Tag Archives: python

Developing bots for Hangouts Chat

Posted by Wesley Chun (@wescpy), Developer Advocate, G Suite

We recently introduced Hangouts Chat to general availability. This next-generation messaging platform gives G Suite users a new place to communicate and to collaborate in teams. It features archive & search, tighter G Suite integration, and the ability to create separate, threaded chat rooms. The key new feature for developers is a bot framework and API. Whether it's to automate common tasks, query for information, or perform other heavy-lifting, bots can really transform the way we work.

In addition to plain text replies, Hangouts Chat can also display bot responses with richer user interfaces (UIs) called cards which can render header information, structured data, images, links, buttons, etc. Furthermore, users can interact with these components, potentially updating the displayed information. In this latest episode of the G Suite Dev Show, developers learn how to create a bot that features an updating interactive card.

As you can see in the video, the most important thing when bots receive a message is to determine the event type and take the appropriate action. For example, a bot will perform any desired "paperwork" when it is added to or removed from a room or direct message (DM), generically referred to as a "space" in the vernacular.

Receiving an ordinary message sent by users is the most likely scenario; most bots do "their thing" here in serving the request. The last event type occurs when a user clicks on an interactive card. Similar to receiving a standard message, a bot performs its requisite work, including possibly updating the card itself. Below is some pseudocode summarizing these four event types and represents what a bot would likely do depending on the event type:

function processEvent(req, rsp) {
var event = req.body; // event type received
var message; // JSON response message

if (event.type == 'REMOVED_FROM_SPACE') {
// no response as bot removed from room
return;

} else if (event.type == 'ADDED_TO_SPACE') {
// bot added to room; send welcome message
message = {text: 'Thanks for adding me!'};

} else if (event.type == 'MESSAGE') {
// message received during normal operation
message = responseForMsg(event.message.text);

} else if (event.type == 'CARD_CLICKED') {
// user-click on card UI
var action = event.action;
message = responseForClick(
action.actionMethodName, action.parameters);
}

rsp.send(message);
};

The bot pseudocode as well as the bot featured in the video respond synchronously. Bots performing more time-consuming operations or those issuing out-of-band notifications, can send messages to spaces in an asynchronous way. This includes messages such as job-completed notifications, alerts if a server goes down, and pings to the Sales team when a new lead is added to the CRM (Customer Relationship Management) system.

Hangouts Chat supports more than JavaScript or Python and Google Apps Script or Google App Engine. While using JavaScript running on Apps Script is one of the quickest and simplest ways to get a bot online within your organization, it can easily be ported to Node.js for a wider variety of hosting options. Similarly, App Engine allows for more scalability and supports additional languages (Java, PHP, Go, and more) beyond Python. The bot can also be ported to Flask for more hosting options. One key takeaway is the flexibility of the platform: developers can use any language, any stack, or any cloud to create and host their bot implementations. Bots only need to be able to accept HTTP POST requests coming from the Hangouts Chat service to function.

At Google I/O 2018 last week, the Hangouts Chat team leads and I delivered a longer, higher-level overview of the bot framework. This comprehensive tour of the framework includes numerous live demos of sample bots as well as in a variety of languages and platforms. Check out our ~40-minute session below.

To help you get started, check out the bot framework launch post. Also take a look at this post for a deeper dive into the Python App Engine version of the vote bot featured in the video. To learn more about developing bots for Hangouts Chat, review the concepts guides as well as the "how to" for creating bots. You can build bots for your organization, your customers, or for the world. We look forward to all the exciting bots you're going to build!

Tangent: Source-to-Source Debuggable Derivatives

Crossposted on the Google Research Blog

Tangent is a new, free, and open source Python library for automatic differentiation. In contrast to existing machine learning libraries, Tangent is a source-to-source system, consuming a Python function f and emitting a new Python function that computes the gradient of f. This allows much better user visibility into gradient computations, as well as easy user-level editing and debugging of gradients. Tangent comes with many more features for debugging and designing machine learning models.
This post gives an overview of the Tangent API. It covers how to use Tangent to generate gradient code in Python that is easy to interpret, debug and modify.

