BI 4.1 Internet - Web Server

Quick launch into Flask, sending and receiving data from a web server.

• 10 min read

Introduction

Welcome to this journey into the world of web servers and the Flask framework! In the previous weeks, you’ve successfully set up a web server using GitHub Pages, converting Jupyter Notebooks into Markdown for a seamless online presentation. Today, we’ll take that knowledge to the next level as we dive into creating your very own web server using Flask.

Understanding Web Servers

What is a Web Server?

Traditionally, we had librarians at libraries that would help you find books or information. Today in the digital world, thousands upon thousands of home pages, search engines, and digital archives have been built using web servers.

GitHub Pages vs. Flask

You’ve already experienced a form of web server through GitHub Pages. Think of GitHub Pages as a library that has established rules for publishing Markdown notes and Jupyter Notebooks neatly on a bookshelf.

Now, let’s introduce Flask, your personal web server. Flask can create and manage any type of content, including customizing everything according to your preferences, and even serve additional information (like a database with APIs).

The Flask Framework Flask is a micro web framework written in Python. It’s designed to be minimal and easy to use, making it perfect for building web applications, APIs, and, yes, even your web server. Today, we will start with the basics of Flask and see how it empowers you to create and manage web content.

Our Goals for Today

Here’s what we’ll accomplish in this session:

  • Create a minimal Flask server.
  • Explore the Python/Flask process.
  • Access data from our Flask server using Python.
  • Access data from our Flask server using JavaScript.
  • Learn how to stop the Python/Flask process gracefully.

Install required libraries

These libraries are required to run and interact with the Python web server.

!pip install flask flask-cors requests

Collecting flask
  Using cached flask-3.1.0-py3-none-any.whl.metadata (2.7 kB)
Collecting flask-cors
  Using cached Flask_Cors-5.0.0-py2.py3-none-any.whl.metadata (5.5 kB)
Requirement already satisfied: requests in /home/canadianpotatoe/Kanhay_2025/venv/lib/python3.12/site-packages (2.32.3)
Collecting Werkzeug>=3.1 (from flask)
  Using cached werkzeug-3.1.3-py3-none-any.whl.metadata (3.7 kB)
Requirement already satisfied: Jinja2>=3.1.2 in /home/canadianpotatoe/Kanhay_2025/venv/lib/python3.12/site-packages (from flask) (3.1.4)
Collecting itsdangerous>=2.2 (from flask)
  Using cached itsdangerous-2.2.0-py3-none-any.whl.metadata (1.9 kB)
Collecting click>=8.1.3 (from flask)
  Using cached click-8.1.7-py3-none-any.whl.metadata (3.0 kB)
Collecting blinker>=1.9 (from flask)
  Using cached blinker-1.9.0-py3-none-any.whl.metadata (1.6 kB)
Requirement already satisfied: charset-normalizer<4,>=2 in /home/canadianpotatoe/Kanhay_2025/venv/lib/python3.12/site-packages (from requests) (3.4.0)
Requirement already satisfied: idna<4,>=2.5 in /home/canadianpotatoe/Kanhay_2025/venv/lib/python3.12/site-packages (from requests) (3.10)
Requirement already satisfied: urllib3<3,>=1.21.1 in /home/canadianpotatoe/Kanhay_2025/venv/lib/python3.12/site-packages (from requests) (2.2.3)
Requirement already satisfied: certifi>=2017.4.17 in /home/canadianpotatoe/Kanhay_2025/venv/lib/python3.12/site-packages (from requests) (2024.8.30)
Requirement already satisfied: MarkupSafe>=2.0 in /home/canadianpotatoe/Kanhay_2025/venv/lib/python3.12/site-packages (from Jinja2>=3.1.2->flask) (3.0.2)
Using cached flask-3.1.0-py3-none-any.whl (102 kB)
Using cached Flask_Cors-5.0.0-py2.py3-none-any.whl (14 kB)
Using cached blinker-1.9.0-py3-none-any.whl (8.5 kB)
Using cached click-8.1.7-py3-none-any.whl (97 kB)
Using cached itsdangerous-2.2.0-py3-none-any.whl (16 kB)
Using cached werkzeug-3.1.3-py3-none-any.whl (224 kB)
Installing collected packages: Werkzeug, itsdangerous, click, blinker, flask, flask-cors
Successfully installed Werkzeug-3.1.3 blinker-1.9.0 click-8.1.7 flask-3.1.0 flask-cors-5.0.0 itsdangerous-2.2.0

Start Web Server

This Python code provides a simple server with an accessible API.

