Web Scraping and Text Processing: A Comprehensive Guide to Flask App Development
The world of web scraping can seem intimidating at first glance. With so many different libraries and tools available to developers, it may be difficult to know where to start.
In this article, we’ll explore the fundamentals of web scraping and text processing, focusing on key topics such as Flask app development, local development environments, and deploying to Heroku.
Setting up your local development environment
Before jumping into coding, it’s important to set up a local development environment. This will help you create, test, and debug your Flask app more efficiently.
Flask is a popular Python web framework that’s easy to set up and get started with. You’ll need to install Python before you can start working with Flask.
Once Python is installed, you’ll need to install Flask. You can do this by typing in the following command in your terminal:
“`
pip install flask
“`
Now that Flask is installed, you can create a new Flask app. Create a new file called `app.py` and enter the following code:
“`
from flask import Flask
app = Flask(__name__)
@app.route(“/”)
def index():
return “Hello, World!”
if __name__ == “__main__”:
app.run()
“`
This code creates a new Flask app and defines a route for the index page. When you run the app, the index route will return the text “Hello, World!”.
Deploying your app to Heroku
Once your Flask app is up and running locally, you may want to deploy it to a production environment such as Heroku. Heroku is a cloud platform that allows you to deploy, manage, and scale web applications.
Before deploying to Heroku, you’ll need to create a new Git repository for your app. This can be done by navigating to your project folder and typing in the following command:
“`
git init
“`
You’ll then need to create a new file called `Procfile` and enter the following code:
“`
web: gunicorn app:app
“`
This file tells Heroku how to run your app. The `web` process type is used for web applications, and `gunicorn` is a Python WSGI HTTP server that’s commonly used with Flask.
The `app:app` syntax tells gunicorn which file and application to use. Once your Git repository is set up and your Procfile is created, you can deploy your app to Heroku.
First, you’ll need to create a new Heroku app by typing in the following command:
“`
heroku create
“`
This will create a new Heroku app with a random name. You can then upload your code to Heroku by typing in the following command:
“`
git push heroku master
“`
Your app should now be accessible at the URL assigned to it by Heroku.
Using the requests library to grab HTML
One of the first steps in web scraping is to retrieve the HTML page you’re interested in. This can be done using the requests library.
The requests library is a popular Python library used for making HTTP requests. To use the requests library, you’ll need to install it by typing in the following command:
“`
pip install requests
“`
Once the requests library is installed, you can use it to grab the HTML page from a URL. Here’s an example:
“`
import requests
url = “https://www.example.com”
response = requests.get(url)
html = response.text
“`
In this example, we’re grabbing the HTML page from the URL `https://www.example.com`. We then store the response in the variable `response`.
Finally, we extract the HTML text from the response and store it in the variable `html`.
Text processing using BeautifulSoup and Natural Language Toolkit libraries
Once you’ve retrieved the HTML page, the next step is to process the text. This can be done using libraries such as BeautifulSoup and Natural Language Toolkit (NLTK).
BeautifulSoup is a Python library used for parsing HTML and XML documents. NLTK is a Python library used for natural language processing tasks such as tokenization, stemming, and named entity recognition.
To use BeautifulSoup, you’ll need to install it by typing in the following command:
“`
pip install beautifulsoup4
“`
Once BeautifulSoup is installed, you can use it to extract text from HTML. Here’s an example:
“`
from bs4 import BeautifulSoup
soup = BeautifulSoup(html, “html.parser”)
text = soup.get_text()
“`
In this example, we’re creating a new BeautifulSoup object and passing in the HTML text as well as the parser to use. We then extract the text from the HTML using the `get_text()` method.
Counting frequency of words and removing stop words
Once you’ve extracted the text, you may want to count the frequency of words in the text and remove stop words. Stop words are common words such as “the”, “and”, and “a” that are often removed from text during processing.
To count the frequency of words, you can use Python’s built-in `collections` module. Here’s an example:
“`
import collections
from nltk.tokenize import word_tokenize
words = word_tokenize(text.lower())
word_counts = collections.Counter(words)
“`
In this example, we’re tokenizing the text using NLTK’s `word_tokenize()` function and converting all the words to lowercase. We’re then using Python’s `collections` module to count the frequency of each word.
To remove stop words, you can use NLTK’s built-in `stopwords` corpus. Here’s an example:
“`
from nltk.corpus import stopwords
stop_words = set(stopwords.words(“english”))
filtered_words = [word for word in words if word.lower() not in stop_words]
“`
In this example, we’re creating a set of stop words using NLTK’s `stopwords` corpus.
