לחשוב בצורה מופשטת או לפתח רעיונות יצירתיים באותן דרכים כמו בני אדם. בינה מלאכותית צרה: בינה מלאכותית צרה כוללת בדרך כלל טכנולוגיות זיהוי חזותי ועיבוד שפה טבעית (NLP). זהו כלי רב עוצמה להשלמת עבודות שגרתיות המבוססות על ידע נפוץ, כגון השמעת מוזיקה לפי דרישה באמצעות מכשיר התומך בקול. בינה מלאכותית רחבה: בינה מלאכותית רחבה מסתמכת בדרך כלל על מערכי נתונים בלעדיים הקשורים לעסק המדובר. זה נחשב בדרך כלל לקטגוריית הבינה המלאכותית השימושית ביותר עבור עסק. מנהיגים עסקיים ישלבו פתרון AI רחב עם תהליך עסקי ספציפי שבו נדרש ידע ספציפי לארגון. כיצד ניתן להשתמש בבינה מלאכותית בעסק? AI מספקת דרכים חדשות לבני אדם לעסוק במכונות, ומעבירה את הצוות מחוויות דיגיטליות טהורות לאינטראקציות טבעיות דמויות אדם. זה נקרא מעורבות קוגניטיבית. בינה מלאכותית מגדילה ומשפרת את האופן שבו בני אדם קולטים ומעבדים מידע, לעתים קרובות בזמן אמת. זה נקרא תובנות קוגניטיביות וניהול ידע. מעבר לאוטומציה של תהליכים, AI מאפשר החלטות עסקיות עתירות ידע, תוך חיקוי אינטליגנציה אנושית מורכבת. זה נקרא אוטומציה קוגניטיבית. מהן טכנולוגיות הבינה המלאכותית השונות בעסק? למידת מכונה, למידה עמוקה, רובוטיקה, ראייה ממוחשבת, מחשוב קוגניטיבי, בינה כללית מלאכותית, עיבוד שפה טבעית וחשיבת ידע הם חלק מהיישומים העסקיים הנפוצים ביותר של AI. מה ההבדל בין בינה מלאכותית ולמידת מכונה ולמידה עמוקה? בינה מלאכותית (AI) מיישמת ניתוח מתקדמות וטכניקות מבוססות לוגיקה, כולל למידת מכונה, כדי לפרש אירועים, לתמוך ולהפוך החלטות לאוטומטיות ולנקוט פעולות. למידת מכונה היא יישום של בינה מלאכותית (AI) המספק למערכות את היכולת ללמוד ולהשתפר מניסיון באופן אוטומטי מבלי להיות מתוכנתים במפורש. למידה עמוקה היא תת-קבוצה של למידת מכונה בבינה מלאכותית (AI) שיש לה רשתות המסוגלות ללמוד ללא פיקוח מנתונים שאינם מובנים או ללא תווית. מהן היכולות הנוכחיות והעתידיות של בינה מלאכותית? היכולות הנוכחיות של AI כוללות דוגמאות כמו עוזרים אישיים (Siri, Alexa, Google Home), מכוניות חכמות (Tesla), התאמה התנהגותית לשיפור האינטליגנציה הרגשית של נציגי תמיכת לקוחות, שימוש בלמידת מכונה ואלגוריתמים חזויים כדי לשפר את חווית הלקוח, עסקאות בינה מלאכותית כמו זו של אמזון, המלצות תוכן מותאמות אישית (Netflix), שליטה קולית ותרמוסטטים ללמידה. יכולות עתידיות של AI עשויות לכלול כנראה מכוניות אוטונומיות מלאות, חקלאות מדויקת, בקרי תעבורה אוויריים עתידיים, כיתות עתידיות עם אינפורמטיקה סביבתית, מערכות עירוניות, ערים חכמות וכן הלאה. כדי לדעת יותר על היקף הבינה המלאכותית בעסק שלך, אנא צור קשר עם המומחה שלנו.

Glossary of Terms


Application Programming Interface(API):

An API, or application programming interface, is a set of rules and protocols that allows different software programs to communicate and exchange information with each other. It acts as a kind of intermediary, enabling different programs to interact and work together, even if they are not built using the same programming languages or technologies. API's provide a way for different software programs to talk to each other and share data, helping to create a more interconnected and seamless user experience.

