Build an API under 30 lines of code with Python and Flask




Hello everyone. Now a days developers need to perform many jobs. Like web development, database development, API development and so on. Some companies are just having jobs called API developer on their openings sheet.What role APIs are playing now and why one should learn building them is our topic today. Developing an API with Python is a very easy task when compared to other languages. So, sit back and grab this skill for you. Take my words ,this skill is hot right now in the market.

For my other works on software development, visit my publication:

What is a REST API?

REST (REpresentational State Transfer) is an architectural style, and an approach to communications that is often used in the development of Web services. The use of REST is often preferred over the more heavyweight SOAP (Simple Object Access Protocol) style because REST does not leverage as much…

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Augmented reality with Python and OpenCV (part 1)


Oh, these amazing wizards…

Bites of code

You may (or may not) have heard of or seen the augmented reality Invizimals video game or the Topps 3D baseball cards. The main idea is to render in the screen of a tablet, PC or smartphone a 3D model of a specific figure on top of a card according to the position and orientation of the card. 

IMG_1139Figure 1: Invizimal augmented reality cards. Source: 

Well, this past semester I took a course in Computer Vision where we studied some aspects of projective geometry and thought it would be an entertaining project to develop my own implementation of a card based augmented reality application. I warn you that we will need a bit of algebra to make it work but I’ll try to keep it as light as possible. To make the most out of it you should be comfortable working with different coordinate systems and transformation matrices.


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TensorFlow for Poets


With this kind of stuff you can understand why Siraj calls them wizards…

Pete Warden's blog

When I first started investigating the world of deep learning, I found it very hard to get started. There wasn’t much documentation, and what existed was aimed at academic researchers who already knew a lot of the jargon and background. Thankfully that has changed over the last few years, with a lot more guides and tutorials appearing.

I always loved EC2 for Poets though, and I haven’t seen anything for deep learning that’s aimed at as wide an audience. EC2 for Poets is an explanation of cloud computing that removes a lot of the unnecessary mystery by walking anyone with basic computing knowledge step-by-step through building a simple application on the platform. In the same spirit, I want to show how anyone with a Mac laptop and the ability to use the Terminal can create their own image classifier using TensorFlow, without having to do any coding.

I feel…

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DisAtBot – How I Built a Chatbot With Telegram And Python

Originally guest posted by Rodolfo Ferro on Sun 10 December 2017 in Tools @ PyBites.

Rodolfo recently joined the Code Challenges and built Disaster Attention Bot (DisAtBot), a chatbot that helps people affected by natural disasters. In this article he shows how he built this bot with Telegram and (of course) Python. Show him some love because who knows, this could be a life saver (pun intended)! We are delighted to have him show this interesting project he submitted for Code Challenge 43 which earned him a book on chatbots. /Rod please share …

“¿Quién convocó a tanto muchacho, de dónde salió tanto voluntario, cómo fue que la sangre sobró en los hospitales, quién organizó las brigadas que dirigieron el tránsito de vehículos y de peatones por toda la zona afectada? No hubo ninguna convocatoria, no se hizo ningún llamado y todos acudieron”

“El jueves negro que cambió a México”
– Emilio Viale, 1985.

A bit of context…

Since September 19th, 2017 Mexico has been hit by several earthquakes (The Guardian, CNN). This made me wonder how we could better handle the reporting of damaged zones, people buried under the rubble of buildings, injured people in need of medical attention and other situations.

Verificado 19s was an immediate solution to follow up reports from social media and visualize the info on an online map. This required a lot of real-time (24/7) monitoring of posts on social media from people that were located in the effected areas. And that data was updated every ~10 minutes.

So I started thinking about a way to optimize this process for future situations, not only for earthquakes, but for disaster situations in general. This incentivized me to work on this bot for Pybites Code Challenge 43 – Build a Chatbot Using Python.

So DisAtBot was born

DisAtBot automates the process of reporting incidents via messaging platforms, such as Telegram, Facebook Messenger, Twitter, etc. At this time it only supports Telegram, but I hope to expand it to other social media. If you’d like to contribute, see the Contribute section at the end.

You can find DisAtBot at:

The idea was to have a simple flow that allowed disaster reporting to be quick and easy. The general process of DisAtBot is as follows:

disatbot flow

The idea is that any user can interact with the bot by selecting options from button menus in the conversation. This greatly speeds up incidents reporting.

The next step would be opening a ticket which will be stored in a database, for the corresponding government instance/public organization/NGO/etc. to validate and send assistance. When no more help is needed, or the situation is under control, the ticket is closed.


