How to Automatically Send Whatsapp Messages using Python

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

Design the PCB using your favorite CAD or drawing software.
Print the top and bottom copper and top silk screen layers to a PDF file.
Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
Use a Gerber viewer to double-check the output against the original PCB design.
Make adjustments as needed.
Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.

pdf2gerb_cfg.pm

#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)


##############################################################################################
#configurable settings:
#change values here instead of in main pfg2gerb.pl file

use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)}Pdf2Gerb.pl ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1); 

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
(
#round or square pads (> 0) and drills (< 0):
    .010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
    .031, -.014,  #used for vias
    .041, -.020,  #smallest non-filled plated hole
    .051, -.025,
    .056, -.029,  #useful for IC pins
    .070, -.033,
    .075, -.040,  #heavier leads
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
    .100, -.046,
    .115, -.052,
    .130, -.061,
    .140, -.067,
    .150, -.079,
    .175, -.088,
    .190, -.093,
    .200, -.100,
    .220, -.110,
    .160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
    .090, -.040,  #want a .090 pad option, but use dummy hole size
    .065, -.040, #.065 x .065 rect pad
    .035, -.040, #.035 x .065 rect pad
#traces:
    .001,  #too thin for real traces; use only for board outlines
    .006,  #minimum real trace width; mainly used for text
    .008,  #mainly used for mid-sized text, not traces
    .010,  #minimum recommended trace width for low-current signals
    .012,
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .025,
    .030,  #heavy-current traces; be careful with these ones!
    .040,
    .050,
    .060,
    .080,
    .100,
    .120,
);
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
{
    HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
    RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
    SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
    RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
    TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
    REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects
};

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
{
    CIRCLE_ADJUST_MINX => 0,
    CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
    CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
    CIRCLE_ADJUST_MAXY => 0,
    SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
    WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
    RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found
};

#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
{
    CAP_NONE => 0, #butt (none); line is exact length
    CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
    CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
    CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
};
    
#number of elements in each shape type:
use constant
{
    RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
    LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
    CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
    CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
};
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
our %SHAPELEN =
(
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,
);

#panelization:
#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes. 
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load


#############################################################################################
#junk/experiment:

#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html

#my $caller = "pdf2gerb::";

#sub cfg
#{
#    my $proto = shift;
#    my $class = ref($proto) || $proto;
#    my $settings =
#    {
#        $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless($settings, $class);
#    return $settings;
#}

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#print STDERR "read cfg file\n";

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }

Download Details:

Author: swannman
Source Code: https://github.com/swannman/pdf2gerb

License: GPL-3.0 license

#perl

Sam Son

1567822183

How to Build Virtual Assistant with Python

In this lab we are going to build demo TARS from Interstellar movie with Python. TARS can help you to automate your tasks such as search videos in YouTube and play them, send emails, open websites, search materials in Wikipedia and read them,inform weather forecast in your country, greetings and more. By building TARS you will increase your Python knowledge and learn many useful libraries/tools. I will push source code to my git repository so feel free to contribute and improve functionality of TARS

Let’s start by creating virtual environment and building the base audio system of TARS.

mkdir TARS
cd TARS
virtualenv venv

To activate the venv run command below

. venv/bin/activate

What is virtual environment?

Once you activated venv, we need to install main libraries by following commands:

pip3 install gTTS
pip3 install SpeechRecognition
pip3 install PyAudio
pip3 install pygame

gTTS (Google Text-to-Speech) is a Python library and CLI tool to interface with Google Translate’s text-to-speech API. This module helps to convert String text to Spoken text and can be saved as .mp3

Speech Recognition is an important feature in several applications used such as home automation, artificial intelligence, etc. Recognizing speech needs audio input, and SpeechRecognition makes it really simple to retrieve this input. Instead of building scripts from scratch to access microphones and process audio files, SpeechRecognition will have you up and running in just a few minutes.