Artificial Intelligence(AI):

the intelligence displayed by machines in performing tasks that typically require human intelligence, such as learning, problem-solving, decision-making, and language understanding. AI is achieved by developing algorithms and systems that can process, analyze, and understand large amounts of data and make decisions based on that data.

Compute Unified Device Architecture(CUDA):

CUDA is a way that computers can work on really hard and big problems by breaking them down into smaller pieces and solving them all at the same time. It helps the computer work faster and better by using special parts inside it called GPUs. It's like when you have lots of friends help you do a puzzle - it goes much faster than if you try to do it all by yourself.

The term "CUDA" is a trademark of NVIDIA Corporation, which developed and popularized the technology.

Data Processing:

The process of preparing raw data for use in a machine learning model, including tasks such as cleaning, transforming, and normalizing the data.

Deep Learning(DL):

A subfield of machine learning that uses deep neural networks with many layers to learn complex patterns from data.

Feature Engineering:

The process of selecting and creating new features from the raw data that can be used to improve the performance of a machine learning model.

Freemium:

You might see the term "Freemium" used often on this site. It simply means that the specific tool that you're looking at has both free and paid options. Typically there is very minimal, but unlimited, usage of the tool at a free tier with more access and features introduced in paid tiers.

Generative Art:

Generative art is a form of art that is created using a computer program or algorithm to generate visual or audio output. It often involves the use of randomness or mathematical rules to create unique, unpredictable, and sometimes chaotic results.

Generative Pre-trained Transformer(GPT):

GPT stands for Generative Pretrained Transformer. It is a type of large language model developed by OpenAI.

GitHub:

GitHub is a platform for hosting and collaborating on software projects


Google Colab:

Google Colab is an online platform that allows users to share and run Python scripts in the cloud

Graphics Processing Unit(GPU):

A GPU, or graphics processing unit, is a special type of computer chip that is designed to handle the complex calculations needed to display images and video on a computer or other device. It's like the brain of your computer's graphics system, and it's really good at doing lots of math really fast. GPUs are used in many different types of devices, including computers, phones, and gaming consoles. They are especially useful for tasks that require a lot of processing power, like playing video games, rendering 3D graphics, or running machine learning algorithms.

Large Language Model(LLM):

A type of machine learning model that is trained on a very large amount of text data and is able to generate natural-sounding text.

Machine Learning(ML):

A method of teaching computers to learn from data, without being explicitly programmed.

Natural Language Processing(NLP):

A subfield of AI that focuses on teaching machines to understand, process, and generate human language

Neural Networks:

A type of machine learning algorithm modeled on the structure and function of the brain.

Neural Radiance Fields(NeRF):

Neural Radiance Fields are a type of deep learning model that can be used for a variety of tasks, including image generation, object detection, and segmentation. NeRFs are inspired by the idea of using a neural network to model the radiance of an image, which is a measure of the amount of light that is emitted or reflected by an object.

OpenAI:

OpenAI is a research institute focused on developing and promoting artificial intelligence technologies that are safe, transparent, and beneficial to society

Overfitting:

A common problem in machine learning, in which the model performs well on the training data but poorly on new, unseen data. It occurs when the model is too complex and has learned too many details from the training data, so it doesn't generalize well.

Prompt:

A prompt is a piece of text that is used to prime a large language model and guide its generation

Python:

Python is a popular, high-level programming language known for its simplicity, readability, and flexibility (many AI tools use it)

Reinforcement Learning:

A type of machine learning in which the model learns by trial and error, receiving rewards or punishments for its actions and adjusting its behavior accordingly.

Spatial Computing:

Spatial computing is the use of technology to add digital information and experiences to the physical world. This can include things like augmented reality, where digital information is added to what you see in the real world, or virtual reality, where you can fully immerse yourself in a digital environment. It has many different uses, such as in education, entertainment, and design, and can change how we interact with the world and with each other.

Stable Diffusion:

Stable Diffusion generates complex artistic images based on text prompts. It’s an open source image synthesis AI model available to everyone. Stable Diffusion can be installed locally using code found on GitHub or there are several online user interfaces that also leverage Stable Diffusion models.

Supervised Learning:

A type of machine learning in which the training data is labeled and the model is trained to make predictions based on the relationships between the input data and the corresponding labels.

Unsupervised Learning:

A type of machine learning in which the training data is not labeled, and the model is trained to find patterns and relationships in the data on its own.