First clone the repo. I used Python 3.6 and the following packages:

To install all dependencies create a virtual env and run:

pip install -r requirements.txt

Then cd into the scripts folder and run the bot as follows:



The focus of the initial version was the creation of menu buttons for an easy interaction with the user. The second –and main– issue addressed was the conversation handler. A finite state machine was needed to preserve the desired flow and the responses for each state.

I won’t go too deep into the explanation, but the code below will show how I tackled this.

First of all, Telegram’s library has several methods to create button menus for user responses during the conversation flow. The idea is to create a Keyboard Markup to handle responses through buttons. This can either be Inline (buttons will appear in the conversation window) or as a Reply Keyboard (buttons will be displayed under the textbox to write messages).

An example can be seen in the menu function:

def menu(bot, update):
    Main menu function.
    This will display the options from the main menu.
    # Create buttons to select language:
    keyboard = [[send_report[LANG], view_map[LANG]],
                [view_faq[LANG], view_about[LANG]]]

    reply_markup = ReplyKeyboardMarkup(keyboard,

    user = update.message.from_user"Menu command requested by {}.".format(user.first_name))
    update.message.reply_text(main_menu[LANG], reply_markup=reply_markup)

    return SET_STAT

As you can see, the keyboard variable is a list that contains the four buttons to be displayed. The layout can be set by nesting lists inside. In this case the Report and Map buttons are in the first row, while FAQ and About buttons are in the second row. This looks like:

disatbot menu

Continuing with the code, a ReplyMarkup is needed to handle the button responses. It specifies the layout of the menu: if only one menu is displayed, if it needs to be resized, etc.

A logger is used for the bot, and the update.message.reply(...) function is used to update the displayed text according to the response from the user. The SET_STAT variable returned in this function is a (predefined) integer to return the state at that time, and to follow the flow.

We now understand the menu creation and handling. The reason of using buttons is that we want a quick interaction because the bot is used in an emergency situation.

The conversation handler – Telegram’s ConversationHandler – takes care of setting the state or step of the flow we’re currently at, the finite state machine I mentioned earlier. Note that each state also needs to handle its respective information (button responses, etc.)

This code shows the conversation handler:

def main():
    Main function.
    This function handles the conversation flow by setting
    states on each step of the flow. Each state has its own
    handler for the interaction with the user.
    global LANG
    # Create the EventHandler and pass it your bot's token.
    updater = Updater(telegram_token)

    # Get the dispatcher to register handlers:
    dp = updater.dispatcher

    # Add conversation handler with predefined states:
    conv_handler = ConversationHandler(
        entry_points=[CommandHandler('start', start)],

            SET_LANG: [RegexHandler('^(ES|EN)$', set_lang)],

            MENU: [CommandHandler('menu', menu)],

            SET_STAT: [RegexHandler(
                            send_report['ES'], view_map['ES'],
                            view_faq['ES'], view_about['ES']),
                            send_report['EN'], view_map['EN'],
                            view_faq['EN'], view_about['EN']),

            LOCATION: [MessageHandler(Filters.location, location),
                       CommandHandler('menu', menu)]

        fallbacks=[CommandHandler('cancel', cancel),
                   CommandHandler('help', help)]


    # Log all errors:

    # Start DisAtBot:

    # Run the bot until the user presses Ctrl-C or the process
    # receives SIGINT, SIGTERM or SIGABRT:

It might seem a bit confusing at first, but it boils down to:
– The conversation handler has the states of the flow.
– It also has entry points (such as the start function), and fallbacks (such as the cancel and help functions).
– It also contains some error handlers.
– A global LANG variable is used, since the implementation – I forgot to mention – support interacting in English or Spanish! To support this I created dictionaries for each interaction in both languages.

If you want to check the full code of this bot, check out the scripts directory where you’ll find the main script and the language dictionaries.

Some other features implemented are geolocation handling and About / FAQ sections. But the best way to know about this project is by watching it in action (for a live demo go to 8.30):

Future work

For future development I am thinking about adding a map. The system already creates a GeoJSON file from the locations acquired.

As mentioned I am considering expanding this to other platforms like Facebook Messenger and Twitter. Another good thing to add would be a website explaining the main use cases of the bot, maybe a wiki –kinda– site?

If you have any other ideas or suggestions feel free to contact me or:


If you’re interested in contributing to this project, feel free to take a look at the repo’s CONTRIBUTING file. I’d be very pleased if this project would grow out to something used in real life to alleviate the dramatic consequences of natural disaster, which always seem to hit when least expected.

Keep Calm and Code in Python!
– Rodolfo


Image Analysis – Finger detection


An interesting approach!


I wanted to create a program which can detect the fingers on a photo. So I made one. This was my homework for my Industrial image processing class in University.

This was the rock,paper,scissors problem because my Matlab app can find the fingers on the image. So it’s just a playful name for an interesting problem.

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