To access your microphone with SpeechRecognizer, you’ll have to install the PyAudio package

Pygame is a cross-platform set of Python modules designed for writing video games. It includes computer graphics and sound libraries designed to be used with the Python programming language.

Now, let’s build voice system of TARS:

from gtts import gTTS
import speech_recognition as sr
from pygame import mixer

def talk(audio):
    print(audio)
    for line in audio.splitlines():
        text_to_speech = gTTS(text=audio, lang='en-uk')
        text_to_speech.save('audio.mp3')
        mixer.init()
        mixer.music.load("audio.mp3")
        mixer.music.play()

As you see we are passing audio as an argument to let the TARS speak. For instance, talk(‘Hey I am TARS! How can I help you?’) program will loop these lines with the help of splitlines() method. This method is used to split the lines at line boundaries. Check splitlines() for more. Then, gTTS will handle to convert all these texts to speech. text parameter defines text to be read and lang defines the language (IETF language tag) to read the text in. Once loop finished, save() method writes result to file.

pygame.mixer is a module for loading and playing sounds and must be initialized before using it.

Alright! Now, let’s create a function that will listen for commands.

def myCommand():
    #Initialize the recognizer 
    r = sr.Recognizer()

    with sr.Microphone() as source:
        print('TARS is Ready...')
        r.pause_threshold = 1
        #wait for a second to let the recognizer adjust the  
        #energy threshold based on the surrounding noise level 
        r.adjust_for_ambient_noise(source, duration=1)
        #listens for the user's input
        audio = r.listen(source)

    try:
        command = r.recognize_google(audio).lower()
        print('You said: ' + command + '\n')

    #loop back to continue to listen for commands if unrecognizable speech is received
    except sr.UnknownValueError:
        print('Your last command couldn\'t be heard')
        command = myCommand();

    return command

In this function we are using SpeechRecognition library. It acts as a wrapper for several popular speech APIs and is thus extremely flexible. One of these—the Google Web Speech API—supports a default API key that is hard-coded into the SpeechRecognition library. That means you can get off your feet without having to sign up for a service.

To be able to work with your own voice with speech recognition, you need the PyAudio package. Like Recognizer for audio files, we will need Microphone for real-time speech data.

You can capture input from the microphone using the listen() method of the Recognizer class inside of the with block. This method takes an audio source as its first argument and records input from the source until silence is detected.

Try to say your commands in silence place( with less background noise ) otherwise TARS can confuse.

Take a look The Ultimate Guide To Speech Recognition With Pyth on

import random

def tars(command):
    errors=[
        "I don\'t know what you mean!",
        "Excuse me?",
        "Can you repeat it please?",
    ]

    if 'Hello' in command:
        talk('Hello! I am TARS. How can I help you?')

    else:
        error = random.choice(errors)
        talk(error)


talk('TARS is ready!')


while True:
    assistant(myCommand())

Once you run the program TARS will start talk with you by saying ‘TARS is ready!’ and continue to listen your commands until you stop the program. Start by saying ‘Hello’ :)

When TARS didn’t get the command we will handle the error by random sentences.

Here is the full code of main structure:

from gtts import gTTS
import speech_recognition as sr
from pygame import mixer
import random
def talk(audio):
    print(audio)
    for line in audio.splitlines():
        text_to_speech = gTTS(text=audio, lang='en-uk')
        text_to_speech.save('audio.mp3')
        mixer.init()
        mixer.music.load("audio.mp3")
        mixer.music.play()

def myCommand():
    #Initialize the recognizer
    #The primary purpose of a Recognizer instance is, of course, to recognize speech. 
    r = sr.Recognizer()

    with sr.Microphone() as source:
        print('TARS is Ready...')
        r.pause_threshold = 2
        #wait for a second to let the recognizer adjust the  
        #energy threshold based on the surrounding noise level 
        r.adjust_for_ambient_noise(source, duration=1)
        #listens for the user's input
        audio = r.listen(source)

    try:
        command = r.recognize_google(audio).lower()
        print('You said: ' + command + '\n')

    #loop back to continue to listen for commands if unrecognizable speech is received
    except sr.UnknownValueError:
        print('Your last command couldn\'t be heard')
        command = myCommand();
    return command

def tars(command):
    errors=[
        "I don't know what you mean",
        "Did you mean astronaut?",
        "Can you repeat it please?",
    ]
    if 'hello' in command:
        talk('Hello! I am TARS. How can I help you?')
    else:
        error = random.choice(errors)
        talk(error)


talk('TARS is ready!')

