web2py

Other recipes

Upgrading

In the "site" page of the administrative interface there is an "upgrade now" button. In case this is not feasible or does not work (for example because of a file locking issue), upgrading web2py manually is very easy.

Simply unzip the latest version of web2py over the old installation.

This will upgrade all the libraries as well as the applications admin, examples, welcome. It will also create a new empty file "NEWINSTALL". Upon restarting, web2py will delete the empty file and package the welcome app into "welcome.w2p" that will be used as the new scaffolding app.

web2py does not upgrade any file in other existing applications.

How to distribute your applications as binaries

It is possible to bundle your app with the web2py binary distribution and distribute them together. The license allows this as long you make clear in the license of your app that you are bundling with web2py and add a link to the web2py.com.

Here we explain how to do it for Windows:

• Create your app as usual
• Using admin, bytecode compile your app (one click)
• Using admin, pack your app compiled (another click)
• Create a folder "myapp"
• Download a web2py windows binary distribution
• Unzip it in folder "myapp" and start it (two clicks)
• Upload using admin the previously packed and compiled app with the name "init" (one click)
• Create a file "myapp/start.bat" that contains "cd web2py; web2py.exe"
• Create a file "myapp/license" that contains a license for your app and make sure it states that it is being "distributed with an unmodified copy of web2py from web2py.com"
• Zip the myapp folder into a file "myapp.zip"
• Distribute and/or sell "myapp.zip"

When users will unzip "myapp.zip" and click "run" they will see your app instead of the "welcome" app. There is no requirement on the user side, not even Python pre-installed.

For Mac binaries the process is the same but there is no need for the "bat" file.

Building a minimalist web2py

Some times we need to deploy web2py in a server with very small memory footprint. In this case we want to strip down web2py to its bare minimum.

An easy way to do it is the following:

• On a production machine, install the full web2py from source
• From inside the main web2py folder run

python scripts/make_min_web2py.py /path/to/minweb2py

• Now copy under "/path/to/minweb2py/applications" the applications you want to deploy
• Deploy "/path/to/minweb2py" to the small footprint server

The script "make_min_web2py.py" builds a minimalist web2py distribution that does not include:

• admin
• examples
• welcome
• scripts
• rarely used contrib modules

It does include a "welcome" app consisting of a single file to allow testing deployment. Look into this script. At the top it contains a detailed list of what is included and what is ignored. You can easily modify it and tailor to your needs.

Fetching an external URL

Python includes the urllib library for fetching urls:

import urllib
page = urllib.urlopen('http://www.web2py.com').read()

This is often fine, but the urllib module does not work on the Google App Engine. Google provides a different API for downloading URLs that works on GAE only. In order to make your code portable, web2py includes a fetch function that works on GAE as well as other Python installations:

from google.tools import fetch
page = fetch('http://www.web2py.com')

Pretty dates

It is often useful to represent a datetime not as "2009-07-25 14:34:56" but as "one year ago". web2py provides a utility function for this:

import datetime
d = datetime.datetime(2009,7,25,14,34,56)
from gluon.tools import prettydate
pretty_d = prettydate(d,T)

The second argument (T) must be passed to allow internationalization for the output.

Geocoding

If you need to convert an address (for example: "243 S Wabash Ave, Chicago, IL, USA") into geographical coordinates (latitude and longitude), web2py provides a function to do so.

from gluon.tools import geocode
address = '243 S Wabash Ave, Chicago, IL, USA'
(latitude, longitude) = geocode(address)

The function geocode requires a network connection and it connects to the Google geocoding service for the geocoding. The function returns (0,0) in case of failure. Notice that the Google geocoding service caps the number of requests, so you should check their service agreement. The geocode function is built on top of the fetch function and thus it works on GAE.

Pagination

This recipe is a useful trick to minimize database access in case of pagination, e.g., when you need to display a list of rows from a database but you want to distribute the rows over multiple pages.

Start by creating a primes application that stores the first 1000 prime numbers in a database.

Here is the model db.py:

db = DAL('sqlite://primes.db')
db.define_table('prime',Field('value','integer'))
def isprime(p):
    for i in range(2,p):
        if p%i==0: return False
    return True
if len(db().select(db.prime.id))==0:
   p=2
   for i in range(1000):
       while not isprime(p): p+=1
       db.prime.insert(value=p)
       p+=1

Now create an action list_items in the "default.py" controller that reads like this:

def list_items():
    if len(request.args): page=int(request.args[0])
    else: page=0
    items_per_page=20
    limitby=(page*items_per_page,(page+1)*items_per_page+1)
    rows=db().select(db.prime.ALL,limitby=limitby)
    return dict(rows=rows,page=page,items_per_page=items_per_page)

Notice that this code selects one more item than is needed, 20+1. The extra element tells the view whether there is a next page.

