Boost Your Website's SEO with this Python and Streamlit Web App

Boost Your Website's SEO with this Python and Streamlit Web App

Table of Contents

1. Introduction
2. Setting up the SEO Analyzer Web App
3. Analyzing the SEO of a Website
   • 3.1. Retrieving the Keywords
   • 3.2. Extracting the Bigrams
   • 3.3. Analyzing the Frequency of Bigrams
4. Building the Streamlit Web App
   • 4.1. Creating Tabs
   • 4.2. Displaying the Keywords
   • 4.3. Displaying the Bigrams
   • 4.4. Displaying the Good News
   • 4.5. Displaying the Bad News
5. Conclusion

Introduction

In this tutorial, we will be creating an SEO analyzer web app using Streamlit. If you are new to analyzing the SEO of a website using Python, I recommend watching my previous tutorials on this topic. The source code for this project can be found on my website, pythonology.eu.

Setting up the SEO Analyzer Web App

To get started, we need to install the necessary packages. Open your terminal and run the following commands:

• pip install requests
• pip install beautifulsoup4
• pip install nltk

Next, we need to import the required modules in our code. We will be using the streamlit library for building the web app, so make sure you have it installed. You can import it using the following code:

import streamlit as st

Analyzing the SEO of a Website

3.1. Retrieving the Keywords

In order to analyze the SEO of a website, we first need to retrieve the keywords from the web page. We can do this by making a request to the URL and extracting the relevant information.

First, let's define a function called SEO_analysis which takes the URL as a parameter. Inside this function, we will retrieve the keywords using the following steps:

1. Make a request to the URL using the requests library.
2. Check if the status code of the response is 200 (indicating a successful request).
3. If the status code is not 200, display an error message on the web page.
4. If the status code is 200, extract the keywords from the web page.

import requests

def SEO_analysis(URL: str):
    response = requests.get(URL)

    if response.status_code != 200:
        st.error("Error: Unable to access the website.")
        return

    # Extract the keywords from the web page
    # Your code here

3.2. Extracting the Bigrams

Once we have retrieved the keywords, we need to extract the bigrams from the text. Bigrams are pairs of words that occur together, and they can provide valuable insights for SEO analysis. We will use the nltk library to tokenize the text and extract the bigrams.

To extract the bigrams, we need to:

1. Tokenize the text using the nltk library.
2. Use the ngrams function to generate the bigrams.
3. Save the bigrams in a variable for further analysis.

from nltk.util import ngrams

# Tokenize the text
# Your code here

# Generate the bigrams
# Your code here

# Save the bigrams in a variable
# Your code here

3.3. Analyzing the Frequency of Bigrams

Now that we have the bigrams, we can analyze their frequency to see which ones occur more often. This information can help us optimize the SEO of the website.

To analyze the frequency of the bigrams, we will:

1. Count the frequency of each bigram using a dictionary.
2. Sort the bigrams based on their frequency.
3. Select the top 10 most frequent bigrams for display.

# Count the frequency of each bigram
# Your code here

# Sort the bigrams based on their frequency
# Your code here

# Select the top 10 most frequent bigrams
# Your code here

Building the Streamlit Web App

4.1. Creating Tabs

To create a user-friendly interface, we will organize the information into different tabs using the streamlit library. This will allow users to navigate easily through the different sections of the SEO analysis.

To create tabs, we use the st.tabs function and define the headings for each tab. For example:

tabs = st.tabs(["Keywords", "Bigrams", "Good News", "Bad News"])

4.2. Displaying the Keywords

In the first tab, we will display the keywords retrieved from the web page. To do this, we can use the st.write function to display each keyword on a separate line.

with tabs[0]:
    st.write("Keywords:")
    for keyword in keywords:
        st.write(keyword)

4.3. Displaying the Bigrams

In the second tab, we will display the bigrams along with their frequency. Similar to displaying the keywords, we can use the st.write function to display each bigram and its frequency.

with tabs[1]:
    st.write("Bigrams Frequency:")
    for bigram, frequency in bigrams_freq.iteritems():
        st.write(f"{bigram}: {frequency}")

4.4. Displaying the Good News

In the third tab, we will display the positive aspects of the website's SEO. This could include elements such as a well-structured title, meta description, and headings. To enhance the visual representation, we can use the st.success function to highlight the positive aspects.

with tabs[2]:
    st.success("The title exists.")
    st.success("The meta description is present.")
    st.success("All headings are present.")

4.5. Displaying the Bad News

In the fourth tab, we will display any potential issues or warnings related to the website's SEO. This could include missing ALT attributes for images, missing meta description, and missing title tags. To highlight the issues, we can use the st.error function.

with tabs[3]:
    st.error("No ALT attribute for some images.")
    st.error("Missing meta description.")
    st.error("Missing title tags.")

Conclusion

In this tutorial, we have learned how to create an SEO analyzer web app using Streamlit. We covered the process of retrieving keywords from a web page, extracting bigrams, and analyzing their frequency. With the Streamlit library, we created a user-friendly interface with tabs for easy navigation. This web app can help website owners analyze and optimize their SEO for better search engine ranking.