This simple Streamlit app is designed to make it easy to find topics which require additional citations. [citation needed].
Simply enter a keyword to search Wikipedia for articles which require additional citations.
This app is geared more towards sites which are an authority in their industry that can leverage existing content, or create new content which can be used to improve Wikipedia.
The Python script uses the Google Vision API to identify and fetch matching high-resolution images from an Image export from a Screaming Frog crawl to source higher quality images on autopilot!
This is aimed primarily at medium / large eCommerce sites that source standardised images from product suppliers.
The output file shows the new size pixel difference vs the original file making the implementation easy to prioritise. It’s simple, robust and actionable.
While working on a Streamlit project, I recognized the need for a straightforward method to integrate Google Search Console (GSC) data using OAuth.
I initially started to refactor the original GSC connector by Charly Wargnier (AKA @datachaz), but realised it would be less work (and more fun) to write something from scratch.
I aimed for an extremely barebones, lightweight, and maintainable solution that could be seamlessly integrated into other Streamlit projects, enabling easy access to Search Console data.
For that reason, I’m releasing the source code and a GSC Streamlit app to kick start development.
eCommerce businesses often host a large amount of PDF files to accompany products and ranges. While these files can rank well on search engines and attract users, they often lead visitors away from the main website. This situation can result in potential customers finding what they need in the PDF and, sometimes, making their purchases elsewhere.
Noticing this, I saw a practical opportunity to make these PDFs work harder.
The idea is simple: embed a branded header in each PDF. This header would include a phone number and website address, subtly encouraging users to engage directly with your business.
For details on how to implement this, including features, settings, and step-by-step instructions, keep reading. Or, if you’re ready to start right away, go ahead and download the script.
The first version of this app was recently highlighted in this excellent article, ‘Mastering Topic Clusters in SEO.‘ (If you haven’t yet, it’s a must-read!)
This got me thinking it’s high time for an overhaul of the original app. Checking in, I’m amazed it had over 17,000 unique visitors since its release in May 2022!
I’ve done absolutely nothing to market it other than the initial tweet and being featured as Streamlit’s app of the month in May 2022.
This success led me to realise that it’s high time the app received an overhaul.
This Streamlit app automates the matching of Screaming Frog crawl files from a live site with those from a staging site, finding the best matching URL between the two.
My approach differs by matching on multiple columns simultaneously and then returning the URL in the destination crawl file with the highest match score.
You can rerun the app with different column combinations or matching models to observe changes in the similarity score distribution, ensuring the optimal setup.
For the tech-savvy, the full source code is also available. Feel free to experiment with Sentence Transformers, support for your favourite crawler, and more. The modular design makes it straightforward.
Command Line Semantic Keyword Clustering Tool.
An easy to use tool for semantic keyword clustering in Python. Easily manage extensive keyword lists with this easy to use command line interface tool.
This tool exports easy-to-understand visualisations, (great for reports and analysis), a convenient pivot table and appends itself to your original data. Common use cases include interlinking pages (cluster on the h1 tag), consolidating internal links, creating negative keyword reports, Search Console analysis, content creation and more.