Here is the "default/list_items.html" view:

{{extend 'layout.html'}}

{{for i,row in enumerate(rows):}}
{{if i==items_per_page: break}}
{{=row.value}}<br />
{{pass}}

{{if page:}}
<a href="{{=URL(args=[page-1])}}">previous</a>
{{pass}}

{{if len(rows)>items_per_page:}}
<a href="{{=URL(args=[page+1])}}">next</a>
{{pass}}

In this way we have obtained pagination with one single select per action, and that one select only selects one row more then we need.

httpserver.log and the Log File Format

The web2py web server logs all requests to a file called:

httpserver.log

in the root web2py directory. An alternative filename and location can be specified via web2py command-line options.

New entries are appended to the end of the file each time a request is made. Each line looks like this:

127.0.0.1, 2008-01-12 10:41:20, GET, /admin/default/site, HTTP/1.1, 200, 0.270000

The format is:

ip, timestamp, method, path, protocol, status, time_taken

Where

• ip is the IP address of the client who made the request
• timestamp is the date and time of the request in ISO 8601 format, YYYY-MM-DDT HH:MM:SS
• method is either GET or POST
• path is the path requested by the client
• protocol is the HTTP protocol used to send to the client, usually HTTP/1.1
• status is the one of the HTTP status codes ^[status]
• time_taken is the amount of time the server took to process the request, in seconds, not including upload/download time.

In the appliances repository ^[appliances] , you will find an appliance for log analysis.

This logging is disabled by default when using mod_wsgi since it would be the same as the Apache log.

Populating database with dummy data

For testing purposes it is convenient to be able to populate database tables with dummy data. web2py includes a Bayesian classifier already trained to generate dummy but readable text for this purpose.

Here is the simplest way to use it:

from gluon.contrib.populate import populate
populate(db.mytable,100)

It will insert 100 dummy records into db.mytable. It will try to do intelligently by generating short text for string fields, longer text for text fields, integers, doubles, dates, datetimes, times, booleans, etc. for the corresponding fields. It will try to respect requirements imposed by validators. For fields containing the word "name" it will try to generate dummy names. For reference fields it will generate valid references.

If you have two tables (A and B) where B references A, make sure to populate A first and B second.

Because population is done in a transaction, do not attempt to populate too many records at once, particularly if references are involved. Instead, populate 100 at a time, commit, loop.

for i in range(10):
    populate(db.mytable,100)
    db.commit()

You can use the Bayesian classifier to learn some text and generate dummy text that sounds similar but should not make sense:

from gluon.contrib.populate import Learner, IUP
ell=Learner()
ell.learn('some very long input text ...')
print ell.generate(1000,prefix=None)

Accepting credit card payments

There are multiple ways to accept credit card payments online. web2py provides specific APIs for some of the most popular and practical ones:

• Google Wallet ^{[googlewallet]}
• PayPal paypalcite
• Stripe.com ^[stripe]
• Authorize.net ^{[authorizenet]}
• DowCommerece ^{[dowcommerce]}

The first two mechanisms above delegate the process of authenticating the payee to an external service. While this is the best solution for security (your app does not handle any credit card information at all) it makes the process cumbersome (the user must login twice; for example, once with your app, and once with Google) and does not allow your app to handle recurrent payments in an automated way.

There are times when you need more control and you want to generate yourself the entry form for the credit card info and than programmatically ask the processor to transfer money from the credit card to your account.

For this reason web2py provide integration out of the box with Stripe, Authorize.net (the module was developed by John Conde and slightly modified) and DowCommerce. Stripe is the simplest to use and also the cheapest for low volume of transactions (they charge no fix cost but charge about 3% per transaction). Authorize.net is better for high volumes (has a fixed yearly costs plus a lower cost per transaction).

Mind that in the case of Stripe and Authorize.net your program will be accepting credit cards information. You do not have to store this information and we advice you not to because of the legal requirements involved (check with Visa or Mastercard) but there are times when you may want to store the information for recurrent payments or to reproduce the Amazon one-click pay button.

Google Wallet

The simplest way to use Google Wallet (Level 1) consists of embedding a button on your page that, when clicked, redirects your visitor to a payment page provided by Google.

First of all you need to register a Google Merchant Account at the url:

https://checkout.google.com/sell

You will need to provide Google with your bank information. Google will assign you a merchant_id and a merchant_key (do not confuse them, keep them secret).