#loop to continue executing multiple commands
while True:
    tars(myCommand())

Well… Is AI anything more than a bunch of IF statements?

If you are talking about “real” AI , then yes it’s a lot more than just If statements.The development of AI has historically been split into two fields; symbolic AI, and machine learning.

Symbolic AI is the field in which artificially intelligent systems were designed with if-else type logic. Programmers would attempt to define every possible scenario for the system to deal with. Until the late seventies this was the dominant form of AI system development. Experts in the field argued very strongly that machine-learning would never catch on and that AI could only be written in this way.

Now we know that accounting for every possible scenario in an intelligent system is enormously impractical and we use machine-learning instead. Machine learning uses statistics to look for and define patterns in data so that a machine can learn about and improve the tasks that it is designed to perform. This is significantly more flexible.

We are using just bunch of IF statements to understand basics of AI. But we will implement some cool ML algorithms later.

I hope you learned new things so far, now, it is time to teach TARS how to automate stuff.

Open Google and search for something

We are going to import webbrowser module in Python which provides an interface to display Web-based documents.

While we are saying commands, TARS have to detect availability of these commands by matching them. Python has a built-in package called re, which can be used to work with Regular Expressions.

import re
import webbrowser

if 'open google' in command:
        #matching command to check it is available
        reg_ex = re.search('open google (.*)', command)
        url = 'https://www.google.com/'
        if reg_ex:
            subgoogle = reg_ex.group(1)
            url = url + 'r/' + subreddit
        webbrowser.open(url)
        print('Done!')

The re.search() method takes a regular expression pattern and a string and searches for that pattern within the string. If the search is successful, search() returns a match object or None otherwise. Therefore, the search is usually immediately followed by an if-statement to test if the search succeeded

The code reg_ex = re.search('open google (.)', command)* stores the search result in a variable named “reg_ex”. Then the if-statement tests the match – if true the search succeeded and group() is the matching text. Otherwise if the match is false (None to be more specific), then the search did not succeed, and there is no matching text. The 1 in reg_ex.group(1) represents the first parenthesized subgroup.

Even you can install Selenium to make search in Google by TARS. To install Selenium run the following command:

pip3 install selenium

Selenium WebDriver is a collection of open source APIs which are used to automate the testing of a web application. This tool is used to automate web application testing to verify that it works as expected. It supports many browsers such as Safari, Firefox, IE, and Chrome.

You can search how to use Selenium with Python there is a lot of sources on internet and it is really easy to learn. Let’s add this feature to TARS

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

    if 'open google and search' in command:
        reg_ex = re.search('open google and search (.*)', command)
        search_for = command.split("search",1)[1]
        url = 'https://www.google.com/'
        if reg_ex:
            subgoogle = reg_ex.group(1)
            url = url + 'r/' + subgoogle
        talk('Okay!')
        driver = webdriver.Firefox(executable_path='/path/to/geckodriver') #depends which web browser you are using
        driver.get('http://www.google.com')
        search = driver.find_element_by_name('q') # finds search
        search.send_keys(str(search_for)) #sends search keys 
        search.send_keys(Keys.RETURN) #hits enter

TARS will consider strings after “open google and search” command and takes all words as a search keys. I am using Firefox so I installed geckodriver but if you are using Chrome check the following StackOverflow question.