Webhook:

A webhook is a way for one computer program to send a message or data to another program over the internet in real-time. It works by sending the message or data to a specific URL, which belongs to the other program. Webhooks are often used to automate processes and make it easier for different programs to communicate and work together. They are a useful tool for developers who want to build custom applications or create integrations between different software systems.


מילון מונחים


ממשק תכנות יישומים (API): API, או ממשק תכנות יישומים, הוא קבוצה של כללים ופרוטוקולים המאפשרים לתוכנות שונות לתקשר ולהחליף מידע ביניהן. הוא פועל כמעין מתווך, המאפשר לתוכניות שונות לקיים אינטראקציה ולעבוד יחד, גם אם הן אינן בנויות באמצעות אותן שפות תכנות או טכנולוגיות. ממשקי API מספקים דרך לתוכנות שונות לדבר ביניהן ולשתף נתונים, ועוזרות ליצור חווית משתמש מקושרת יותר וחלקה יותר. בינה מלאכותית (AI): האינטליגנציה שמוצגת על ידי מכונות בביצוע משימות הדורשות בדרך כלל אינטליגנציה אנושית, כגון למידה, פתרון בעיות, קבלת החלטות והבנת שפה. AI מושגת על ידי פיתוח אלגוריתמים ומערכות שיכולים לעבד, לנתח ולהבין כמויות גדולות של נתונים ולקבל החלטות על סמך הנתונים הללו. Compute Unified Device Architecture (CUDA): CUDA היא דרך שבה מחשבים יכולים לעבוד על בעיות קשות וגדולות באמת על ידי פירוקן לחתיכות קטנות יותר ופתרון כולן בו זמנית. זה עוזר למחשב לעבוד מהר יותר וטוב יותר על ידי שימוש בחלקים מיוחדים בתוכו הנקראים GPUs. זה כמו כשיש לך הרבה חברים שעוזרים לך לעשות פאזל - זה הולך הרבה יותר מהר מאשר אם אתה מנסה לעשות את זה לבד. המונח "CUDA" הוא סימן מסחרי של NVIDIA Corporation, אשר פיתחה והפכה את הטכנולוגיה לפופולרית. עיבוד נתונים: תהליך הכנת נתונים גולמיים לשימוש במודל למידת מכונה, כולל משימות כמו ניקוי, שינוי ונימול של הנתונים. למידה עמוקה (DL): תת-תחום של למידת מכונה המשתמש ברשתות עצביות עמוקות עם רבדים רבים כדי ללמוד דפוסים מורכבים מנתונים. הנדסת תכונות: תהליך הבחירה והיצירה של תכונות חדשות מהנתונים הגולמיים שניתן להשתמש בהם כדי לשפר את הביצועים של מודל למידת מכונה. Freemium: ייתכן שתראה את המונח "Freemium" בשימוש לעתים קרובות באתר זה. זה פשוט אומר שלכלי הספציפי שאתה מסתכל עליו יש אפשרויות חינמיות וגם בתשלום. בדרך כלל יש שימוש מינימלי מאוד, אך בלתי מוגבל, בכלי בשכבה חינמית עם יותר גישה ותכונות שהוצגו בשכבות בתשלום. אמנות גנרטיבית: אמנות גנרטיבית היא צורה של אמנות שנוצרת באמצעות תוכנת מחשב או אלגוריתם ליצירת פלט חזותי או אודיו. לרוב זה כרוך בשימוש באקראיות או בכללים מתמטיים כדי ליצור תוצאות ייחודיות, בלתי צפויות ולעיתים כאוטיות. Generative Pre-trained Transformer(GPT): GPT ראשי תיבות של Generative Pre-trained Transformer. זהו סוג של מודל שפה גדול שפותח על ידי OpenAI. GitHub: GitHub היא פלטפורמה לאירוח ושיתוף פעולה בפרויקטי תוכנה