Neural networks (NNs) have led to great advances in machine learning models for images, video, audio, and text. The fundamental abstraction that lets us train NNs to perform well at these tasks is a 30-year-old idea called reverse-mode automatic differentiation (also known as backpropagation), which comprises two passes through the NN. First, we run a “forward pass” to calculate the output value of each node. Then we run a “backward pass” to calculate a series of derivatives to determine how to update the weights to increase the model’s accuracy.

Training NNs, and doing research on novel architectures, requires us to compute these derivatives correctly, efficiently, and easily. We also need to be able to debug these derivatives when our model isn’t training well, or when we’re trying to build something new that we do not yet understand. Automatic differentiation, or just “autodiff,” is a technique to calculate the derivatives of computer programs that denote some mathematical function, and nearly every machine learning library implements it.

Existing libraries implement automatic differentiation by tracing a program’s execution (at runtime, like TF Eager, PyTorch and Autograd) or by building a dynamic data-flow graph and then differentiating the graph (ahead-of-time, like TensorFlow). In contrast, Tangent performs ahead-of-time autodiff on the Python source code itself, and produces Python source code as its output.
As a result, you can finally read your automatic derivative code just like the rest of your program. Tangent is useful to researchers and students who not only want to write their models in Python, but also read and debug automatically-generated derivative code without sacrificing speed and flexibility.

You can easily inspect and debug your models written in Tangent, without special tools or indirection. Tangent works on a large and growing subset of Python, provides extra autodiff features other Python ML libraries don’t have, is high-performance, and is compatible with TensorFlow and NumPy.

Automatic differentiation of Python code

How do we automatically generate derivatives of plain Python code? Math functions like tf.exp or tf.log have derivatives, which we can compose to build the backward pass. Similarly, pieces of syntax, such as  subroutines, conditionals, and loops, also have backward-pass versions. Tangent contains recipes for generating derivative code for each piece of Python syntax, along with many NumPy and TensorFlow function calls.

Tangent has a one-function API:
import tangent
df = tangent.grad(f)
Here’s an animated graphic of what happens when we call tangent.grad on a Python function:
If you want to print out your derivatives, you can run
import tangent
df = tangent.grad(f, verbose=1)
Under the hood, tangent.grad first grabs the source code of the Python function you pass it. Tangent has a large library of recipes for the derivatives of Python syntax, as well as TensorFlow Eager functions. The function tangent.grad then walks your code in reverse order, looks up the matching backward-pass recipe, and adds it to the end of the derivative function. This reverse-order processing gives the technique its name: reverse-mode automatic differentiation.

The function df above only works for scalar (non-array) inputs. Tangent also supports
Although we started with TensorFlow Eager support, Tangent isn’t tied to one numeric library or another—we would gladly welcome pull requests adding PyTorch or MXNet derivative recipes.

Next Steps

Tangent is open source now at github.com/google/tangent. Go check it out for download and installation instructions. Tangent is still an experiment, so expect some bugs. If you report them to us on GitHub, we will do our best to fix them quickly.

We are working to add support in Tangent for more aspects of the Python language (e.g., closures, inline function definitions, classes, more NumPy and TensorFlow functions). We also hope to add more advanced automatic differentiation and compiler functionality in the future, such as automatic trade-off between memory and compute (Griewank and Walther 2000; Gruslys et al., 2016), more aggressive optimizations, and lambda lifting.

We intend to develop Tangent together as a community. We welcome pull requests with fixes and features. Happy deriving!

By Alex Wiltschko, Research Scientist, Google Brain Team

Acknowledgments

Bart van Merriënboer contributed immensely to all aspects of Tangent during his internship, and Dan Moldovan led TF Eager integration, infrastructure and benchmarking. Also, thanks to the Google Brain team for their support of this post and special thanks to Sanders Kleinfeld and Aleks Haecky for their valuable contribution for the technical aspects of the post.

Introducing Python Fire, a library for automatically generating command line interfaces

By David Bieber, Software Engineer on Google Brain

Originally posted on the Google Open Source Blog


Today we are pleased to announce the open-sourcing of Python Fire. Python Fire generates command line interfaces (CLIs) from any Python code. Simply call the Fire function in any Python program to automatically turn that program into a CLI. The library is available from pypi via `pip install fire`, and the source is available on GitHub.


