From 1797c0aac9637da609db053d56ee46cccf955033 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 16 Jan 2025 10:38:49 +0000 Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- vizro-ai/docs/pages/tutorials/project-tutorial.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md index 6ee524a47..7afc62c0b 100644 --- a/vizro-ai/docs/pages/tutorials/project-tutorial.md +++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md @@ -4,9 +4,9 @@ This tutorial uses Vizro-AI to build a prototype dashboard with three charts tha ## Project explanation -The dataset for this project was a set of books data [exported from a personal Goodreads account](https://www.goodreads.com/review/import), which is provided with the project so you can run it. The dataset used can be downloaded from the [Vizro repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv). If you use Goodreads, you can export your own data in CSV format, substitute it for the dataset provided, and explore it with the code for this project. +The dataset for this project was a set of books data [exported from a personal Goodreads account](https://www.goodreads.com/review/import), which is provided with the project so you can run it. The dataset used can be downloaded from the [Vizro repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv). \If you use Goodreads, you can export your own data in CSV format, substitute it for the dataset provided, and explore it with the code for this project. -The dataset was filtered to retain only books with an ISBN, which can be used with [Google Books API](https://developers.google.com/books) to retrieve additional data about a book. The Books API wasn't used in this project, but by including ISBN data, there is scope to extend the prototype project in future. +The dataset was filtered to retain only books with an ISBN, which can be used with [Google Books API](https://developers.google.com/books) to retrieve additional data about a book. The Books API wasn't used in this project, but by including ISBN data, there is scope to extend the prototype project in future. ## OpenAI @@ -132,7 +132,7 @@ The plot this code returns looks as follows: ![](../../assets/tutorials/project/ ### Chart 3: Reviews comparison -The third chart should illustrate the difference between the rating the Goodreads reader assigned a book and the average rating across the Goodreads community. This prompt took a degree of iteration and needed us to specify how to draw the lines between the points, which is a key learning when using generative AI: your results will vary from run to run. The type of chart ("dumbbell") was given explicitly to guide the chart creation. +The third chart should illustrate the difference between the rating the Goodreads reader assigned a book and the average rating across the Goodreads community. This prompt took a degree of iteration and needed us to specify how to draw the lines between the points, which is a key learning when using generative AI: your results will vary from run to run. The type of chart ("dumbbell") was given explicitly to guide the chart creation. ``` For each row, create a dumbbell chart to show the difference @@ -279,7 +279,7 @@ There are three changes to the Notebook code needed for it to run on PyCafe: 1. Add `from vizro import Vizro` to the imports list 1. Add `Vizro().build(model).run()` at the end of the code block 1. Uncomment the data manager code and replace it with code needed to access the dataset: - - either download the dataset from the [Vizro GitHub repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv) and upload it to the PyCafe project. + - either download the dataset from the [Vizro GitHub repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv)\ and upload it to the PyCafe project. - or use the code added to the snippet below to read the dataset directly from online storage. Follow the link at the bottom of the code snippet titled **☕️ Run and edit this code in PyCafe** to use and edit the dashboard.