Note: Jupyter magic commmand %%python --bg that follows runs the server in background. This enables us to continue interacting with the subsequent Notebook cells.

%%python --bg

from flask import Flask, jsonify
from flask_cors import CORS

# initialize a flask application (app)
app = Flask(__name__)
CORS(app, supports_credentials=True, origins='*')  # Allow all origins (*)

# ... your existing Flask

# add an api endpoint to flask app
@app.route('/api/data')
def get_data():
    # start a list, to be used like a information database
    InfoDb = []

    # add a row to list, an Info record
    InfoDb.append({
        "FirstName": "John",
        "LastName": "Mortensen",
        "DOB": "October 21",
        "Residence": "San Diego",
        "Email": "jmortensen@powayusd.com",
        "Owns_Cars": ["2015-Fusion", "2011-Ranger", "2003-Excursion", "1997-F350", "1969-Cadillac"]
    })

    # add a row to list, an Info record
    InfoDb.append({
        "FirstName": "Shane",
        "LastName": "Lopez",
        "DOB": "February 27",
        "Residence": "San Diego",
        "Email": "slopez@powayusd.com",
        "Owns_Cars": ["2021-Insight"]
    })
    InfoDb.append({
        "FirstName": "Kanhay",
        "LastName": "Patil",
        "DOB": "October 14",
        "Residence": "San Diego",
        "Email": "Kanhay.Patil@gmail.com",
        "Owns_Cars": []
    })
    
    return jsonify(InfoDb)

# add an HTML endpoint to flask app
@app.route('/')
def say_hello():
    html_content = """
    <html>
    <head>
        <title>Hellox</title>
    </head>
    <body>
        <h2>Hello, World!
            I'm Kanhay
        </h2>
    </body>
    </html>
    """
    return html_content

if __name__ == '__main__':
    # starts flask server on default port, http://127.0.0.1:5001
    app.run(port=5001)

Explore the Python/Flask process with Linux

This script discovers the running flask process on your machine using Linux commands.

  1. lsof - list open files
  2. lsof and awk return the process id, so ps can list details, the vericle bar is called a pipe. A pipe flows output from one command to the next.
  3. curl is a Linux utiltity that is easiest way to test if web server is responding
%%script bash

# After app.run(), see the the Python open files on port 5001
echo "Python open files on port 5001" 
lsof -i :5001
# see the the Python process 
echo
echo "Python process"
lsof -i :5001 | awk '/Python/ {print $2}' | xargs ps
# show ontent of the Python server using curl
echo
echo "Content of the Python root endpoint (aka /), using curl",  
curl http://localhost:5001/



Python open files on port 5001
COMMAND  PID            USER   FD   TYPE DEVICE SIZE/OFF NODE NAME
python  1726 canadianpotatoe    3u  IPv4  27486      0t0  TCP localhost:5001 (LISTEN)
python  1726 canadianpotatoe    4u  IPv4  18141      0t0  TCP localhost:5001->localhost:45170 (ESTABLISHED)
python  1726 canadianpotatoe    5u  IPv4  18140      0t0  TCP localhost:5001->localhost:45158 (ESTABLISHED)

Python process


your 131072x1 screen size is bogus. expect trouble


    PID TTY          TIME CMD
    714 pts/2    00:00:00 sh
    715 pts/2    00:00:00 sh
    720 pts/2    00:00:00 sh
    724 pts/2    00:00:03 node
    775 pts/2    00:00:02 node
    786 pts/2    00:00:12 node
    820 pts/2    00:00:01 pet
   1089 pts/2    00:00:00 node
   1116 pts/2    00:00:11 node
   1192 pts/2    00:00:00 node
   1611 pts/2    00:00:00 python
   1726 pts/2    00:00:00 python
   2194 pts/2    00:00:00 bash
   2198 pts/2    00:00:00 xargs
   2199 pts/2    00:00:00 ps

Content of the Python root endpoint (aka /), using curl,


  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   135  100   135    0     0  34677      0 --:--:-- --:--:-- --:--:-- 45000



    <html>
    <head>
        <title>Hellox</title>
    </head>
    <body>
        <h2>Hello, World!</h2>
    </body>
    </html>

Access data from our Flask server using Python

The code block below shows alternate ways to access the Web Server.

  1. Import requests and use it to obtain response from endpoints
  2. The response is a Python object that contains status codes and data
  3. The data can be in different forms, we will be focussed on JSON responses in Full-Stack.
import requests
from IPython.display import HTML, display

# call api root endpoint (aka '/'), often called home page
response = requests.get('http://127.0.0.1:5001/api/data')
# output response in different forms
print("Print Status Message:", response)
print("\nPrint Raw HTML:\n", response.text)
display(HTML(response.text))

# call unknown api endpoint
response = requests.get('http://127.0.0.1:5001/unknown-page')
print("Print Status Message:", response)

Print Status Message: <Response [200]>

Print Raw HTML:
 [{"DOB":"October 21","Email":"jmortensen@powayusd.com","FirstName":"John","LastName":"Mortensen","Owns_Cars":["2015-Fusion","2011-Ranger","2003-Excursion","1997-F350","1969-Cadillac"],"Residence":"San Diego"},{"DOB":"February 27","Email":"slopez@powayusd.com","FirstName":"Shane","LastName":"Lopez","Owns_Cars":["2021-Insight"],"Residence":"San Diego"}]

[{“DOB”:”October 21”,”Email”:”jmortensen@powayusd.com”,”FirstName”:”John”,”LastName”:”Mortensen”,”Owns_Cars”:[“2015-Fusion”,”2011-Ranger”,”2003-Excursion”,”1997-F350”,”1969-Cadillac”],”Residence”:”San Diego”},{“DOB”:”February 27”,”Email”:”slopez@powayusd.com”,”FirstName”:”Shane”,”LastName”:”Lopez”,”Owns_Cars”:[“2021-Insight”],”Residence”:”San Diego”}]

Print Status Message: <Response [404]>

import requests
# an api endpoint most commonly returns JSON data
response = requests.get('http://127.0.0.1:5001/api/data')
response.json()

[{'DOB': 'October 21',
  'Email': 'jmortensen@powayusd.com',
  'FirstName': 'John',
  'LastName': 'Mortensen',
  'Owns_Cars': ['2015-Fusion',
   '2011-Ranger',
   '2003-Excursion',
   '1997-F350',
   '1969-Cadillac'],
  'Residence': 'San Diego'},
 {'DOB': 'February 27',
  'Email': 'slopez@powayusd.com',
  'FirstName': 'Shane',
  'LastName': 'Lopez',
  'Owns_Cars': ['2021-Insight'],
  'Residence': 'San Diego'}]

Access data from our Flask server using JavaScript

This sample is very similar to Full-Stack as the JavaScript is running through Jupyter and the Web server is a Python Process running on our machine (local server).

  1. HTML is used to setup basics of a table
  2. The script block, has javascript fetch that passes endpoint (url) and options. The options are critical to communicating request requirements.
  3. Similar to python examples, data is extracted and that data is written to the document, which is what is viewable to the user as the page is rendered. Headings are static in the document, but rows are dynamically extracted according to the information contained in the server.
%%html

<h1>Access data from our Flask server using JavaScript</h1>

<p>This code extracts data "live" from a local Web Server with JavaScript fetch.  Additionally, it formats the data into a table.</p>

<!-- Head contains information to Support the Document -->


<!-- HTML table fragment for page -->
<table id="demo" class="table">
  <thead>
      <tr>
          <th>First Name</th>
          <th>Last Name</th>
          <th>Residence</th>
      </tr>
  </thead>
  <tbody id="result">
    <!-- javascript generated data -->
  </tbody>
</table>

<script>
  // prepare HTML result container for new output
  let resultContainer = document.getElementById("result");
  
  // prepare URL
  url = "http://127.0.0.1:5001/api/data";

  // set options for cross origin header request
  let options = {
    method: 'GET', // *GET, POST, PUT, DELETE, etc.
    mode: 'cors', // no-cors, *cors, same-origin
    cache: 'default', // *default, no-cache, reload, force-cache, only-if-cached
    credentials: 'include', // include, *same-origin, omit
    headers: {
      'Content-Type': 'application/json',
    },
  };