Send Email

We are going to import smtplib to send emails with Python. SMTP stands for Simple Mail Transfer Protocol and it is useful for communicating with mail servers to send mail.

  import smtplib

  elif 'email' or 'gmail' in command:
        talk('What is the subject?')
        time.sleep(3)
        subject = myCommand()
        talk('What should I say?')
        time.sleep(3)
        message = myCommand()
        content = 'Subject: {}\n\n{}'.format(subject, message)

        #init gmail SMTP
        mail = smtplib.SMTP('smtp.gmail.com', 587)

        #identify to server
        mail.ehlo()

        #encrypt session
        mail.starttls()

        #login
        mail.login('your_gmail', 'your_gmail_password')

        #send message
        mail.sendmail('FROM', 'TO', content)

        #end mail connection
        mail.close()

        talk('Email sent.')

Note that, in a nutshell, google is not allowing you to log in via smtplib because it has flagged this sort of login as “less secure”, so what you have to do is go to this link while you’re logged in to your google account, and allow the access.

Crawl Data

We are doing great so far! TARS can send mails and search whatever you want on google. Now, let’s implement more complex function to make TARS crawl some Wikipedia data and read it for us.

Beautiful Soup is a Python library for pulling data out of HTML and XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work. Run the following command in your terminal to install beautifulsoup:

pip install beautifulsoup4

We also will need requests library for making HTTP requests in Python. It abstracts the complexities of making requests behind a beautiful, simple API so that you can focus on interacting with services and consuming data in your application. Alright! Let’s see the code:

import bs4
import requests

elif 'wikipedia' in command:
        reg_ex = re.search('search in wikipedia (.+)', command)
        if reg_ex: 
            query = command.split()
            response = requests.get("https://en.wikipedia.org/wiki/" + query[3])

            if response is not None:
                html = bs4.BeautifulSoup(response.text, 'html.parser')
                title = html.select("#firstHeading")[0].text
                paragraphs = html.select("p")
                for para in paragraphs:
                    print (para.text)


                intro = '\n'.join([ para.text for para in paragraphs[0:5]])
                print (intro)
                mp3name = 'speech.mp3'
                language = 'en'
                myobj = gTTS(text=intro, lang=language, slow=False)   
                myobj.save(mp3name)
                mixer.init()
                mixer.music.load("speech.mp3")
                mixer.music.play()
    elif 'stop' in command:
        mixer.music.stop()

“search in wikipedia Mars” and TARS will take “Mars” as a keyword to search in Wikipedia. If you search something on Wikipedia you will see URL will look like https://en.wikipedia.org/wiki/Keyword so we are sending get request with keyword(what to search) to access data. Once request succeed, beautifulsoup will parse content inside Wikipedia. The join() method is a string method and returns a string in which the elements of sequence have been joined by str separator and we are using it to separate paragraphs. You already familiar with gTTS and mixer so I am passing that part.

TARS will display the crawled data on console and start to reading it for you.

Search videos on YouTube and play

This function is similar to search with google but this time it is better to use urllib. The main objective is to learn new things with Python, so I don’t want include Selenium in this function. Here is the code:

import urllib.request #used to make requests
import urllib.parse #used to parse values into the url

 elif 'youtube' in command:
        talk('Ok!')
        reg_ex = re.search('youtube (.+)', command)
        if reg_ex:
            domain = command.split("youtube",1)[1] 
            query_string = urllib.parse.urlencode({"search_query" : domain})
            html_content = urllib.request.urlopen("http://www.youtube.com/results?" + query_string) 
            search_results = re.findall(r'href=\"\/watch\?v=(.{11})', html_content.read().decode()) # finds all links in search result
            webbrowser.open("http://www.youtube.com/watch?v={}".format(search_results[0]))
            pass

The urllib module in Python 3 allows you access websites via your program. This opens up as many doors for your programs as the internet opens up for you. urllib in Python 3 is slightly different than urllib2 in Python 2, but they are mostly the same. Through urllib, you can access websites, download data, parse data, modify your headers, and do any GET and POST requests you might need to do.