Python Fire will automatically turn your code into a CLI without you needing to do any additional work. You don't have to define arguments, set up help information, or write a main function that defines how your code is run. Instead, you simply call the `Fire` function from your main module, and Python Fire takes care of the rest. It uses inspection to turn whatever Python object you give it -- whether it's a class, an object, a dictionary, a function, or even a whole module -- into a command line interface, complete with tab completion and documentation, and the CLI will stay up-to-date even as the code changes.


To illustrate this, let's look at a simple example.
#!/usr/bin/env python
import fire


class Example(object):
 def hello(self, name='world'):
   """Says hello to the specified name."""
   return 'Hello {name}!'.format(name=name)


def main():
 fire.Fire(Example)


if __name__ == '__main__':
 main()


When the Fire function is run, our command will be executed. Just by calling Fire, we can now use the Example class as if it were a command line utility.


$ ./example.py hello
Hello world!
$ ./example.py hello David
Hello David!
$ ./example.py hello --name=Google
Hello Google!


Of course, you can continue to use this module like an ordinary Python library, enabling you to use the exact same code both from Bash and Python. If you're writing a Python library, then you no longer need to update your main method or client when experimenting with it; instead you can simply run the piece of your library that you're experimenting with from the command line. Even as the library changes, the command line tool stays up to date.


At Google, engineers use Python Fire to generate command line tools from Python libraries. We have an image manipulation tool built by using Fire with the Python Imaging Library, PIL. In Google Brain, we use an experiment management tool built with Fire, allowing us to manage experiments equally well from Python or from Bash.


Every Fire CLI comes with an interactive mode. Run the CLI with the `--interactive` flag to launch an IPython REPL with the result of your command, as well as other useful variables already defined and ready to use. Be sure to check out Python Fire's documentation for more on this and the other useful features Fire provides.


Between Python Fire's simplicity, generality, and power, we hope you find it a useful library for your own projects.

Introducing Python Fire, a library for automatically generating command line interfaces

Today we are pleased to announce the open-sourcing of Python Fire. Python Fire generates command line interfaces (CLIs) from any Python code. Simply call the Fire function in any Python program to automatically turn that program into a CLI. The library is available from pypi via `pip install fire`, and the source is available on GitHub.

Python Fire will automatically turn your code into a CLI without you needing to do any additional work. You don't have to define arguments, set up help information, or write a main function that defines how your code is run. Instead, you simply call the `Fire` function from your main module, and Python Fire takes care of the rest. It uses inspection to turn whatever Python object you give it -- whether it's a class, an object, a dictionary, a function, or even a whole module -- into a command line interface, complete with tab completion and documentation, and the CLI will stay up-to-date even as the code changes.

To illustrate this, let's look at a simple example.

#!/usr/bin/env python
import fire

class Example(object):
def hello(self, name='world'):
"""Says hello to the specified name."""
return 'Hello {name}!'.format(name=name)

def main():
fire.Fire(Example)

if __name__ == '__main__':
main()

When the Fire function is run, our command will be executed. Just by calling Fire, we can now use the Example class as if it were a command line utility.

$ ./example.py hello
Hello world!
$ ./example.py hello David
Hello David!
$ ./example.py hello --name=Google
Hello Google!

Of course, you can continue to use this module like an ordinary Python library, enabling you to use the exact same code both from Bash and Python. If you're writing a Python library, then you no longer need to update your main method or client when experimenting with it; instead you can simply run the piece of your library that you're experimenting with from the command line. Even as the library changes, the command line tool stays up to date.

At Google, engineers use Python Fire to generate command line tools from Python libraries. We have an image manipulation tool built by using Fire with the Python Imaging Library, PIL. In Google Brain, we use an experiment management tool built with Fire, allowing us to manage experiments equally well from Python or from Bash.

Every Fire CLI comes with an interactive mode. Run the CLI with the `--interactive` flag to launch an IPython REPL with the result of your command, as well as other useful variables already defined and ready to use. Be sure to check out Python Fire's documentation for more on this and the other useful features Fire provides.

Between Python Fire's simplicity, generality, and power, we hope you find it a useful library for your own projects.

By David Bieber, Software Engineer on Google Brain

Grumpy: Go running Python!

Google runs millions of lines of Python code. The front-end server that drives youtube.com and YouTube’s APIs is primarily written in Python, and it serves millions of requests per second! YouTube’s front-end runs on CPython 2.7, so we’ve put a ton of work into improving the runtime and adapting our application to work optimally within it. These efforts have borne a lot of fruit over the years, but we always run up against the same issue: it's very difficult to make concurrent workloads perform well on CPython.