Google Colab: Google Colab היא פלטפורמה מקוונת המאפשרת למשתמשים לשתף ולהריץ סקריפטים של Python בענן Graphics Processing Unit(GPU): GPU, או יחידת עיבוד גרפית, הוא סוג מיוחד של שבב מחשב שנועד להתמודד עם המורכבות חישובים הדרושים להצגת תמונות ווידאו במחשב או במכשיר אחר. זה כמו המוח של המערכת הגרפית של המחשב שלך, והוא ממש טוב לעשות הרבה מתמטיקה ממש מהר. GPUs משמשים סוגים רבים ושונים של מכשירים, כולל מחשבים, טלפונים וקונסולות משחקים. הם שימושיים במיוחד למשימות הדורשות כוח עיבוד רב, כמו משחקי וידאו, עיבוד גרפיקה תלת-ממדית או הפעלת אלגוריתמים של למידת מכונה. מודל שפה גדול (LLM): סוג של מודל למידת מכונה שאומן על כמות גדולה מאוד של נתוני טקסט ומסוגל ליצור טקסט בעל צליל טבעי. Machine Learning (ML): שיטה ללמד מחשבים ללמוד מנתונים, מבלי להיות מתוכנתים במפורש. עיבוד שפה טבעית (NLP): תת-תחום של AI המתמקד בהוראת מכונות להבין, לעבד וליצור שפה אנושית רשתות עצביות: סוג של אלגוריתם למידת מכונה המבוססת על המבנה והתפקוד של המוח. שדות קרינה עצביים (NeRF): שדות קרינה עצביים הם סוג של מודל למידה עמוקה שיכול לשמש למגוון משימות, כולל יצירת תמונה, זיהוי אובייקטים ופילוח. NeRFs שואבים השראה מהרעיון של שימוש ברשת עצבית למודל של זוהר תמונה, שהוא מדד לכמות האור שנפלט או מוחזר על ידי אובייקט. OpenAI: OpenAI הוא מכון מחקר המתמקד בפיתוח וקידום טכנולוגיות בינה מלאכותית שהן בטוחות, שקופות ומועילות לחברה. Overfitting: בעיה נפוצה בלמידת מכונה, שבה המודל מתפקד היטב בנתוני האימון אך גרועים בחדשים, בלתי נראים. נתונים. זה מתרחש כאשר המודל מורכב מדי ולמד יותר מדי פרטים מנתוני האימון, כך שהוא לא מכליל היטב. הנחיה: הנחיה היא פיסת טקסט המשמשת לתכנון מודל שפה גדול ולהנחות את הדור שלו Python: Python היא שפת תכנות פופולרית ברמה גבוהה הידועה בפשטות, בקריאות ובגמישות שלה (כלי AI רבים משתמשים בה) למידת חיזוק: סוג של למידת מכונה שבה המודל לומד על ידי ניסוי וטעייה, מקבל תגמולים או עונשים על מעשיו ומתאים את התנהגותו בהתאם. מחשוב מרחבי: מחשוב מרחבי הוא השימוש בטכנולוגיה כדי להוסיף מידע וחוויות דיגיטליות לעולם הפיזי. זה יכול לכלול דברים כמו מציאות רבודה, שבה מידע דיגיטלי מתווסף למה שאתה רואה בעולם האמיתי, או מציאות מדומה, שבה אתה יכול לשקוע במלואו בסביבה דיגיטלית. יש לו שימושים רבים ושונים, כמו בחינוך, בידור ועיצוב, והוא יכול לשנות את האופן שבו אנו מתקשרים עם העולם ואחד עם השני. דיפוזיה יציבה: דיפוזיה יציבה מייצרת תמונות אמנותיות מורכבות המבוססות על הנחיות טקסט. זהו מודל AI של סינתזת תמונות בקוד פתוח הזמין לכולם. ניתן להתקין את ה-Stable Diffusion באופן מקומי באמצעות קוד שנמצא ב-GitHub או שישנם מספר ממשקי משתמש מקוונים הממנפים גם מודלים של Stable Diffusion. למידה מפוקחת: סוג של למידת מכונה שבה נתוני האימון מסומנים והמודל מאומן לבצע תחזיות על סמך היחסים בין נתוני הקלט והתוויות המתאימות. למידה ללא פיקוח: סוג של למידת מכונה שבה נתוני האימון אינם מסומנים, והמודל מאומן למצוא דפוסים ויחסים בנתונים בעצמו. Webhook: Webhook הוא דרך של תוכנת מחשב אחת לשלוח הודעה או נתונים לתוכנית אחרת דרך האינטרנט בזמן אמת. זה עובד על ידי שליחת ההודעה או הנתונים לכתובת URL ספציפית, השייכת לתוכנית האחרת. Webhooks משמשים לעתים קרובות כדי להפוך תהליכים לאוטומטיים ולהקל על תוכניות שונות לתקשר ולעבוד יחד. הם כלי שימושי למפתחים שרוצים לבנות יישומים מותאמים אישית או ליצור אינטגרציות בין מערכות תוכנה שונות.

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