Check this tutorial for more about urllib

Search key must be encoded before parsing into url. If you search something on YouTube you can see after [http://www.youtube.com/results?"](http://www.youtube.com/results?" “http://www.youtube.com/results?"”) there is a encoded search keys. Once these search keys encoded program can successfully access search results. The expression re.findall() returns all the non-overlapping matches of patterns in a string as a list of strings. Each video on youtube has its own 11 characters ID (https://www.youtube.com/watch?v=gEPmA3USJdI)and re.findall() will find all matches in decoded html_content(in search results page). decode() is used to convert from one encoding scheme, in which argument string is encoded to the desired encoding scheme. This works opposite to the encode. It accepts the encoding of the encoding string to decode it and returns the original string. Finally, it plays first video in search results because usually the first video is nearest one for search keys.

Full Code:

from gtts import gTTS
import speech_recognition as sr
import re
import time
import webbrowser
import random
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import smtplib
import requests
from pygame import mixer
import urllib.request
import urllib.parse
import bs4


def talk(audio):
    "speaks audio passed as argument"

    print(audio)
    for line in audio.splitlines():
        text_to_speech = gTTS(text=audio, lang='en-uk')
        text_to_speech.save('audio.mp3')
        mixer.init()
        mixer.music.load("audio.mp3")
        mixer.music.play()


def myCommand():
    "listens for commands"
    #Initialize the recognizer
    #The primary purpose of a Recognizer instance is, of course, to recognize speech. 
    r = sr.Recognizer()

    with sr.Microphone() as source:
        print('TARS is Ready...')
        r.pause_threshold = 1
        #wait for a second to let the recognizer adjust the  
        #energy threshold based on the surrounding noise level 
        r.adjust_for_ambient_noise(source, duration=1)
        #listens for the user's input
        audio = r.listen(source)
        print('analyzing...')

    try:
        command = r.recognize_google(audio).lower()
        print('You said: ' + command + '\n')
        time.sleep(2)

    #loop back to continue to listen for commands if unrecognizable speech is received
    except sr.UnknownValueError:
        print('Your last command couldn\'t be heard')
        command = myCommand();

    return command


def tars(command):
    errors=[
        "I don't know what you mean",
        "Excuse me?",
        "Can you repeat it please?",
    ]
    "if statements for executing commands"

    # Search on Google
    if 'open google and search' in command:
        reg_ex = re.search('open google and search (.*)', command)
        search_for = command.split("search",1)[1] 
        print(search_for)
        url = 'https://www.google.com/'
        if reg_ex:
            subgoogle = reg_ex.group(1)
            url = url + 'r/' + subgoogle
        talk('Okay!')
        driver = webdriver.Firefox(executable_path='/home/coderasha/Desktop/geckodriver')
        driver.get('http://www.google.com')
        search = driver.find_element_by_name('q')
        search.send_keys(str(search_for))
        search.send_keys(Keys.RETURN) # hit return after you enter search text

    #Send Email
    elif 'email' in command:
        talk('What is the subject?')
        time.sleep(3)
        subject = myCommand()
        talk('What should I say?')
        message = myCommand()
        content = 'Subject: {}\n\n{}'.format(subject, message)

        #init gmail SMTP
        mail = smtplib.SMTP('smtp.gmail.com', 587)

        #identify to server
        mail.ehlo()

        #encrypt session
        mail.starttls()

        #login
        mail.login('your_mail', 'your_mail_password')

        #send message
        mail.sendmail('FROM', 'TO', content)

        #end mail connection
        mail.close()

        talk('Email sent.')