To solve this problem, we investigated a number of other Python runtimes. Each had trade-offs and none solved the concurrency problem without introducing other issues.
MeatGrinder.png
So we asked ourselves a crazy question: What if we were to implement an alternative runtime optimized for real-time serving? Once we started going down the rabbit hole, Go seemed like an obvious choice of platform since its operational characteristics align well with our use case (e.g. lightweight threads). We wanted first class language interoperability and Go’s powerful runtime type reflection system made this straightforward. Python in Go felt very natural, and so Grumpy was born.

Grumpy is an experimental Python runtime for Go. It translates Python code into Go programs, and those transpiled programs run seamlessly within the Go runtime. We needed to support a large existing Python codebase, so it was important to have a high degree of compatibility with CPython (quirks and all). The goal is for Grumpy to be a drop-in replacement runtime for any pure-Python project.

Two design choices we made had big consequences. First, we decided to forgo support for C extension modules. This means that Grumpy cannot leverage the wealth of existing Python C extensions but it gave us a lot of flexibility to design an API and object representation that scales for parallel workloads. In particular, Grumpy has no global interpreter lock, and it leverages Go’s garbage collection for object lifetime management instead of counting references. We think Grumpy has the potential to scale more gracefully than CPython for many real world workloads. Results from Grumpy’s synthetic Fibonacci benchmark demonstrate some of this potential:



Second, Grumpy is not an interpreter. Grumpy programs are compiled and linked just like any other Go program. The downside is less development and deployment flexibility, but it offers several advantages. For one, it creates optimization opportunities at compile time via static program analysis. But the biggest advantage is that interoperability with Go code becomes very powerful and straightforward: Grumpy programs can import Go packages just like Python modules! For example, the Python snippet below uses Go’s standard net/http package to start a simple server:

from __go__.net.http import ListenAndServe, RedirectHandler

handler = RedirectHandler('http://github.com/google/grumpy', 303)
ListenAndServe('127.0.0.1:8080', handler)

We’re excited about the prospects for Grumpy. Although it’s still alpha software, most of the language constructs and many core built-in types work like you’d expect. There are still holes to fill — many built-in types are missing methods and attributes, built-in functions are absent and the standard library is virtually empty. If you find things that you wish were working, file an issue so we know what to prioritize. Or better yet, submit a pull request.

Stay Grumpy!

By Dylan Trotter, YouTube Engineering

Open source down under: Linux.conf.au 2017

It’s a new year and open source enthusiasts from around the globe are preparing to gather at the edge of the world for Linux.conf.au 2017. Among those preparing are Googlers, including some of us from the Open Source Programs Office.

This year Linux.conf.au is returning to Hobart, the riverside capital of Tasmania, home of Australia’s famous Tasmanian devils, running five days between January 16 and 20. The theme is the “Future of Open Source.”
Circle_DevilTuz.png
Tuz, a Tasmanian devil sporting a penguin beak, is the Linux.conf.au mascot.
(Artwork by Tania Walker licensed under CC BY-SA.)
The conference, which began in 1999 and is community organized, is well equipped to explore that theme which is reflected in the program schedule and miniconfs.

You’ll find Googlers speaking throughout the week, as well as participating in the hallway track. Don’t miss our Birds of a Feather session if you’re a student, educator, project maintainer, or otherwise interested in talking about outreach and student programs like Google Summer of Code and Google Code-in.

Monday, January 16th
12:20pm The Sound of Silencing by Julien Goodwin
4:35pm   Year of the Linux Desktop? by Jessica Frazelle

Tuesday, January 17th
All day    Community Leadership Summit X at LCA

Wednesday, January 18th
2:15pm   Community Building Beyond the Black Stump by Josh Simmons
4:35pm   Contributing to and Maintaining Large Scale Open Source Projects by Jessica Frazelle

Thursday, January 19th
4:35pm   Using Python for creating hardware to record FOSS conferences! by Tim Ansell

Friday, January 20th
1:20pm   Linux meets Kubernetes by Vishnu Kannan

Not able to make it to the conference? Keynotes and sessions will be livestreamed, and you can always find the session recordings online after the event.

We’ll see you there!