  // fetch the API
  fetch(url, options)
    // response is a RESTful "promise" on any successful fetch
    .then(response => {
      // check for response errors and display
      if (response.status !== 200) {
          console.error(response.status);
          return;
      }
      // valid response will contain json data
      response.json().then(data => {
          console.log(data);
          for (const row of data) {
            // tr and td build out for each row
            const tr = document.createElement("tr");
            const firstname = document.createElement("td");
            const lastname = document.createElement("td");
            const residence = document.createElement("td");
            // data is specific to the API
            firstname.innerHTML = row.FirstName; 
            lastname.innerHTML = row.LastName; 
            residence.innerHTML = row.Residence; 
            // this builds each td into tr
            tr.appendChild(firstname);
            tr.appendChild(lastname);
            tr.appendChild(residence);
            // add HTML to container
            resultContainer.appendChild(tr);
          }
      })
  })
  
</script>

Access data from our Flask server using JavaScript

This code extracts data "live" from a local Web Server with JavaScript fetch. Additionally, it formats the data into a table.

First Name Last Name Residence

Stop the Python/Flask process

This script ends Python/Flask process using pipes to obtain the python process. Then echo the python process to kill -9.

%%script bash

python_ps=$(lsof -i :5001 | awk '/python/ {print $2}')
echo "Killing python process with PID: $python_ps"
echo $python_ps | xargs kill -9

Killing python process with PID: 



Usage:
 kill [options] <pid> [...]

Options:
 <pid> [...]            send signal to every <pid> listed
 -<signal>, -s, --signal <signal>
                        specify the <signal> to be sent
 -q, --queue <value>    integer value to be sent with the signal
 -l, --list=[<signal>]  list all signal names, or convert one to a name
 -L, --table            list all signal names in a nice table

 -h, --help     display this help and exit
 -V, --version  output version information and exit

For more details see kill(1).



---------------------------------------------------------------------------

CalledProcessError                        Traceback (most recent call last)

Cell In[2], line 1
----> 1 get_ipython().run_cell_magic('script', 'bash', '\npython_ps=$(lsof -i :5001 | awk \'/python/ {print $2}\')\necho "Killing python process with PID: $python_ps"\necho $python_ps | xargs kill -9\n')


File ~/Kanhay_2025/venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py:2541, in InteractiveShell.run_cell_magic(self, magic_name, line, cell)
   2539 with self.builtin_trap:
   2540     args = (magic_arg_s, cell)
-> 2541     result = fn(*args, **kwargs)
   2543 # The code below prevents the output from being displayed
   2544 # when using magics with decorator @output_can_be_silenced
   2545 # when the last Python token in the expression is a ';'.
   2546 if getattr(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, False):


File ~/Kanhay_2025/venv/lib/python3.12/site-packages/IPython/core/magics/script.py:315, in ScriptMagics.shebang(self, line, cell)
    310 if args.raise_error and p.returncode != 0:
    311     # If we get here and p.returncode is still None, we must have
    312     # killed it but not yet seen its return code. We don't wait for it,
    313     # in case it's stuck in uninterruptible sleep. -9 = SIGKILL
    314     rc = p.returncode or -9
--> 315     raise CalledProcessError(rc, cell)


CalledProcessError: Command 'b'\npython_ps=$(lsof -i :5001 | awk \'/python/ {print $2}\')\necho "Killing python process with PID: $python_ps"\necho $python_ps | xargs kill -9\n'' returned non-zero exit status 123.

Hacks

Edit, stop and start the web server.

  • Add to the Home Page
  • Add your own information to the Web API
  • Use from Template to start your own Team Flask project https://github.com/nighthawkcoders/flocker_backend

Stuff I did Schema Update for theme_mode:

Database Schema Reset: I initiated a schema reset and dropped existing tables.

Schema Update: Added the _theme_mode column to the users table to store users’ theme preferences.

Model and API Adjustments:

Model Update: Updated the User model in user.py to include _theme_mode, ensuring the model matched the updated schema.

API Update: Adjusted the API to handle theme_mode during user creation and updates.

Frontend Integration:

Display Current Theme: Created a button to display the current theme mode, setting up the foundation to toggle between light and dark modes.