Plutonic Rainbows

Today, I embarked on an exciting journey into natural language processing by creating a syntax tree application using Python’s NLTK library. The project started by demonstrating the significance of syntax in NLP, where I built a simple Python script that tokenizes input sentences, performs POS tagging, and uses a regular-expression-based grammar to generate syntax trees. This approach provided a clear, structured visualisation of how sentences are parsed, setting a strong foundation for the project. With the core NLP functionality in place, I integrated a web interface using Flask, allowing users to enter sentences and see their parsed syntax trees in real time. To elevate the user experience, I turned to D3.js to create an interactive, horizontally oriented SVG visualisation. This step involved dynamically sizing the SVG based on the tree’s layout and adding zoom and pan features so that users could easily navigate through complex trees. The result was a sleek, responsive display that brought the underlying language structure to life.

To add a final touch of interactivity and polish, I implemented smooth hover animations and tooltips for each node in the tree. When a user hovers over a node, its circle gently enlarges while a tooltip appears, providing detailed information about the node. Throughout this process, I ensured that all text elements maintained a clean and modern appearance. This project not only deepened my understanding of NLP and web visualisation but also showcased how to seamlessly merge backend processing with interactive front-end design.

After reading a few positive reviews, I decided to purchase this product. It is available exclusively in a 30 ml bottle, and the quality is outstanding. It makes an immediate impact and wonderfully evokes the timeless charm of vintage Polo Ralph Lauren.

It opens with a sharp burst of aromatic herbs and mint that immediately grabs attention, before evolving into a complex heart where animalic and spicy nuances — most notably a potent castoreum — intermingle with earthy, resinous notes.

Despite its bold and polarising opening, the dry down reveals a surprisingly balanced composition, merging warm, woody elements with subtle sweetness that endures impressively over time. Ultimately, Unutamam is celebrated for its transformative character and lasting impact, making it a fragrance best suited for those willing to embrace its unconventional and provocative nature.

I have updated the app to now calculate a weighted score (using a Bayesian average–style formula) for each prompt and display the results on the admin dashboard. This version of the app continues to provide personalised prompt generation, user authentication, rating submission (on a 1-to-10 scale with an optional comment), and an admin dashboard that shows aggregated data (with the top three results highlighted in the table).

Today, I integrated several new features into my prompt evaluation app to enhance its functionality and data-driven personalisation. I expanded the rating scale from a simple binary choice to a 1-to-10 scale, allowing for more nuanced feedback from users. I also updated the prompt generation process to keep prompt text and seed words separate, ensuring that the descriptive content is richer and more varied. Additionally, I incorporated a trained machine learning model into the prompt selection function — this model weights candidate one-liner prompts based on historical user ratings, and it gracefully falls back to random selection if the model isn’t available.

I further enhanced the admin dashboard to provide a clear visual representation of prompt performance. Using Chart.js, I set up a bar chart that displays the average rating and count for each prompt description, and I implemented a feature to highlight the top three performing prompts with distinct colours. These updates, along with maintaining user authentication and basic administrative routes, have made my application more sophisticated and have paved the way for future improvements in personalisation and data analysis.

Later in the day, I added a comments section for users — this will also be saved to the SQL database.