By Josh Simmons, Open Source Programs Office

Generating slides from spreadsheet data

Posted by Wesley Chun (@wescpy), Developer Advocate, G Suite

The G Suite team recently launched the very first Google Slides API, opening up a whole new set of possibilities, including leveraging data already sitting in a spreadsheet or database, and programmatically generating slide decks or slide content based on that data. Why is this a big deal? One of the key advantages of slide decks is that they can take database or spreadsheet data and make it more presentable for human consumption. This is useful when the need arises to communicate the information reflected by that data to management or potential customers.

Walking developers through a short application demonstrating both the Sheets and Slides APIs to make this happen is the topic of today's DevByte video. The sample app starts by reading all the necessary data from the spreadsheet using the Sheets API. The Slides API takes over from there, creating new slides for the data, then populating those slides with the Sheets data.

Developers interact with Slides by sending API requests. Similar to the Google Sheets API, these requests come in the form of JSON payloads. You create an array like in the JavaScript pseudocode below featuring requests to create a cell table on a slide and import a chart from a Sheet:


var requests = [
   {"createTable": {
       "elementProperties":
           {"pageObjectId": slideID},
       "rows": 8,
       "columns": 4
   }},
   {"createSheetsChart": {
       "spreadsheetId": sheetID,
       "chartId": chartID,
       "linkingMode": "LINKED",
       "elementProperties": {
           "pageObjectId": slideID,
           "size": {
               "height": { ... },
               "width": { ... }
           },
           "transform": { ... }
       }
   }}
];
If you've got at least one request, say in a variable named requests (as above), including the Sheet's sheetID and chartID plus the presentation page's slideID. You'd then pass it to the API with just one call to the presentations().batchUpdate() command, which in Python looks like the below if SLIDES is your API service endpoint:
SLIDES.presentations().batchUpdate(presentationId=slideID,
       body=requests).execute()

Creating tables is fairly straightforward. Creating charts has some magical features, one of those being the linkingMode. A value of "LINKED" means that if the Sheet data changes (altering the chart in the Sheet), the same chart in a slide presentation can be refreshed to match the latest image, either by the API or in the Slides user interface! You can also request a plain old static image that doesn't change with the data by selecting a value of "NOT_LINKED_IMAGE" for linkingMode. More on this can be found in the documentationon creating charts, and check out the video where you'll see both those API requests in action.

For a detailed look at the complete code sample featured in the video, check out the deep dive post. We look forward to seeing the interesting integrations you build with the power of both APIs!

Formatting cells with the Google Sheets API

Posted by Wesley Chun (@wescpy), Developer Advocate, G Suite
At Google I/O earlier this year, we launched a new Google Sheets API (click here to watch the entire announcement). The updated API includes many new features that weren't available in previous versions, including access to more functionality found in the Sheets desktop and mobile user interfaces. Formatting cells in Sheets is one example of something that wasn't possible with previous versions of the API and is the subject of today's DevByte video.
In our previous Sheets API video, we demonstrated how to get data into and out of a Google Sheet programmatically, walking through a simple script that reads rows out of a relational database and transferring the data to a new Google Sheet. The Sheet created using the code from that video is where we pick up today.

Formatting spreadsheets is accomplished by creating a set of request commands in the form of JSON payloads, and sending them to the API. Here is a sample JavaScript Object made up of an array of requests (only one this time) to bold the first row of the default Sheet automatically created for you (whose ID is 0):

{"requests": [
{"repeatCell": {
"range": {
"sheetId": 0,
"startRowIndex": 0,
"endRowIndex": 1
},
"cell": {
"userEnteredFormat": {
"textFormat": {
"bold": true
}
}
},
"fields": "userEnteredFormat.textFormat.bold"
}}
]}
With at least one request, say in a variable named requests and the ID of the sheet as SHEET_ID, you send them to the API via an HTTP POST to https://sheets.googleapis.com/v4/spreadsheets/{SHEET_ID}:batchUpdate, which in Python, would be a single call that looks like this:
SHEETS.spreadsheets().batchUpdate(spreadsheetId=SHEET_ID,
body=requests).execute()

For more details on the code in the video, check out the deepdive blog post. As you can probably guess, the key challenge is in constructing the JSON payload to send to API calls—the common operations samples can really help you with this. You can also check out our JavaScript codelab where we guide you through writing a Node.js app that manages customer orders for a toy company, featuring the toy orders data we looked at today but in a relational database. While the resulting equivalent Sheet is featured prominently in today's video, we will revisit it again in an upcoming episode showing you how to generate slides with spreadsheet data using the new Google Slides API, so stay tuned for that!

