Make sure tags aren't rendered inside a guide (#5007)

* remove tags

* small fixes to guides

* add changeset

* add changeset

* add changeset

* add changeset

---------

Co-authored-by: gradio-pr-bot <gradio-pr-bot@users.noreply.github.com>

.changeset/ninety-papayas-pick.md

@@ -0,0 +1,5 @@
+---
+"website": patch
+---
+
+fix:Make sure tags aren't rendered inside a guide 

guides/06_tabular-data-science-and-plots/01_connecting-to-a-database.md

@@ -1,6 +1,6 @@
 # Connecting to a Database
 
-Related spaces: https://huggingface.co/spaces/gradio/chicago-bike-share-dashboard
+Related spaces: https://huggingface.co/spaces/gradio/chicago-bikeshare-dashboard
 Tags: TABULAR, PLOTS 
 
 ## Introduction
@@ -20,7 +20,7 @@ Our goal is to create a dashboard that will enable our business stakeholders to
 
 At the end of this guide, we will have a functioning application that looks like this:
 
-<gradio-app space="gradio/chicago-bike-share-dashboard"> </gradio-app>
+<gradio-app space="gradio/chicago-bikeshare-dashboard"> </gradio-app>
 
 
 ## Step 1 - Creating your database
@@ -146,7 +146,7 @@ You will have to add the `DB_USER`, `DB_PASSWORD`, and `DB_HOST` variables as "R
 ## Conclusion
 Congratulations! You know how to connect your gradio app to a database hosted on the cloud! ☁️
 
-Our dashboard is now running on [Spaces](https://huggingface.co/spaces/gradio/chicago-bike-share-dashboard).
-The complete code is [here](https://huggingface.co/spaces/gradio/chicago-bike-share-dashboard/blob/main/app.py)
+Our dashboard is now running on [Spaces](https://huggingface.co/spaces/gradio/chicago-bikeshare-dashboard).
+The complete code is [here](https://huggingface.co/spaces/gradio/chicago-bikeshare-dashboard/blob/main/app.py)
  
 As you can see, gradio gives you the power to connect to your data wherever it lives and display however you want! 🔥

guides/06_tabular-data-science-and-plots/plot-component-for-maps.md

@@ -1,6 +1,5 @@
 # How to Use the Plot Component for Maps
 
-Related spaces:
 Tags: PLOTS, MAPS
 
 ## Introduction

guides/06_tabular-data-science-and-plots/using-gradio-for-tabular-workflows.md

@@ -1,4 +1,4 @@
-## Using Gradio for Tabular Data Science Workflows
+# Using Gradio for Tabular Data Science Workflows
 
 Related spaces: https://huggingface.co/spaces/scikit-learn/gradio-skops-integration, https://huggingface.co/spaces/scikit-learn/tabular-playground, https://huggingface.co/spaces/merve/gradio-analysis-dashboard
 
@@ -7,7 +7,7 @@ Related spaces: https://huggingface.co/spaces/scikit-learn/gradio-skops-integrat
 
 Tabular data science is the most widely used domain of machine learning, with problems ranging from customer segmentation to churn prediction. Throughout various stages of the tabular data science workflow, communicating your work to stakeholders or clients can be cumbersome; which prevents data scientists from focusing on what matters, such as data analysis and model building. Data scientists can end up spending hours building a dashboard that takes in dataframe and returning plots, or returning a prediction or plot of clusters in a dataset. In this guide, we'll go through how to use `gradio` to improve your data science workflows. We will also talk about how to use `gradio` and [skops](https://skops.readthedocs.io/en/stable/) to build interfaces with only one line of code!
 
-### Prerequisites
+### Prerequisites
 
 Make sure you have the `gradio` Python package already [installed](/getting_started).
 

js/_website/generate_jsons/src/guides/__init__.py

@@ -59,11 +59,13 @@ for guide_folder in guide_folders:
         metadata_labels = []
 
         def get_labeled_metadata(label, is_list=True):
+            global guide_content
             metadata_labels.append(label)
             full_label = label + " "
             metadata = [] if is_list else None
             if full_label in guide_content:
                 metadata = guide_content.split(full_label)[1].split("\n")[0]
+                guide_content = guide_content.replace(full_label + metadata, "")
                 if is_list:
                     metadata = metadata.split(", ")
             return metadata