    # search in wikipedia (e.g. Can you search in wikipedia apples)
    elif 'wikipedia' in command:
        reg_ex = re.search('wikipedia (.+)', command)
        if reg_ex: 
            query = command.split("wikipedia",1)[1] 
            response = requests.get("https://en.wikipedia.org/wiki/" + query)
            if response is not None:
                html = bs4.BeautifulSoup(response.text, 'html.parser')
                title = html.select("#firstHeading")[0].text
                paragraphs = html.select("p")
                for para in paragraphs:
                    print (para.text)
                intro = '\n'.join([ para.text for para in paragraphs[0:3]])
                print (intro)
                mp3name = 'speech.mp3'
                language = 'en'
                myobj = gTTS(text=intro, lang=language, slow=False)   
                myobj.save(mp3name)
                mixer.init()
                mixer.music.load("speech.mp3")
               while mixer.music.play()
    elif 'stop' in command:
        mixer.music.stop()

    # Search videos on Youtube and play (e.g. Search in youtube believer)
    elif 'youtube' in command:
        talk('Ok!')
        reg_ex = re.search('youtube (.+)', command)
        if reg_ex:
            domain = command.split("youtube",1)[1] 
            query_string = urllib.parse.urlencode({"search_query" : domain})
            html_content = urllib.request.urlopen("http://www.youtube.com/results?" + query_string)
            search_results = re.findall(r'href=\"\/watch\?v=(.{11})', html_content.read().decode())
            #print("http://www.youtube.com/watch?v=" + search_results[0])
            webbrowser.open("http://www.youtube.com/watch?v={}".format(search_results[0]))
            pass



    elif 'hello' in command:
        talk('Hello! I am TARS. How can I help you?')
        time.sleep(3)
    elif 'who are you' in command:
        talk('I am one of four former U.S. Marine Corps tactical robots')
        time.sleep(3)
    else:
        error = random.choice(errors)
        talk(error)
        time.sleep(3)


talk('TARS activated!')

#loop to continue executing multiple commands
while True:
    time.sleep(4)
    tars(myCommand())

Cool! We just created demo TARS and I hope you learned many things from this lab. Please feel free to contribute this project on GitHub, TARS will wait for improvements.

I hope this tutorial will surely help and you if you liked this tutorial, please consider sharing it with others.

#python #web-development

Shardul Bhatt

1626775355

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.

5 Reasons to Utilize Python for Programming Web Apps

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.

Simple to read and compose

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.

Utilized by the best

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.

Massive community support

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.

Progressive applications

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Darius Greenholt

1597751700

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python

Kiwanuka Timothy

1679971140

Using Singular Value Separation in Python and Numpy (linalg.svd)

In this pythonn - Numpy tutorial we will learn about Numpy linalg.svd: Singular Value Decomposition in Python. In mathematics, a singular value decomposition (SVD) of a matrix refers to the factorization of a matrix into three separate matrices. It is a more generalized version of an eigenvalue decomposition of matrices. It is further related to the polar decompositions.

In Python, it is easy to calculate the singular decomposition of a complex or a real matrix using the numerical python or the numpy library. The numpy library consists of various linear algebraic functions including one for calculating the singular value decomposition of a matrix.

In machine learning models, singular value decomposition is widely used to train models and in neural networks. It helps in improving accuracy and in reducing the noise in data. Singular value decomposition transforms one vector into another without them necessarily having the same dimension. Hence, it makes matrix manipulation in vector spaces easier and efficient. It is also used in regression analysis.

Syntax of Numpy linalg.svd() function

The function that calculates the singular value decomposition of a matrix in python belongs to the numpy module, named linalg.svd() .

The syntax of the numpy linalg.svd () is as follows:

numpy.linalg.svd(A, full_matrices=True, compute_uv=True, hermitian=False)

You can customize the true and false boolean values based on your requirements.

The parameters of the function are given below:

A->array_like: This is the required matrix whose singular value decomposition is being calculated. It can be real or complex as required. It’s dimension should be >= 2.
full_matrices->boolean value(optional): If set to true, then the Hermitian transpose of the given matrix is a square, if it’s false then it isn’t.
compute_uv->boolen value(optional): It determines whether the Hermitian transpose is to be calculated or not in addition to the singular value decomposition.
hermitian->boolean value(optional): The given matrix is considered hermitian(that is symmetric, with real values) which might provide a more efficient method for computation.