Then you simply need to create the following code in your view:

{{from gluon.contrib.google_wallet import button}}
{{=button(merchant_id="123456789012345",
           products=[dict(name="shoes",
                          quantity=1,
                          price=23.5,
                          currency='USD',
                          description="running shoes black")])}}

When a visitor clicks on the button, the visitor will be redirected to the Google page where he/she can pay for the items. Here products is a list of products and each product is a dictionary of parameters that you want to pass describing your items (name, quantity, price, currency, description, and other optional ones which you can find described in the Google Wallet documentation).

If you choose to use this mechanism you may want to generate the values passed to the button programmatically based on your inventory and the visitor shopping chart.

All the tax and shipping information will be handled on the Google side. Same for accounting information. By default your application is not notified that the transaction has been completed therefore you will have to visit your Google Merchant site to see which products have been purchased and paid for, and which products you need to ship to your buyers there. Google will also send you an email with the information.

If you want a tighter integration you have to use the Level 2 notification API. In that case you can pass more information to Google and Google will call your API to notify about purchases. This allows you to keep accounting information within your application but it requires you expose web services that can talk to Google Wallet.

This is a considerable more difficult problem but such API has already been implemented and it is available as plugin from

http://web2py.com/plugins/static/web2py.plugin.google_checkout.w2p

You can find the documentation of the plugin in the plugin itself.

Paypal

Paypal integration is not described here but you can find more information about it at this resource:

http://www.web2pyslices.com/main/slices/take_slice/9

Stripe.com

This is probably one of the easiest way and flexible ways to accept credit card payments.

You need to register with Stripe.com and that is a very easy process, in fact Stripe will assign you an API key to try even before you create any credentials.

Once you have the API key you can accept credit cards with the following code:

from gluon.contrib.stripe import Stripe
stripe = Stripe(api_key)
d = stripe.charge(amount=100,
	      currency='usd',
              card_number='4242424242424242',
              card_exp_month='5',
              card_exp_year='2012',
              card_cvc_check='123',
              description='the usual black shoes')
if d.get('paid',False):
    # payment accepted
elif:
    # error is in d.get('error','unknown')

The response, d, is a dictionary which you can explore yourself. The card number used in the example is a sandbox and it will always succeed. Each transaction is associated to a transaction id stored in d['id'].

Stripe also allows you verify a transaction at a later time:

d = Stripe(key).check(d['id'])

and refund a transaction:

r = Stripe(key).refund(d['id'])
if r.get('refunded',False):
    # refund was successful
elif:
    # error is in d.get('error','unkown')

Stripe makes very easy to keep the accounting within your application.

All the communications between your app and Stripe go over RESTful web services. Stripe actually exposes even more services and provides a larger set of Python API. You can read more on their web site.

Authorize.Net

Another simple way to accept credit cards is to use Authorize.Net. As usual you need to register and you will obtain a login and a transaction key (transkey. Once you have them it works very much like Stripe does:

from gluon.contrib.AuthorizeNet import process
if process(creditcard='4427802641004797',
           expiration="122012,
           total=100.0,cvv='123',tax=None,invoice=None,
           login='cnpdev4289', transkey='SR2P8g4jdEn7vFLQ',testmode=True):
   # payment was processed
else:
   # payment was rejected

If you have a valid Authorize.Net account you should replace the sandbox login and transkey with those of your account, set testmode=False to run on the real platform instead of the sandbox, and use credit card information provided by the visitor.

If process returns True, the money has been transferred from the visitor credit card account to your Authorize.Net account. invoice is just a string that you can set and will be store by Authorize.Net with this transaction so that you can reconcile the data with the information in your application.

Here is a more complex example of workflow where more variables are exposed:

from gluon.contrib.AuthorizeNet import AIM
payment = AIM(login='cnpdev4289',
              transkey='SR2P8g4jdEn7vFLQ',
              testmod=True)
payment.setTransaction(creditcard, expiration, total, cvv, tax, invoice)
payment.setParameter('x_duplicate_window', 180) # three minutes duplicate windows
payment.setParameter('x_cust_id', '1324')       # customer ID
payment.setParameter('x_first_name', 'Agent')
payment.setParameter('x_last_name', 'Smith')
payment.setParameter('x_company', 'Test Company')
payment.setParameter('x_address', '1234 Main Street')
payment.setParameter('x_city', 'Townsville')
payment.setParameter('x_state', 'NJ')
payment.setParameter('x_zip', '12345')
payment.setParameter('x_country', 'US')
payment.setParameter('x_phone', '800-555-1234')
payment.setParameter('x_description', 'Test Transaction')
payment.setParameter('x_customer_ip', socket.gethostbyname(socket.gethostname()))
payment.setParameter('x_email', '[email protected]')
payment.setParameter('x_email_customer', False)

payment.process()
if payment.isApproved():
    print 'Response Code: ', payment.response.ResponseCode
    print 'Response Text: ', payment.response.ResponseText
    print 'Response: ', payment.getResultResponseFull()
    print 'Transaction ID: ', payment.response.TransactionID
    print 'CVV Result: ', payment.response.CVVResponse
    print 'Approval Code: ', payment.response.AuthCode
    print 'AVS Result: ', payment.response.AVSResponse
elif payment.isDeclined():
    print 'Your credit card was declined by your bank'
elif payment.isError():
    print 'It did not work'
print 'approved',payment.isApproved()
print 'declined',payment.isDeclined()
print 'error',payment.isError()

Notice the code above uses a dummy test account. You need to register with Authorize.Net (it is not a free service) and provide your own login, transkey, testmode=True or False to the AIM constructor.

Dropbox API

Dropbox is a very popular storage service. It not only stores your files but it keeps the cloud storage in sync with all your machines. It allows you to create groups and give read/write permissions to the various folders to individual users or groups. It also keeps version history of all your files. It includes a folder called "Public" and each file you put in there will have its own public URL. Dropbox is a great way to collaborate.

You can access dropbox easily by registering at

https://www.dropbox.com/developers

you will get an APP_KEY and an APP_SECRET. Once you have them you can use Dropbox to authenticate your users.

Create a file called "yourapp/private/dropbox.key" and in it write

<APP_KEY>:<APP_SECERT>:app_folder

where <APP_KEY> and APP_SECRET are your key and secret.

Then in "models/db.py" do:

from gluon.contrib.login_methods.dropbox_account import use_dropbox
use_janrain(auth,filename='private/dropbox.key')
mydropbox = auth.settings.login_form

This will allow users to login into your app using their dropbox credentials, and your program will be able to upload files into their dropbox account:

stream = open('localfile.txt','rb')
mydropbox.put('destfile.txt',stream)

download files:

stream = mydropbox.get('destfile.txt')
open('localfile.txt','wb').write(read)

and get directory listings:

contents = mydropbox.dir(path = '/')['contents']

Twitter API

Here are some quick examples on how to post/get tweets. No third-party libraries are required, since Twitter uses simple RESTful APIs.

Here is an example of how to post a tweet:

def post_tweet(username,password,message):
    import urllib, urllib2, base64
    import gluon.contrib.simplejson as sj
    args= urllib.urlencode([('status',message)])
    headers={}
    headers['Authorization'] = 'Basic '+base64.b64encode(
        username+':'+password)
    req = urllib2.Request(
        'http://twitter.com/statuses/update.json',
        args, headers)
    return  sj.loads(urllib2.urlopen(req).read())

Here is an example of how to receive tweets:

def get_tweets():
    user='web2py'
    import urllib
    import gluon.contrib.simplejson as sj
    page = urllib.urlopen('http://twitter.com/%s?format=json' % user).read()
    tweets=XML(sj.loads(page)['#timeline'])
    return dict(tweets=tweets)

For more complex operations, refer to the Twitter API documentation.

Streaming virtual files

It is common for malicious attackers to scan web sites for vulnerabilities. They use security scanners like Nessus to explore the target web sites for scripts that are known to have vulnerabilities. An analysis of web server logs from a scanned machine or directly in the Nessus database reveals that most of the known vulnerabilities are in PHP scripts and ASP scripts. Since we are running web2py, we do not have those vulnerabilities, but we will still be scanned for them. This is annoying, so we like to respond to those vulnerability scans and make the attacker understand their time is being wasted.

One possibility is to redirect all requests for .php, .asp, and anything suspicious to a dummy action that will respond to the attack by keeping the attacker busy for a large amount of time. Eventually the attacker will give up and will not scan us again.

This recipe requires two parts.

A dedicated application called jammer with a "default.py" controller as follows:

class Jammer():
   def read(self,n): return 'x'*n
def jam(): return response.stream(Jammer(),40000)

When this action is called, it responds with an infinite data stream full of "x"-es. 40000 characters at a time.

The second ingredient is a "route.py" file that redirects any request ending in .php, .asp, etc. (both upper case and lower case) to this controller.

route_in=(
 ('.*.(php|PHP|asp|ASP|jsp|JSP)','jammer/default/jam'),
)

The first time you are attacked you may incur a small overhead, but our experience is that the same attacker will not try twice.