We hope all these resources help developers enhance their next app using G Suite APIs! Please subscribe to our channel and tell us what topics you would like to see in other episodes of the G Suite Dev Show!

Budou: Automatic Japanese line breaking tool

Today we are pleased to introduce Budou, an automatic line breaking tool for Japanese. What is a line breaking tool and why is it necessary? English uses spacing and hyphenation as cues to allow for beautiful, aka more legible, line breaks. Japanese, which has none of these, is notoriously more difficult. Breaks occur randomly, usually in the middle of a word.

This is a long standing issue in Japanese typography on the web, and results in degradation of readability. We can specify the place which line breaks can occur with CSS coding, but this is a non-trivial manual process which requires Japanese vocabulary and knowledge of grammar.


Budou automatically translates Japanese sentences into organized HTML code with meaningful chunks wrapped in non-breaking markup so as to semantically control line breaks. Budou uses Cloud Natural Language API to analyze the input sentence, and it concatenates proper words in order to produce meaningful chunks utilizing PoS (part-of-speech) tagging and syntactic information. Budou outputs HTML code by wrapping the chunks in a SPAN tag. By specifying their display property as inline-block in CSS, semantic units will no longer be split at the end of a line.

Budou is a simple Python script that runs each sentence through the Cloud Natural Language API. It can easily be extended as a custom filter for template engines, or as a task for runners such as Grunt and Gulp. The latest version also caches the response so no duplicate requests are sent. If you are using Budou for a static website, you can process your HTML code before deployment.

Budou is aimed to be used in relatively short sentences such as titles and headings. Screen readers may read a sentence by splitting the chunks wrapped by SPAN tag or split by WBR tag, so it is discouraged to use Budou for body paragraphs.

As of October 2016, the Cloud Natural Language API supports English, Spanish, and Japanese, and Budou currently only supports Japanese. Support for other Asian languages with line break issues, such as Chinese and Thai, will be added as the API adds support.

Any comments and suggestions are welcome. You can find us on GitHub.

By Shuhei Iitsuka, UX Engineer

Introducing the Google Sheets API v4: Transferring data from a SQL database to a Sheet

Posted by Wesley Chun (@wescpy), Developer Advocate, Google Apps

At Google I/O 2016, we launched a new Google Sheets API—click hereto watch the entire announcement. The updated API includes many new features that weren’t available in previous versions, including access to functionality found in the Sheets desktop and mobile user interfaces. My latest DevBytevideo shows developers how to get data into and out of a Google Sheet programmatically, walking through a simple script that reads rows out of a relational database and transferring the data to a brand new Google Sheet.

Let’s take a sneak peek of the code covered in the video. Assuming that SHEETS has been established as the API service endpoint, SHEET_ID is the ID of the Sheet to write to, and datais an array with all the database rows, this is the only call developers need to make to write that raw data into the Sheet:


SHEETS.spreadsheets().values().update(spreadsheetId=SHEET_ID,
range='A1', body=data, valueInputOption='RAW').execute()
Reading rows out of a Sheet is even easier. With SHEETS and SHEET_ID again, this is all you need to read and display those rows:
rows = SHEETS.spreadsheets().values().get(spreadsheetId=SHEET_ID,
range='Sheet1').execute().get('values', [])
for row in rows:
print(row)

If you’re ready to get started, take a look at the Python or other quickstarts in a variety of languages before checking out the DevByte. If you want a deeper dive into the code covered in the video, check out the post at my Python blog. Once you get going with the API, one of the challenges developers face is in constructing the JSON payload to send in API calls—the common operations samples can really help you with this. Finally, if you’re ready to get going with a meatier example, check out our JavaScript codelab where you’ll write a sample Node.js app that manages customer orders for a toy company, the database of which is used in this DevByte, preparing you for the codelab.

We hope all these resources help developers create amazing applications and awesome tools with the new Google Sheets API! Please subscribe to our channel, give us your feedback below, and tell us what topics you would like to see in future episodes!