We’re then creating a new list of words that don’t appear in the stop words set.
Refactoring the Index Route
Now that we’ve covered the fundamentals of web scraping and text processing, let’s explore how to refactor the index route of a Flask app. This can help improve the user experience and make your code more organized.
Rendering a form to accept URLs and displaying errors
One common feature of web scraping apps is a form that accepts URLs. This can be done using Flask’s built-in `request` object. Here’s an example:
“`
from flask import request, render_template
@app.route(“/”, methods=[“GET”, “POST”])
def index():
url = “”
error = “”
if request.method == “POST”:
url = request.form[“url”]
if not url.startswith(“http”):
error = “Invalid URL”
url = “”
return render_template(“index.html”, url=url, error=error)
“`
In this example, we’re checking if the request method is POST.
If it is, we’re retrieving the URL from the form using the `request.form` object. We’re then checking if the URL starts with “http”.
If it doesn’t, we’re setting an error message and resetting the URL to an empty string.
Using POST and GET methods to handle form submissions
To handle form submissions, we can use Flask’s `redirect` and `url_for` methods. Here’s an example:
“`
from flask import redirect, url_for
@app.route(“/results/
def results(url):
# retrieve HTML, process text, etc.
return “”
@app.route(“/”, methods=[“GET”, “POST”])
def index():
if request.method == “POST”:
url = request.form[“url”]
return redirect(url_for(“.results”, url=url))
return render_template(“index.html”)
“`
In this example, we’re defining a new route called `results` that takes a URL as a path parameter. We’re then redirecting to this route when the form is submitted using Flask’s `redirect` and `url_for` methods.
Cleaning HTML text using BeautifulSoup
To clean the HTML text, we can use the same method as before:
“`
from bs4 import BeautifulSoup
soup = BeautifulSoup(html, “html.parser”)
text = soup.get_text()
“`
Counting raw words and removing punctuation
To count the raw words and remove punctuation, we can use the same methods as before:
“`
import collections
from nltk.tokenize import word_tokenize
import string
words = word_tokenize(text.lower())
words = [word for word in words if word not in string.punctuation]
word_counts = collections.Counter(words)
“`
In this example, we’re removing all punctuation using Python’s `string` module.
Conclusion
In this article, we explored the fundamentals of web scraping and text processing. We covered key topics such as Flask app development, local development environments, and deploying to Heroku.
We also explored libraries such as requests, BeautifulSoup, and NLTK that are commonly used for web scraping and text processing tasks. Finally, we refactored the index route of a Flask app to improve the user experience and make our code more organized.
Saving the Results: Storing Processed Text Data Using SQLAlchemy and Alembic
Web scraping and text processing can be time-consuming tasks that require a significant amount of computational power. Due to the high volume of traffic that web scraping applications can receive, it is important to implement a way to store and retrieve processed text data quickly and efficiently.
This is where SQLAlchemy and Alembic come in.
Using SQLAlchemy for Object-Relational Mapping
SQLAlchemy is a popular Python library for object-relational mapping (ORM). ORM is a programming technique for converting data between incompatible type systems.
This is useful for web applications where data is stored in a relational database, but needs to be retrieved in Python objects. Before using SQLAlchemy, you’ll need to install it by typing in the following command:
“`
pip install sqlalchemy
“`
Once SQLAlchemy is installed, you can create a models file to define the database schema. Here’s an example:
“`
from sqlalchemy import Column, Integer, String, Text
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
class TextData(Base):
__tablename__ = “text_data”
id = Column(Integer, primary_key=True)
url = Column(String)
text = Column(Text)
“`
In this example, we’re creating a new model called `TextData` that has an `id`, `url`, and `text` column.
The `__tablename__` attribute specifies the name of the database table.
Using Alembic for Database Migrations
Alembic is a database migration library that’s commonly used with SQLAlchemy. Database migrations are a way to update the database schema without losing any data.
This is useful for web applications where the database schema changes frequently. Before using Alembic, you’ll need to install it by typing in the following command:
“`
pip install alembic
“`
Once Alembic is installed, you can create a new migration file by typing in the following command:
“`
alembic revision -m “create text_data table”
“`
This will create a new migration file that you can edit to define the changes to the database schema. Here’s an example migration file:
“`
from alembic import op
import sqlalchemy as sa
def upgrade():
op.create_table(
“text_data”,
sa.Column(“id”, sa.Integer, primary_key=True),
sa.Column(“url”, sa.String),
sa.Column(“text”, sa.Text)
)
def downgrade():
op.drop_table(“text_data”)
“`
This migration file creates a new table called `text_data` with the same columns as the `TextData` model.
