You can work on the project using your own machine, or some other environment. However, the course staff can only support the enviroment we recommend. You will also need to make sure that the URL you submit for part 3 works when the staff visits and grades it.
The following documentation may be helpful for both learning Python and Flask:
- Java to Python Cheatsheet
- Python 2 tutorial
- Python 3 tutorial
- Learn Python The Hard Way
- Flask documentation
- Flask Tutorial
- Jinja Template documentation
- Jinja Tutorial
If your application has users, and you'd like to implement login/logout pages with password authentication, check:
- Flask Quickstart: Sessions
- Creating a login page
- Note: do not follow the "Connecting to your database" section of this tutorial, as it uses ORM. Remember that you are not allowed to use ORM, and your code must issue SQL queries instead.
One drawback of using a cloud computing platform is that it is difficult to open GUI text editors such as Sublime Text to write your code. We recommend setting up a version control system for your project, such as git on GitHub, so your team can share code. This way, you can code on your desktop, commit your changes, and pull the updated changes on your cloud virtual machine.
We strongly recommend reading the following documentations:
- General Flask Documentation
- Jinja Templates: this makes it easy to send data (e.g., arrays, dictionaries) to your HTML code and dynamically render them.
In part 3, we will deploy our projects on PythonAnywhere, a free and easy-to-use cloud platform that helps us host our website.
Now, we are going to walk you through the process to deploy your project 1 on the platform. Suppose we already have a simple Flask application on Github that looks something like this.
COMS4111_Proj1/
│ README.md
│ requirements.txt
└───webserver/
│ server.py
└───static
requirements.txt includes all the packages we need for this project and
static directory is where we store all our static files.
To begin with, we need to create a free beginner account on PythonAnywhere.
After logging in PythonAnywhere, click $Bash button under the New console.
A Bash console should pop up.
Now in the command line, type the following command to clone your project on github onto the PythonAnywhere.
git clone <your-github-repo-address>
You need to enter your github Username and generate a Personal access tokens to connect to your github and download your project files.
If success, the bash should look something like this.
We now need to create a virtual environment for our flask application.
Type the following command. Note that if you use a different version of Python for project 1, you need to specify that Python version.
mkvirtualenv <env_name> --python='/usr/bin/python3.9'
If success, the bash should look something like this.
We could now use cd to move to the root directory of our project. And
type the following command. This command will automatically help us to install
all the packages we need in the virtualenv.
pip install -r requirements.txt
If success, the bash should look something like this
One thing to keep in mind is that PythonAnywhere has some limits on what third-party packages
we could install on our virtual machine. If you find the pip install command fail, please check
on this page to make sure that the third-party
packages you required are available on PythonAnywhere.
We have set up the virtual environment for our Flask application. Now, we need to make several settings on PythonAnywhere to make our project running.
We go back to the dashboard of PythonAnywhere and click on Open Web tab button
under the All Web apps title. Press Add a new web app. Ignore the advertisement to lure us to bug
an upgrade plan by clicking Next. Select Manual Configuration. Select the Python version
of the project. Click on Next. Now you should see a web page like this
There are some settings we need to change.
First, specifying the path to our source code by modifying Source code under the Code section.
Path should be in this format:
/home/<Your-username>/<Root-directory-of-Project>
Second, clicking on that WSGI configuration file on the third row and open it.
We first delete the lines from 19-47 and uncomment codes under Flask section.
You WSGI file should look something like this
The most important line is line 82. In our application, variable app is defined
inside server.py file. If your application define app elsewhere, you need to import
app from that specific file.
Third, we need to specify the virtualenv used for our project by entering the name of our
virtual environment like this.
Pressing enter. The final result should looks like this.
At last, link /static/ url to our static directory.
That will be all! Reload and click on the link at the top. You should see your application up and running!
If, unfortunately, your app is not running. One thing you need to do is to check the error log to see what's going wrong on the sever and try to fix that. Don't hesitate to reach to TAs when you couldn't fix the error!
We use a python package called SQLAlchemy to simplify our work for connecting to the database.
For example, server.py contains the following code to load useful functions from
the package:
# import useful functions from the package
from sqlalchemy import *
SQLAlchemy is able to connect to many different types of DBMSes such as
SQLite, PostgreSQL, MySQL, Oracle and other databases. Each such DBMS
is called an "engine". The create_engine() function sets up the configuration
to specify which type of DBMS we want to connect to, and what their parameters are.
engine = create_engine(DATABASEURI)
Given an engine, we can then connect to it (this is similar to how psql connects
to the staff database).
conn = engine.connect()
At this point, the conn connection object can be used to
execute queries to the database. This is basically what psql
is doing under the covers!
cursor = conn.execute("select 1")
The execute function takes a SQL query string as input, and
returns a cursor object. You can think of this as an iterator
over the result relation. This means you can run select *
on a million row table, and not run out of memory. Instead of
sending the entire result at once. Instead, this
object lets you treat the result as an iterator and call .next()
on it, or loop through it. See the documentation for a detailed description.
# this fetches the first row if called right after
# the execute function above. It also moves the
# iterator to the next result row.
record = cursor.fetchone()
# this will fetch the next record, or None if
# there are no more results.
second_record = cursor.fetchone()
# this loops through the results of the cursor one by one
for row in cursor:
print list(row)
The above description is a way to directly write and run SQL queries as strings, and directly manipulate the result relations. SQLAlchemy is also an Object Relational Mapper that provides an interface that hides SQL query strings and result sets from you. Instead you access and manipulate tables in the database as if they were normal Python objects.
In this project, you will directly write and run SQL queries, and will not use any ORM functionality.
-
Fork this repository so you have your own copy that you can edit. You will submit a link to the repository. (click the Fork button on the top right corner of this page)
-
Clone it to your VM (or your local machine, if you have Python installed and want to run locally):
git clone [email protected]:[YOUR_GITHUB_USERNAME]/project1.git -
Edit your files
-
Use the following commands to add and checkpoint (commit) your changes locally
git add --help git add <new files to store in git> git commit -m "a sentence describing your changes" -
When everything has been committed you can
pushall the committed changes so GitHub.com has a copygit push -
If you cloned the repository on another machine (say the VM), then you can download and apply those changes from GitHub.com
git pull
Some notes
- Your life will be easier by setting up SSH keys
and cloning the
git://....versions of repositories. That way GitHub won't keep asking for your password when runninggitcommands. However, if don't know what this means, stick to the original HTTP version. - Most errors you will encounter can be solved by consulting a search engine.
You will deploy your application to PythonAnywhere. Please refer to Flask Python Webserver for
step-by-step instructions on how to do that.
Note: This is just a suggestion. Since it is impossible to support setting up Python on everyone's personal computers, we can't really help debug issues that aren't happening on an Google App Engine VM. Your best bet is google, office hours, or asking your fellow students on the discussion board.
It is much more convenient to be able to test your application on your laptop or local computer, and run it on the Google App Engine VM when you are happy with the code. You can do this by following the virtualenv setup commands from HW0 on your own computer. Once you have the correct virtualenv set up, you can run the the web server with:
To run the webserver, go into the webserver/ directory and run (make sure you have enabled the virtualenv environment)
python server.py --debug
It should print something like:
running on 0.0.0.0:8111
* Running on http://0.0.0.0:8111/
The 0.0.0.0 listens to any IPv4 address on the machine. The 8111 after the : is the port number.
So if this is running on your laptop, you can open you web browser to http://localhost:8111.
You can specify a custom port by passing a host and port as arguments:
python server.py --debug 0.0.0.0 8888
To see its command line options, use the --help flag
python server.py --help
If you run the server with the --debug flag, it will automatically pick up changes when you reload the page, which is more convenient than restarting the server each time. It additionally will display detailed errors in the web browser, instead of only on the console.
The following are optional instructions on how to keep servers running. You'll need it after your project is complete, so staff can access your application to run additional tests if needed.
There are several ways to keep the server running after you have logged out of the VM. Note that these are all poor man's techniques.
-
nohup. the HUP signal is how the terminal warns a process of user logout. the nohup command ensures that the process ignores this signal, allowing it to continue running. the "&" character at the end of the command tells the terminal to detach this process from the terminal.
nohup python server.py 0.0.0.0 8008 &You can kill the process explicitly by getting the process ID and using the
killcommand:ps -A | grep python kill <the ID of the python process> -
tmux is a remote terminal manager. You can think of the terminal as two parts --
the client that you interact with by typing characters and pressing ENTER, and a server that actually reads those commands and runs processes in response.
Usually when you login to a VM, the client and server are tied together in a single process, so that when you logout the client and server both die. TMUX on the other hand explicitly starts two processes -- the server process that continues to run after you log out, and a client process that connects to the server process. This way, even if you disconnect, only the client dies. When you re-connect, you can re-attach to the server process and resume your terminal session! This is what I do.# install tmux sudo apt-get install tmux # run tmux tmux # it will open a terminal python server.py 0.0.0.0 8008 # don't press ctrl-c, just close your window.
Tmux is quite powerful -- come ask me directly or post to the discussion board if you are curious about its other functionalities. GNU Screen is an alternative to tmux.