The function returns three types of matrices based on the parameters mentioned above:

S->array_like: The vector containing the singular values in the descending order with dimensions same as the original matrix.
u->array_like: This is an optional solution that is returned when compute_uv is set to True. It is a set of vectors with singular values.
v-> array_like: Set of unitary arrays only returned when compute_uv is set to True.

It raises a LinALgError when the singular values diverse.

Prerequisites for setup

Before we dive into the examples, make sure you have the numpy module installed in your local system. This is required for using linear algebraic functions like the one discussed in this article. Run the following command in your terminal.

pip install numpy

That’s all you need right now, let’s look at how we will implement the code in the next section.

To calculate Singular Value Decomposition (SVD) in Python, use the NumPy library’s linalg.svd() function. Its syntax is numpy.linalg.svd(A, full_matrices=True, compute_uv=True, hermitian=False), where A is the matrix for which SVD is being calculated. It returns three matrices: S, U, and V.

Example 1: Calculating the Singular Value Decomposition of a 3×3 Matrix

In this first example we will take a 3X3 matrix and compute its singular value decomposition in the following way:

#importing the numpy module
import numpy as np
#using the numpy.array() function to create an array
A=np.array([[2,4,6],
       [8,10,12],
       [14,16,18]])
#calculatin all three matrices for the output
#using the numpy linalg.svd function
u,s,v=np.linalg.svd(A, compute_uv=True)
#displaying the result
print("the output is=")
print('s(the singular value) = ',s)
print('u = ',u)
print('v = ',v)

The output will be:

the output is=
s(the singular value) =  [3.36962067e+01 2.13673903e+00 8.83684950e-16]
u =  [[-0.21483724  0.88723069  0.40824829]
 [-0.52058739  0.24964395 -0.81649658]
 [-0.82633754 -0.38794278  0.40824829]]
v =  [[-0.47967118 -0.57236779 -0.66506441]
 [-0.77669099 -0.07568647  0.62531805]
 [-0.40824829  0.81649658 -0.40824829]]

Example 1

Example 2: Calculating the Singular Value Decomposition of a Random Matrix

In this example, we will be using the numpy.random.randint() function to create a random matrix. Let’s get into it!

#importing the numpy module
import numpy as np
#using the numpy.array() function to craete an array
A=np.random.randint(5, 200, size=(3,3))
#display the created matrix
print("The input matrix is=",A)
#calculatin all three matrices for the output
#using the numpy linalg.svd function
u,s,v=np.linalg.svd(A, compute_uv=True)
#displaying the result
print("the output is=")
print('s(the singular value) = ',s)
print('u = ',u)
print('v = ',v)

The output will be as follows:

The input matrix is= [[ 36  74 101]
 [104 129 185]
 [139 121 112]]
the output is=
s(the singular value) =  [348.32979681  61.03199722  10.12165841]
u =  [[-0.3635535  -0.48363012 -0.79619769]
 [-0.70916514 -0.41054007  0.57318554]
 [-0.60408084  0.77301925 -0.19372034]]
v =  [[-0.49036384 -0.54970618 -0.67628871]
 [ 0.77570499  0.0784348  -0.62620264]
 [ 0.39727203 -0.83166766  0.38794824]]

Example 2

Wrapping Up

In this article, we explored the concept of singular value decomposition in mathematics and how to calculate it using Python’s numpy module. We used the linalg.svd() function to compute the singular value decomposition of both given and random matrices. Numpy provides an efficient and easy-to-use method for performing linear algebra operations, making it highly valuable in machine learning, neural networks, and regression analysis. Keep exploring other linear algebraic functions in numpy to enhance your mathematical toolset in Python.

Article source at: https://www.askpython.com

#python #numpy