Creating a Stop Words List and Using NLTK Stopwords Corpus
Stop words are common words such as “the”, “and”, and “a” that typically do not carry any significant meaning in text analysis. Removing stop words is often necessary to get more accurate results.
To create a stop words list, you can manually define the words you want to remove. Here’s an example:
“`
stop_words = [“the”, “and”, “a”]
filtered_words = [word for word in words if word.lower() not in stop_words]
“`
In this example, we’re creating a list of stop words and using a list comprehension to filter out any words that appear in the stop words list.
However, a more efficient way to remove stop words is to use the NLTK stopwords corpus. Here’s an example:
“`
from nltk.corpus import stopwords
stop_words = set(stopwords.words(“english”))
filtered_words = [word for word in words if word.lower() not in stop_words]
“`
In this example, we’re loading the stopwords corpus from NLTK and creating a set of stop words.
We’re then using a list comprehension to filter out any words that appear in the stop words set.
Sorting Dictionary to Display Words with Highest Count
Once you’ve counted the frequency of each word, you may want to sort the dictionary to display the words with the highest count. Here’s an example:
“`
sorted_counts = sorted(word_counts.items(), key=lambda x: x[1], reverse=True)
“`
In this example, we’re using Python’s built-in `sorted()` function to sort the dictionary in descending order based on the second value (i.e., the count).
Handling Large Amounts of Traffic Using a Task Queue
Web scraping applications can often receive a large amount of traffic, which can slow down the application and lead to delayed responses. To handle large amounts of traffic, a task queue can be implemented.
A task queue is a system that allows tasks to be executed asynchronously and in parallel. One popular task queue for Python is Redis.
To use Redis as a task queue, you’ll need to install `redis` and `rq` by typing in the following commands:
“`
pip install redis
pip install rq
“`
You’ll then need to define a task in a separate file. Here’s an example:
“`
from rq import get_current_job
from time import sleep
def process_text_data(text_data):
job = get_current_job()
print(“Starting job:”, job.id)
sleep(3) # simulate task processing
print(“Completed job:”, job.id)
“`
In this example, we’re defining a `process_text_data()` function that takes a `text_data` argument. The `get_current_job()` function is used to retrieve the current job object, which can be used to monitor the progress of the task.
The `sleep()` function is used to simulate task processing. Front-End Development: Implementing Redis Task Queue and Creating Custom Angular Directive
The front-end of a web scraping application is often where users spend the majority of their time.
It’s important to design a user-friendly interface that’s easy to navigate. Here, we’ll explore how to implement a Redis task queue using Angular and how to create a custom Angular directive for displaying a frequency distribution chart.
Implementing a Redis Task Queue Using Angular
To use Redis as a task queue with Angular, you’ll need to install the `angular-rq` module by typing in the following command:
“`
bower install angular-rq
“`
You’ll then need to define a task queue service in your Angular application. Here’s an example:
“`
angular.module(“myApp”).factory(“TaskQueueService”, [“Rq”, function(Rq) {
var queue = Rq(“textDataQueue”);
function processTextData(textData) {
var job = queue.createJob(“process_text_data”, {
textData: textData
});
return job.save();
}
return {
processTextData: processTextData
}
}]);
“`
In this example, we’re creating a new task queue service called `TaskQueueService`.
The `processTextData()` function is defined to accept a `textData` argument. A new job is then created using the `createJob()` function and saved using the `job.save()` function.
Creating a Custom Angular Directive for Displaying a Frequency Distribution Chart
Angular directives allow you to extend HTML syntax and create reusable components. To create a custom Angular directive for displaying a frequency distribution chart, you’ll need to define a new module and directive.
Here’s an example:
“`
angular.module(“myApp”).directive(“frequencyDistributionChart”, function() {
return {
restrict: “E”,
scope: {
data: “=”
},
link: function(scope, element, attrs) {
var chart = new Chart(element[0], {
type: “bar”,
data: {
labels: scope.data.labels,
datasets: [{
label: “Frequency”,
data: scope.data.counts,
backgroundColor: “rgba(0, 0, 255, 0.5)”
}]
},
options: {
scales: {
yAxes: