From 522793176d466064c47842870264c43c4a4d8e04 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Wed, 15 Jan 2025 10:01:15 +0000
Subject: [PATCH 01/44] First chunk

---
 .../docs/assets/tutorials/project/chart1.png  | Bin 0 -> 151532 bytes
 .../docs/assets/tutorials/project/chart2.png  | Bin 0 -> 76354 bytes
 .../docs/assets/tutorials/project/chart3.png  | Bin 0 -> 124572 bytes
 .../user-interface-hosted-vizro-ai.png        | Bin 0 -> 85745 bytes
 ...ser-interface-settings-hosted-vizro-ai.png | Bin 0 -> 57954 bytes
 .../docs/pages/tutorials/project-tutorial.md  | 161 ++++++++++++++++++
 6 files changed, 161 insertions(+)
 create mode 100644 vizro-ai/docs/assets/tutorials/project/chart1.png
 create mode 100644 vizro-ai/docs/assets/tutorials/project/chart2.png
 create mode 100644 vizro-ai/docs/assets/tutorials/project/chart3.png
 create mode 100644 vizro-ai/docs/assets/tutorials/project/user-interface-hosted-vizro-ai.png
 create mode 100644 vizro-ai/docs/assets/tutorials/project/user-interface-settings-hosted-vizro-ai.png
 create mode 100644 vizro-ai/docs/pages/tutorials/project-tutorial.md

diff --git a/vizro-ai/docs/assets/tutorials/project/chart1.png b/vizro-ai/docs/assets/tutorials/project/chart1.png
new file mode 100644
index 0000000000000000000000000000000000000000..8878ba7f4701164daf9312ce8f48165865f4107d
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diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
new file mode 100644
index 000000000..df076e090
--- /dev/null
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -0,0 +1,161 @@
+# How to build a prototype dashboard with Vizro-AI
+
+This tutorial uses Vizro-AI to build a prototype dashboard with three charts that illustrate a simple dataset. We first show how to create individual charts with Vizro-AI and then move on to learn how to use Vizro-AI to build a dashboard. The tutorial concludes by moving the prototype code generated by Vizro-AI into a project on PyCafe, for others to use and extend.
+
+## Project background and data
+The dataset for this project was a set of books data [exported from a personal Goodreads account](https://www.goodreads.com/review/import). If you use Goodreads, you can export your own data in CSV format and use it with the code for this project.
+
+The dataset was filtered to retain only books with an ISBN, since that 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 used can be downloaded from the [Vizro repository](filtered_books.csv).
+
+## OpenAI
+This tutorial uses OpenAI with Vizro-AI. To run through the steps, you must have an account with paid-for credits available. None of the free accounts will suffice. [Check the OpenAI models and pricing on their website](https://platform.openai.com/docs/models). 
+
+!!! note
+    
+    Before using a model, please review OpenAI's guidelines on risk mitigation to understand potential model limitations and best practices. [See the OpenAI site for more details on responsible usage](https://platform.openai.com/docs/guides/safety-best-practices).
+
+
+## Individual chart generation with Vizro-AI
+In this step, we'll show a UI on a hosted version of Vizro-AI, at [https://py.cafe/app/vizro-official/vizro-ai-charts](https://py.cafe/app/vizro-official/vizro-ai-charts). Use your browser to navigate to the site which looks as follows:
+
+![](../../assets/tutorials/project/user-interface-hosted-vizro-ai.png)
+
+### Settings
+You'll notice a cog icon at the top right hand corner for access to your settings, which look as follows:
+
+![](../../assets/tutorials/project/user-interface-settings-hosted-vizro-ai.png)
+
+Add the API key for your chosen vendor. At the time of writing, you can use OpenAI, Anthropic, Mistral, or xAI. 
+
+Once the API key is set, return to the main screen and upload the data for the project. 
+
+We can now dive use Vizro-AI to build some charts by iterating text to write effective prompts.
+
+### Chart 1: Books timeline
+To ask Vizro-AI to build a chart, describe what you want to see. This chart should show an ordered horiontal timeline to illustrate the sequence of reading the books.
+
+> Plot a chart with the title "Sequence of reading" . It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
+
+You can adjust the model used: the chart below was generated from `gpt-4o-mini`. The chart displays on the right hand side of the screen and the Plotly code to generate the chart is below the prompt.
+
+The plot this code returns looks as follows:
+![](../../assets/tutorials/project/chart1.png)
+
+### Chart 2: Reading velocity
+The second chart should plot the cumulative total of pages read and the cumulative total of books read per year. The prompt took a few iterations to refine:
+
+> Plot a chart with the title "Pages and Book totals" . It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data. Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.
+
+> Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side. 
+
+The Plotly code generated when run with `gpt-4-turbo` was as follows:
+
+```python
+import pandas as pd
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+from vizro.models.types import capture
+
+
+@capture("graph")
+def custom_chart(data_frame):
+    # Convert Date Read to datetime
+    data_frame["Date Read"] = pd.to_datetime(data_frame["Date Read"], dayfirst=True)
+
+    # Group by year and sum pages
+    pages_per_year = (
+        data_frame.groupby(data_frame["Date Read"].dt.year)["Number of Pages"].sum().cumsum()
+    )
+
+    # Count books per year
+    books_per_year = data_frame.groupby(data_frame["Date Read"].dt.year).size()
+
+    # Create subplot
+    fig = make_subplots(specs=[[{"secondary_y": True}]])
+
+    # Add line for cumulative pages
+    fig.add_trace(
+        go.Scatter(
+            x=pages_per_year.index,
+            y=pages_per_year,
+            mode="lines",
+            name="Cumulative Pages",
+        ),
+        secondary_y=False,
+    )
+
+    # Add bar for books count
+    fig.add_trace(
+        go.Bar(x=books_per_year.index, y=books_per_year, name="Total Books"),
+        secondary_y=True,
+    )
+
+    # Set y-axes titles
+    fig.update_yaxes(title_text="Cumulative Pages", secondary_y=False)
+    fig.update_yaxes(title_text="Total Books", secondary_y=True)
+
+    # Set layout
+    fig.update_layout(title="Pages and Book totals", xaxis_title="Year", showlegend=True)
+
+    return fig
+
+```
+
+The plot this code returns looks as follows:
+![](../../assets/tutorials/project/chart2.png)
+
+### 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. It was run several times before it colored each line differently, which is a key learning when using generative AI: your results will vary from run to run.
+
+> For each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book - use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.
+
+
+```python
+import plotly.graph_objects as go
+from vizro.models.types import capture
+
+
+@capture("graph")
+def custom_chart(data_frame):
+    # Filter out rows where 'My Rating' is 0
+    df_filtered = data_frame[data_frame["My Rating"] != 0]
+
+    # Create a blank figure
+    fig = go.Figure()
+
+    # Add dumbbell lines and markers for each book
+    for index, row in df_filtered.iterrows():
+        fig.add_trace(
+            go.Scatter(
+                x=[row["My Rating"], row["Average Rating"]],
+                y=[index, index],
+                mode="markers+lines",
+                marker=dict(size=10),
+                line=dict(width=2),
+                name=row["Title"],
+                showlegend=False,
+            )
+        )
+
+    # Update layout
+    fig.update_layout(
+        title="Comparison of My Rating vs Average Rating",
+        xaxis_title="Rating",
+        yaxis_title="Books",
+        yaxis=dict(
+            tickmode="array",
+            tickvals=list(df_filtered.index),
+            ticktext=df_filtered["Title"],
+        ),
+    )
+    return fig
+```
+
+The plot this code returns looks as follows:
+![](../../assets/tutorials/project/chart3.png)
+
+
+## Dashboard generation with Vizro-AI
+
+## Interactive Vizro dashboards on PyCafe
+ 
\ No newline at end of file

From ddba624be2828193b07f23200ddf126177efd0a4 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Wed, 15 Jan 2025 11:15:03 +0000
Subject: [PATCH 02/44] Second chunk: add notebook

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 .../docs/pages/tutorials/project-tutorial.md  |  82 +++++++++++++++++-
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diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index df076e090..0d920ef7b 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -38,7 +38,7 @@ To ask Vizro-AI to build a chart, describe what you want to see. This chart shou
 
 You can adjust the model used: the chart below was generated from `gpt-4o-mini`. The chart displays on the right hand side of the screen and the Plotly code to generate the chart is below the prompt.
 
-The plot this code returns looks as follows:
+The plot this code returns looks as follows. Hovering over each point gave the date the book was read, but not the title of the book. You'll also notice that the points are spaced evenly rather than proportionately:
 ![](../../assets/tutorials/project/chart1.png)
 
 ### Chart 2: Reading velocity
@@ -109,7 +109,6 @@ The third chart should illustrate the difference between the rating the Goodread
 
 > For each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book - use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.
 
-
 ```python
 import plotly.graph_objects as go
 from vizro.models.types import capture
@@ -157,5 +156,82 @@ The plot this code returns looks as follows:
 
 ## Dashboard generation with Vizro-AI
 
+### Set up a Jupyter Notebook in which to use Vizro-AI
+At this point, we have prototypes for three plotly charts for the Goodreads data. To display these as an interactive dashboard, we need some additional code and Vizro-AI can generate this for us, but not through the PyCafe host at the time of writing. We'll use a Jupyter Notebook
+
+Before running the Notebook code, you need to [set up Vizro-AI](https://vizro.readthedocs.io/projects/vizro-ai/en/latest/pages/user-guides/install/) inside a virtual environment with Python 3.10 or later. Install the package with `pip install vizro_ai`.
+
+You need to give Vizro-AI your API key to access OpenAI by adding it to your environment so the code you write in the next step can access it to successfully call OpenAI. There are some [straightforward instructions in the OpenAI docs](https://platform.openai.com/docs/quickstart/step-2-set-up-your-api-key), and the process is also covered in the our [LLM setup guide](https://vizro.readthedocs.io/projects/vizro-ai/en/latest/pages/user-guides/install/#set-up-access-to-a-large-language-model).
+
+### Build a dashboard
+At this point you can open a Jupyter Notebook to make the dashboard. We'll submit a prompt that combines the three prompts listed above, with some small edits to ask for a dashboard that has three pages: one for each chart.
+
+The following code shows the code to make the request to Vizro-AI to build and display the dashboard.  The Notebook is available for download on [Vizro's GitHub repository](TO DO):
+
+```python
+user_question = """
+Create a dashboard with 3 pages, one for each chart. 
+
+On the first page, plot a chart with the title "Sequence of reading" . 
+It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
+
+On the second page, lot a chart with the title "Pages and Book totals" . 
+It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data. 
+Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.
+Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. 
+Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side. 
+
+On the third page, for each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book.
+Use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.
+"""
+
+result = vizro_ai.dashboard([df_cleaned], user_question, return_elements=True)
+Vizro().build(result.dashboard).run(port=8006)
+print(result.code)
+```
+
+Using `gpt-4-turbo`, Vizro-AI generates a set of plotly chart code and the necessary Vizro support code to build a dashboard. The generated code is displayed as output in the Notebook with the dashboard, although the dashboard is better viewed at `http://localhost:8006/`.
+
+You can see the code output in the Notebook stored on the Vizro GitHub repository, or generate similar output by running it yourself.
+
+The charts generated were similar to those created by the PyCafe host above, although the first chart was improved by spacing the books proportionate to the date read and rolling over each point gave the book title as well as the date read.
+
+![](../../assets/tutorials/project/chart1v2.png)
+
+### Dashboard interactivity
+To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the code for a control. As a simple example, we can extend the prompt to ask for a filter to modify the time period displayed. 
+
+```diff
+user_question = """
+Create a dashboard with 3 pages, one for each chart. 
+
+On the first page, plot a chart with the title "Sequence of reading" . 
+It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
+-
++ Add a filter so the user can change the x axis to adjust the range of dates by year on the x axis, using a slider to range from Jan 2023 to Dec 2024.
+
+On the second page, plot a chart with the title "Pages and Book totals" . 
+It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data. 
+Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.
+Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. 
+
+Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side. 
+-
++ Add a filter so the user can change the x axis to adjust the range of dates by year on the x axis, using a slider to range from Jan 2023 to Dec 2024.
+
+On the third page, for each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book.
+Use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.
+"""
+
+```
+
 ## Interactive Vizro dashboards on PyCafe
- 
\ No newline at end of file
+At this point, we have a Notebook with code to call Vizro-AI to build a prototype Vizro dashboard with a set of three pages and three charts, plus a control to filter the view.
+
+As we've already seen, the code generated by Vizro-AI can vary from run to run, and calling OpenAI each time a dashboard is needed can get costly. 
+
+The project isn't particularly easy to share at present either: sharing a Notebook requires every user to have an OpenAI key and set up an environment.
+
+At this point, to share and iterate the prototype the best course of action is to transfer the generated code from the output of Vizro-AI in the Notebook into a PyCafe project.
+
+

From cc85686c7d4795520d0f4df812a166c342db604e Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Wed, 15 Jan 2025 12:19:09 +0000
Subject: [PATCH 03/44] Update settings to add pycafe to Vizro-AI docs

---
 vizro-ai/hatch.toml |  3 ++-
 vizro-ai/mkdocs.yml | 15 ++++++++++++++-
 2 files changed, 16 insertions(+), 2 deletions(-)

diff --git a/vizro-ai/hatch.toml b/vizro-ai/hatch.toml
index 384d15d57..770cd26f7 100644
--- a/vizro-ai/hatch.toml
+++ b/vizro-ai/hatch.toml
@@ -63,7 +63,8 @@ dependencies = [
   "mkdocs-material",
   "mkdocs-git-revision-date-localized-plugin>=1.2.5",
   "mkdocstrings[python]",
-  "linkchecker"
+  "linkchecker",
+  "mkdocs-pycafe"
 ]
 detached = true
 installer = "uv"
diff --git a/vizro-ai/mkdocs.yml b/vizro-ai/mkdocs.yml
index 9bd285a2a..7a2064ee1 100644
--- a/vizro-ai/mkdocs.yml
+++ b/vizro-ai/mkdocs.yml
@@ -5,6 +5,7 @@ nav:
   - Tutorials:
       - Chart generation: pages/tutorials/quickstart.md
       - Dashboard generation: pages/tutorials/quickstart-dashboard.md
+      - Goodreads tutorial: pages/tutorials/project-tutorial.md
   - How-to guides:
       - FUNDAMENTALS:
           - Install Vizro-AI: pages/user-guides/install.md
@@ -74,7 +75,19 @@ markdown_extensions:
       generic: true
   - footnotes
   - pymdownx.details
-  - pymdownx.superfences
+  - pymdownx.superfences:
+      custom_fences:
+        - name: mermaid
+          class: mermaid
+          format: !!python/name:pymdownx.superfences.fence_code_format
+        - name: python
+          class: "highlight"
+          validator: !!python/name:mkdocs_pycafe.validator
+          format: !!python/object/apply:mkdocs_pycafe.formatter
+            kwds:
+              type: vizro
+              requirements: |
+                vizro==0.1.30  
   - pymdownx.tabbed:
       alternate_style: true
   - pymdownx.mark

From 6a1402ce377cc3614bb558e8943f84cc0f12ad4d Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Wed, 15 Jan 2025 12:19:14 +0000
Subject: [PATCH 04/44] Update project-tutorial.md

---
 .../docs/pages/tutorials/project-tutorial.md  | 191 ++++++++++++++++++
 1 file changed, 191 insertions(+)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index 0d920ef7b..7b48501c1 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -234,4 +234,195 @@ The project isn't particularly easy to share at present either: sharing a Notebo
 
 At this point, to share and iterate the prototype the best course of action is to transfer the generated code from the output of Vizro-AI in the Notebook into a PyCafe project.
 
+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
+2. Add `Vizro().build(model).run()` at the end of the code block
+3. Uncomment the data manager code and replace it with code needed to access the dataset:
+
+        - If you are building your own PyCafe project, you can download the dataset from the Vizro GitHub repository and upload it to the PyCafe project.
+        - Alternatively, you can use the code specified below to read the dataset directly from online storage.
+
+	
+	
+!!! example "First dashboard"
+    === "app.py"
+        ```{.python pycafe-link}
+        from vizro import Vizro
+        import vizro.models as vm
+        from vizro.models.types import capture
+        import pandas as pd
+        import plotly.graph_objects as go
+        from vizro.models.types import capture
+		
+        ####### Function definitions ######
+        @capture("graph")
+        def sequence_reading(data_frame):
+            fig = go.Figure()
+            fig.add_trace(
+                go.Scatter(
+                    x=data_frame["Date Read"],
+                    y=[1] * len(data_frame),
+                    mode="markers",
+                    marker=dict(size=10, color="blue"),
+                )
+            )
+            fig.update_layout(
+                title="Sequence of reading",
+                xaxis_title="Date Read",
+                yaxis_title="Sequence",
+                yaxis=dict(showticklabels=False, showgrid=False),
+                xaxis=dict(tickangle=-45),
+            )
+            return fig
+
+
+        @capture("graph")
+        def pages_books_totals(data_frame):
+            # Prepare data
+            data_frame["Date Read"] = pd.to_datetime(data_frame["Date Read"])
+            data_frame.sort_values("Date Read", inplace=True)
+            data_frame["Cumulative Pages"] = data_frame["Number of Pages"].cumsum()
+
+            # Aggregate data by year for total books read
+            yearly_books = data_frame.groupby(data_frame["Date Read"].dt.year).size()
+
+            # Create figure with secondary y-axis
+            fig = go.Figure()
+
+            # Add line for cumulative pages
+            fig.add_trace(
+                go.Scatter(
+                    x=data_frame["Date Read"],
+                    y=data_frame["Cumulative Pages"],
+                    mode="lines",
+                    name="Cumulative Pages Read",
+                )
+            )
+
+            # Add bars for books read per year
+            fig.add_trace(
+                go.Bar(
+                    x=yearly_books.index, y=yearly_books, name="Books Read per Year", yaxis="y2"
+                )
+            )
+
+            # Set up the layout
+            fig.update_layout(
+                title="Cumulative Pages Read and Books Read per Year",
+                xaxis_title="Date",
+                yaxis=dict(title="Number of Pages"),
+                yaxis2=dict(title="Total Books", overlaying="y", side="right"),
+            )
+
+            return fig
+
+
+        @capture("graph")
+        def rating_comparison(data_frame):
+            # Filter out rows where 'My Rating' is 0
+            df = data_frame[data_frame["My Rating"] != 0]
+
+            # Create a figure
+            fig = go.Figure()
+
+            # Add dumbbell traces
+            for index, row in df.iterrows():
+                fig.add_trace(
+                    go.Scatter(
+                        x=[row["My Rating"], row["Average Rating"]],
+                        y=[row["Title"], row["Title"]],
+                        mode="markers+lines",
+                        name=row["Title"],
+                        marker=dict(size=10),
+                        line=dict(width=2),
+                    )
+                )
+
+            # Update layout
+            fig.update_layout(
+                title="Comparison of My Rating vs Average Rating",
+                xaxis_title="Rating",
+                yaxis_title="Book Title",
+                showlegend=False,
+            )
 
+            return fig
+
+        ########### Read data ############
+        # Define the URL of the raw CSV file
+        # TO DO -- This will change when data is uploaded to Vizro repo
+        import requests
+        url = "https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv"
+
+        # Fetch the CSV file
+        response = requests.get(url)
+        response.raise_for_status()  # Ensure we notice bad responses
+
+        # Load the CSV data into a pandas DataFrame
+        from io import StringIO
+        csv_data = StringIO(response.text)
+        df = pd.read_csv(csv_data)
+        df["Date Read"] = pd.to_datetime(df['Date Read'],dayfirst=True)
+
+
+        ########### Model code ############
+        model = vm.Dashboard(
+            pages=[
+                vm.Page(
+                    components=[
+                        vm.Graph(
+                            id="sequence_reading",
+                            figure=sequence_reading(df),
+                        )
+                    ],
+                    title="Sequence of Reading",
+                    layout=vm.Layout(grid=[[0]]),
+                    controls=[
+                        vm.Filter(
+                            column="Year Published",
+                            targets=["sequence_reading"],
+                            selector=vm.RangeSlider(type="range_slider"),
+                        )
+                    ],
+                ),
+                vm.Page(
+                    components=[
+                        vm.Graph(
+                            id="pages_books_totals",
+                            figure=pages_books_totals(df),
+                        )
+                    ],
+                    title="Pages and Book Totals",
+                    layout=vm.Layout(grid=[[0]]),
+                    controls=[
+                        vm.Filter(
+                            column="Year Published",
+                            targets=["pages_books_totals"],
+                            selector=vm.RangeSlider(type="range_slider"),
+                        )
+                    ],
+                ),
+                vm.Page(
+                    components=[
+                        vm.Graph(
+                            id="rating_comparison",
+                            figure=rating_comparison(df),
+                        )
+                    ],
+                    title="Rating Comparison",
+                    layout=vm.Layout(grid=[[0]]),
+                    controls=[],
+                ),
+            ],
+            title="Book Reading Analysis Dashboard",
+        )        		
+
+        Vizro().build(model).run()
+        ```
+
+ 
+ 
+ 
+ 
+ 

From d18ef29e53c20412eb0788b0dd2231b5bdd72655 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 15 Jan 2025 12:22:15 +0000
Subject: [PATCH 05/44] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 .../docs/pages/tutorials/project-tutorial.md  | 90 +++++++++----------
 vizro-ai/mkdocs.yml                           |  2 +-
 2 files changed, 45 insertions(+), 47 deletions(-)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index 7b48501c1..033488d8b 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -3,50 +3,53 @@
 This tutorial uses Vizro-AI to build a prototype dashboard with three charts that illustrate a simple dataset. We first show how to create individual charts with Vizro-AI and then move on to learn how to use Vizro-AI to build a dashboard. The tutorial concludes by moving the prototype code generated by Vizro-AI into a project on PyCafe, for others to use and extend.
 
 ## Project background and data
+
 The dataset for this project was a set of books data [exported from a personal Goodreads account](https://www.goodreads.com/review/import). If you use Goodreads, you can export your own data in CSV format and use it with the code for this project.
 
 The dataset was filtered to retain only books with an ISBN, since that 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 used can be downloaded from the [Vizro repository](filtered_books.csv).
 
 ## OpenAI
-This tutorial uses OpenAI with Vizro-AI. To run through the steps, you must have an account with paid-for credits available. None of the free accounts will suffice. [Check the OpenAI models and pricing on their website](https://platform.openai.com/docs/models). 
+
+This tutorial uses OpenAI with Vizro-AI. To run through the steps, you must have an account with paid-for credits available. None of the free accounts will suffice. [Check the OpenAI models and pricing on their website](https://platform.openai.com/docs/models).
 
 !!! note
-    
     Before using a model, please review OpenAI's guidelines on risk mitigation to understand potential model limitations and best practices. [See the OpenAI site for more details on responsible usage](https://platform.openai.com/docs/guides/safety-best-practices).
 
-
 ## Individual chart generation with Vizro-AI
+
 In this step, we'll show a UI on a hosted version of Vizro-AI, at [https://py.cafe/app/vizro-official/vizro-ai-charts](https://py.cafe/app/vizro-official/vizro-ai-charts). Use your browser to navigate to the site which looks as follows:
 
 ![](../../assets/tutorials/project/user-interface-hosted-vizro-ai.png)
 
 ### Settings
+
 You'll notice a cog icon at the top right hand corner for access to your settings, which look as follows:
 
 ![](../../assets/tutorials/project/user-interface-settings-hosted-vizro-ai.png)
 
-Add the API key for your chosen vendor. At the time of writing, you can use OpenAI, Anthropic, Mistral, or xAI. 
+Add the API key for your chosen vendor. At the time of writing, you can use OpenAI, Anthropic, Mistral, or xAI.
 
-Once the API key is set, return to the main screen and upload the data for the project. 
+Once the API key is set, return to the main screen and upload the data for the project.
 
 We can now dive use Vizro-AI to build some charts by iterating text to write effective prompts.
 
 ### Chart 1: Books timeline
+
 To ask Vizro-AI to build a chart, describe what you want to see. This chart should show an ordered horiontal timeline to illustrate the sequence of reading the books.
 
 > Plot a chart with the title "Sequence of reading" . It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
 
 You can adjust the model used: the chart below was generated from `gpt-4o-mini`. The chart displays on the right hand side of the screen and the Plotly code to generate the chart is below the prompt.
 
-The plot this code returns looks as follows. Hovering over each point gave the date the book was read, but not the title of the book. You'll also notice that the points are spaced evenly rather than proportionately:
-![](../../assets/tutorials/project/chart1.png)
+The plot this code returns looks as follows. Hovering over each point gave the date the book was read, but not the title of the book. You'll also notice that the points are spaced evenly rather than proportionately: ![](../../assets/tutorials/project/chart1.png)
 
 ### Chart 2: Reading velocity
+
 The second chart should plot the cumulative total of pages read and the cumulative total of books read per year. The prompt took a few iterations to refine:
 
 > Plot a chart with the title "Pages and Book totals" . It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data. Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.
 
-> Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side. 
+> Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side.
 
 The Plotly code generated when run with `gpt-4-turbo` was as follows:
 
@@ -63,9 +66,7 @@ def custom_chart(data_frame):
     data_frame["Date Read"] = pd.to_datetime(data_frame["Date Read"], dayfirst=True)
 
     # Group by year and sum pages
-    pages_per_year = (
-        data_frame.groupby(data_frame["Date Read"].dt.year)["Number of Pages"].sum().cumsum()
-    )
+    pages_per_year = data_frame.groupby(data_frame["Date Read"].dt.year)["Number of Pages"].sum().cumsum()
 
     # Count books per year
     books_per_year = data_frame.groupby(data_frame["Date Read"].dt.year).size()
@@ -98,13 +99,12 @@ def custom_chart(data_frame):
     fig.update_layout(title="Pages and Book totals", xaxis_title="Year", showlegend=True)
 
     return fig
-
 ```
 
-The plot this code returns looks as follows:
-![](../../assets/tutorials/project/chart2.png)
+The plot this code returns looks as follows: ![](../../assets/tutorials/project/chart2.png)
 
 ### 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. It was run several times before it colored each line differently, which is a key learning when using generative AI: your results will vary from run to run.
 
 > For each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book - use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.
@@ -150,13 +150,12 @@ def custom_chart(data_frame):
     return fig
 ```
 
-The plot this code returns looks as follows:
-![](../../assets/tutorials/project/chart3.png)
-
+The plot this code returns looks as follows: ![](../../assets/tutorials/project/chart3.png)
 
 ## Dashboard generation with Vizro-AI
 
 ### Set up a Jupyter Notebook in which to use Vizro-AI
+
 At this point, we have prototypes for three plotly charts for the Goodreads data. To display these as an interactive dashboard, we need some additional code and Vizro-AI can generate this for us, but not through the PyCafe host at the time of writing. We'll use a Jupyter Notebook
 
 Before running the Notebook code, you need to [set up Vizro-AI](https://vizro.readthedocs.io/projects/vizro-ai/en/latest/pages/user-guides/install/) inside a virtual environment with Python 3.10 or later. Install the package with `pip install vizro_ai`.
@@ -164,22 +163,23 @@ Before running the Notebook code, you need to [set up Vizro-AI](https://vizro.re
 You need to give Vizro-AI your API key to access OpenAI by adding it to your environment so the code you write in the next step can access it to successfully call OpenAI. There are some [straightforward instructions in the OpenAI docs](https://platform.openai.com/docs/quickstart/step-2-set-up-your-api-key), and the process is also covered in the our [LLM setup guide](https://vizro.readthedocs.io/projects/vizro-ai/en/latest/pages/user-guides/install/#set-up-access-to-a-large-language-model).
 
 ### Build a dashboard
+
 At this point you can open a Jupyter Notebook to make the dashboard. We'll submit a prompt that combines the three prompts listed above, with some small edits to ask for a dashboard that has three pages: one for each chart.
 
-The following code shows the code to make the request to Vizro-AI to build and display the dashboard.  The Notebook is available for download on [Vizro's GitHub repository](TO DO):
+The following code shows the code to make the request to Vizro-AI to build and display the dashboard. The Notebook is available for download on \[Vizro's GitHub repository\](TO DO):
 
 ```python
 user_question = """
-Create a dashboard with 3 pages, one for each chart. 
+Create a dashboard with 3 pages, one for each chart.
 
-On the first page, plot a chart with the title "Sequence of reading" . 
+On the first page, plot a chart with the title "Sequence of reading" .
 It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
 
-On the second page, lot a chart with the title "Pages and Book totals" . 
-It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data. 
+On the second page, lot a chart with the title "Pages and Book totals" .
+It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data.
 Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.
-Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. 
-Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side. 
+Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column.
+Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side.
 
 On the third page, for each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book.
 Use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.
@@ -199,23 +199,24 @@ The charts generated were similar to those created by the PyCafe host above, alt
 ![](../../assets/tutorials/project/chart1v2.png)
 
 ### Dashboard interactivity
-To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the code for a control. As a simple example, we can extend the prompt to ask for a filter to modify the time period displayed. 
+
+To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the code for a control. As a simple example, we can extend the prompt to ask for a filter to modify the time period displayed.
 
 ```diff
 user_question = """
-Create a dashboard with 3 pages, one for each chart. 
+Create a dashboard with 3 pages, one for each chart.
 
-On the first page, plot a chart with the title "Sequence of reading" . 
+On the first page, plot a chart with the title "Sequence of reading" .
 It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
 -
 + Add a filter so the user can change the x axis to adjust the range of dates by year on the x axis, using a slider to range from Jan 2023 to Dec 2024.
 
-On the second page, plot a chart with the title "Pages and Book totals" . 
-It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data. 
+On the second page, plot a chart with the title "Pages and Book totals" .
+It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data.
 Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.
-Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. 
+Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column.
 
-Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side. 
+Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side.
 -
 + Add a filter so the user can change the x axis to adjust the range of dates by year on the x axis, using a slider to range from Jan 2023 to Dec 2024.
 
@@ -226,9 +227,10 @@ Use shapes to add the horizontal lines between markers. Omit the legend. Don't s
 ```
 
 ## Interactive Vizro dashboards on PyCafe
+
 At this point, we have a Notebook with code to call Vizro-AI to build a prototype Vizro dashboard with a set of three pages and three charts, plus a control to filter the view.
 
-As we've already seen, the code generated by Vizro-AI can vary from run to run, and calling OpenAI each time a dashboard is needed can get costly. 
+As we've already seen, the code generated by Vizro-AI can vary from run to run, and calling OpenAI each time a dashboard is needed can get costly.
 
 The project isn't particularly easy to share at present either: sharing a Notebook requires every user to have an OpenAI key and set up an environment.
 
@@ -237,14 +239,16 @@ At this point, to share and iterate the prototype the best course of action is t
 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
-2. Add `Vizro().build(model).run()` at the end of the code block
-3. Uncomment the data manager code and replace it with code needed to access the dataset:
 
-        - If you are building your own PyCafe project, you can download the dataset from the Vizro GitHub repository and upload it to the PyCafe project.
-        - Alternatively, you can use the code specified below to read the dataset directly from online storage.
+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:
+
+    ```
+     - If you are building your own PyCafe project, you can download the dataset from the Vizro GitHub repository and upload it to the PyCafe project.
+     - Alternatively, you can use the code specified below to read the dataset directly from online storage.
+    ```
 
-	
-	
 !!! example "First dashboard"
     === "app.py"
         ```{.python pycafe-link}
@@ -254,7 +258,7 @@ There are three changes to the Notebook code needed for it to run on PyCafe:
         import pandas as pd
         import plotly.graph_objects as go
         from vizro.models.types import capture
-		
+
         ####### Function definitions ######
         @capture("graph")
         def sequence_reading(data_frame):
@@ -416,13 +420,7 @@ There are three changes to the Notebook code needed for it to run on PyCafe:
                 ),
             ],
             title="Book Reading Analysis Dashboard",
-        )        		
+        )
 
         Vizro().build(model).run()
         ```
-
- 
- 
- 
- 
- 
diff --git a/vizro-ai/mkdocs.yml b/vizro-ai/mkdocs.yml
index 7a2064ee1..bf1768567 100644
--- a/vizro-ai/mkdocs.yml
+++ b/vizro-ai/mkdocs.yml
@@ -87,7 +87,7 @@ markdown_extensions:
             kwds:
               type: vizro
               requirements: |
-                vizro==0.1.30  
+                vizro==0.1.30
   - pymdownx.tabbed:
       alternate_style: true
   - pymdownx.mark

From 6e5929ab86498c86821765a616ba1c028e3f3c59 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Wed, 15 Jan 2025 14:18:20 +0000
Subject: [PATCH 06/44] Update project-tutorial.md

---
 .../docs/pages/tutorials/project-tutorial.md  | 331 ++++++++++--------
 1 file changed, 177 insertions(+), 154 deletions(-)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index 033488d8b..fc64e7f80 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -3,159 +3,177 @@
 This tutorial uses Vizro-AI to build a prototype dashboard with three charts that illustrate a simple dataset. We first show how to create individual charts with Vizro-AI and then move on to learn how to use Vizro-AI to build a dashboard. The tutorial concludes by moving the prototype code generated by Vizro-AI into a project on PyCafe, for others to use and extend.
 
 ## Project background and data
-
 The dataset for this project was a set of books data [exported from a personal Goodreads account](https://www.goodreads.com/review/import). If you use Goodreads, you can export your own data in CSV format and use it with the code for this project.
 
 The dataset was filtered to retain only books with an ISBN, since that 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 used can be downloaded from the [Vizro repository](filtered_books.csv).
 
 ## OpenAI
-
-This tutorial uses OpenAI with Vizro-AI. To run through the steps, you must have an account with paid-for credits available. None of the free accounts will suffice. [Check the OpenAI models and pricing on their website](https://platform.openai.com/docs/models).
+This tutorial uses OpenAI with Vizro-AI. To run through the steps, you must have an account with paid-for credits available. None of the free accounts will suffice. [Check the OpenAI models and pricing on their website](https://platform.openai.com/docs/models). 
 
 !!! note
+    
     Before using a model, please review OpenAI's guidelines on risk mitigation to understand potential model limitations and best practices. [See the OpenAI site for more details on responsible usage](https://platform.openai.com/docs/guides/safety-best-practices).
 
-## Individual chart generation with Vizro-AI
 
+## Individual chart generation with Vizro-AI
 In this step, we'll show a UI on a hosted version of Vizro-AI, at [https://py.cafe/app/vizro-official/vizro-ai-charts](https://py.cafe/app/vizro-official/vizro-ai-charts). Use your browser to navigate to the site which looks as follows:
 
 ![](../../assets/tutorials/project/user-interface-hosted-vizro-ai.png)
 
 ### Settings
-
 You'll notice a cog icon at the top right hand corner for access to your settings, which look as follows:
 
 ![](../../assets/tutorials/project/user-interface-settings-hosted-vizro-ai.png)
 
-Add the API key for your chosen vendor. At the time of writing, you can use OpenAI, Anthropic, Mistral, or xAI.
+Add the API key for your chosen vendor. At the time of writing, you can use OpenAI, Anthropic, Mistral, or xAI. 
 
-Once the API key is set, return to the main screen and upload the data for the project.
+Once the API key is set, return to the main screen and upload the data for the project. 
 
 We can now dive use Vizro-AI to build some charts by iterating text to write effective prompts.
 
 ### Chart 1: Books timeline
-
 To ask Vizro-AI to build a chart, describe what you want to see. This chart should show an ordered horiontal timeline to illustrate the sequence of reading the books.
 
 > Plot a chart with the title "Sequence of reading" . It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
 
 You can adjust the model used: the chart below was generated from `gpt-4o-mini`. The chart displays on the right hand side of the screen and the Plotly code to generate the chart is below the prompt.
 
-The plot this code returns looks as follows. Hovering over each point gave the date the book was read, but not the title of the book. You'll also notice that the points are spaced evenly rather than proportionately: ![](../../assets/tutorials/project/chart1.png)
+The plot this code returns looks as follows. Hovering over each point gave the date the book was read, but not the title of the book. You'll also notice that the points are spaced evenly rather than proportionately:
+![](../../assets/tutorials/project/chart1.png)
 
-### Chart 2: Reading velocity
+??? example "Click to expand"
+    === "Generated by Vizro-AI"
+        ```
+        import plotly.graph_objects as go
+        from vizro.models.types import capture
+		
+        @capture("graph")
+        def custom_chart(data_frame):
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=data_frame["Date Read"], y=[1] * len(data_frame), mode="markers"))
+        fig.update_layout(title="Sequence of reading", xaxis_title="Date Read", yaxis_title="Y=1")
+        return fig
+        ```
 
+### Chart 2: Reading velocity
 The second chart should plot the cumulative total of pages read and the cumulative total of books read per year. The prompt took a few iterations to refine:
 
 > Plot a chart with the title "Pages and Book totals" . It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data. Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.
 
-> Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side.
+> Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side. 
 
 The Plotly code generated when run with `gpt-4-turbo` was as follows:
 
-```python
-import pandas as pd
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-from vizro.models.types import capture
-
-
-@capture("graph")
-def custom_chart(data_frame):
-    # Convert Date Read to datetime
-    data_frame["Date Read"] = pd.to_datetime(data_frame["Date Read"], dayfirst=True)
-
-    # Group by year and sum pages
-    pages_per_year = data_frame.groupby(data_frame["Date Read"].dt.year)["Number of Pages"].sum().cumsum()
+??? example "Click to expand"
+    === "Generated by Vizro-AI"
+		```python
+		import pandas as pd
+		import plotly.graph_objects as go
+		from plotly.subplots import make_subplots
+		from vizro.models.types import capture
+		
+		
+		@capture("graph")
+		def custom_chart(data_frame):
+		    # Convert Date Read to datetime
+		    data_frame["Date Read"] = pd.to_datetime(data_frame["Date Read"], dayfirst=True)
+		
+		    # Group by year and sum pages
+		    pages_per_year = (
+		        data_frame.groupby(data_frame["Date Read"].dt.year)["Number of Pages"].sum().cumsum()
+		    )
+		
+		    # Count books per year
+		    books_per_year = data_frame.groupby(data_frame["Date Read"].dt.year).size()
+		
+		    # Create subplot
+		    fig = make_subplots(specs=[[{"secondary_y": True}]])
+		
+		    # Add line for cumulative pages
+		    fig.add_trace(
+		        go.Scatter(
+		            x=pages_per_year.index,
+		            y=pages_per_year,
+		            mode="lines",
+		            name="Cumulative Pages",
+		        ),
+		        secondary_y=False,
+		    )
+		
+		    # Add bar for books count
+		    fig.add_trace(
+		        go.Bar(x=books_per_year.index, y=books_per_year, name="Total Books"),
+		        secondary_y=True,
+		    )
+		
+		    # Set y-axes titles
+		    fig.update_yaxes(title_text="Cumulative Pages", secondary_y=False)
+		    fig.update_yaxes(title_text="Total Books", secondary_y=True)
+		
+		    # Set layout
+		    fig.update_layout(title="Pages and Book totals", xaxis_title="Year", showlegend=True)
+		
+		    return fig
+		```
+
+The plot this code returns looks as follows:
+![](../../assets/tutorials/project/chart2.png)
 
-    # Count books per year
-    books_per_year = data_frame.groupby(data_frame["Date Read"].dt.year).size()
-
-    # Create subplot
-    fig = make_subplots(specs=[[{"secondary_y": True}]])
-
-    # Add line for cumulative pages
-    fig.add_trace(
-        go.Scatter(
-            x=pages_per_year.index,
-            y=pages_per_year,
-            mode="lines",
-            name="Cumulative Pages",
-        ),
-        secondary_y=False,
-    )
+### 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. It was run several times before it colored each line differently, which is a key learning when using generative AI: your results will vary from run to run.
 
-    # Add bar for books count
-    fig.add_trace(
-        go.Bar(x=books_per_year.index, y=books_per_year, name="Total Books"),
-        secondary_y=True,
-    )
+> For each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book - use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.
 
-    # Set y-axes titles
-    fig.update_yaxes(title_text="Cumulative Pages", secondary_y=False)
-    fig.update_yaxes(title_text="Total Books", secondary_y=True)
+??? example "Click to expand"
+    === "Generated by Vizro-AI"
+        ```
+        import plotly.graph_objects as go
+        from vizro.models.types import capture
 
-    # Set layout
-    fig.update_layout(title="Pages and Book totals", xaxis_title="Year", showlegend=True)
 
-    return fig
-```
+        @capture("graph")
+        def custom_chart(data_frame):
+            # Filter out rows where 'My Rating' is 0
+            df_filtered = data_frame[data_frame["My Rating"] != 0]
 
-The plot this code returns looks as follows: ![](../../assets/tutorials/project/chart2.png)
+            # Create a blank figure
+            fig = go.Figure()
 
-### Chart 3: Reviews comparison
+            # Add dumbbell lines and markers for each book
+            for index, row in df_filtered.iterrows():
+                fig.add_trace(
+                    go.Scatter(
+                        x=[row["My Rating"], row["Average Rating"]],
+                        y=[index, index],
+                        mode="markers+lines",
+                        marker=dict(size=10),
+                        line=dict(width=2),
+                        name=row["Title"],
+                        showlegend=False,
+                    )
+                )
 
-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. It was run several times before it colored each line differently, which is a key learning when using generative AI: your results will vary from run to run.
+            # Update layout
+            fig.update_layout(
+                title="Comparison of My Rating vs Average Rating",
+                xaxis_title="Rating",
+                yaxis_title="Books",
+                yaxis=dict(
+                    tickmode="array",
+                    tickvals=list(df_filtered.index),
+                    ticktext=df_filtered["Title"],
+                ),
+            )
+            return fig
+        ```
 
-> For each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book - use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.
+The plot this code returns looks as follows:
+![](../../assets/tutorials/project/chart3.png)
 
-```python
-import plotly.graph_objects as go
-from vizro.models.types import capture
-
-
-@capture("graph")
-def custom_chart(data_frame):
-    # Filter out rows where 'My Rating' is 0
-    df_filtered = data_frame[data_frame["My Rating"] != 0]
-
-    # Create a blank figure
-    fig = go.Figure()
-
-    # Add dumbbell lines and markers for each book
-    for index, row in df_filtered.iterrows():
-        fig.add_trace(
-            go.Scatter(
-                x=[row["My Rating"], row["Average Rating"]],
-                y=[index, index],
-                mode="markers+lines",
-                marker=dict(size=10),
-                line=dict(width=2),
-                name=row["Title"],
-                showlegend=False,
-            )
-        )
-
-    # Update layout
-    fig.update_layout(
-        title="Comparison of My Rating vs Average Rating",
-        xaxis_title="Rating",
-        yaxis_title="Books",
-        yaxis=dict(
-            tickmode="array",
-            tickvals=list(df_filtered.index),
-            ticktext=df_filtered["Title"],
-        ),
-    )
-    return fig
-```
-
-The plot this code returns looks as follows: ![](../../assets/tutorials/project/chart3.png)
 
 ## Dashboard generation with Vizro-AI
 
 ### Set up a Jupyter Notebook in which to use Vizro-AI
-
 At this point, we have prototypes for three plotly charts for the Goodreads data. To display these as an interactive dashboard, we need some additional code and Vizro-AI can generate this for us, but not through the PyCafe host at the time of writing. We'll use a Jupyter Notebook
 
 Before running the Notebook code, you need to [set up Vizro-AI](https://vizro.readthedocs.io/projects/vizro-ai/en/latest/pages/user-guides/install/) inside a virtual environment with Python 3.10 or later. Install the package with `pip install vizro_ai`.
@@ -163,32 +181,33 @@ Before running the Notebook code, you need to [set up Vizro-AI](https://vizro.re
 You need to give Vizro-AI your API key to access OpenAI by adding it to your environment so the code you write in the next step can access it to successfully call OpenAI. There are some [straightforward instructions in the OpenAI docs](https://platform.openai.com/docs/quickstart/step-2-set-up-your-api-key), and the process is also covered in the our [LLM setup guide](https://vizro.readthedocs.io/projects/vizro-ai/en/latest/pages/user-guides/install/#set-up-access-to-a-large-language-model).
 
 ### Build a dashboard
-
 At this point you can open a Jupyter Notebook to make the dashboard. We'll submit a prompt that combines the three prompts listed above, with some small edits to ask for a dashboard that has three pages: one for each chart.
 
-The following code shows the code to make the request to Vizro-AI to build and display the dashboard. The Notebook is available for download on \[Vizro's GitHub repository\](TO DO):
+The following code shows the code to make the request to Vizro-AI to build and display the dashboard.  The Notebook is available for download on [Vizro's GitHub repository](TO DO):
 
-```python
-user_question = """
-Create a dashboard with 3 pages, one for each chart.
+??? example "Click to expand"
+    === "Generated by Vizro-AI"
+        ```
+        user_question = """
+        Create a dashboard with 3 pages, one for each chart. 
 
-On the first page, plot a chart with the title "Sequence of reading" .
-It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
+        On the first page, plot a chart with the title "Sequence of reading" . 
+        It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
 
-On the second page, lot a chart with the title "Pages and Book totals" .
-It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data.
-Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.
-Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column.
-Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side.
+        On the second page, lot a chart with the title "Pages and Book totals" . 
+        It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data. 
+        Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.
+        Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. 
+        Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side. 
 
-On the third page, for each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book.
-Use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.
-"""
+        On the third page, for each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book.
+        Use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.
+        """
 
-result = vizro_ai.dashboard([df_cleaned], user_question, return_elements=True)
-Vizro().build(result.dashboard).run(port=8006)
-print(result.code)
-```
+        result = vizro_ai.dashboard([df_cleaned], user_question, return_elements=True)
+        Vizro().build(result.dashboard).run(port=8006)
+        print(result.code)
+        ```
 
 Using `gpt-4-turbo`, Vizro-AI generates a set of plotly chart code and the necessary Vizro support code to build a dashboard. The generated code is displayed as output in the Notebook with the dashboard, although the dashboard is better viewed at `http://localhost:8006/`.
 
@@ -199,38 +218,38 @@ The charts generated were similar to those created by the PyCafe host above, alt
 ![](../../assets/tutorials/project/chart1v2.png)
 
 ### Dashboard interactivity
+To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the code for a control. As a simple example, we can extend the prompt to ask for a filter to modify the time period displayed. 
 
-To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the code for a control. As a simple example, we can extend the prompt to ask for a filter to modify the time period displayed.
+??? example "Click to expand"
+    === "Generated by Vizro-AI"
+        ```diff
+        user_question = """
+        Create a dashboard with 3 pages, one for each chart. 
 
-```diff
-user_question = """
-Create a dashboard with 3 pages, one for each chart.
+        On the first page, plot a chart with the title "Sequence of reading" . 
+        It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
 
-On the first page, plot a chart with the title "Sequence of reading" .
-It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
--
-+ Add a filter so the user can change the x axis to adjust the range of dates by year on the x axis, using a slider to range from Jan 2023 to Dec 2024.
+        + Add a filter so the user can adjust the range of dates by year on the x axis.
 
-On the second page, plot a chart with the title "Pages and Book totals" .
-It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data.
-Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.
-Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column.
+        On the second page, plot a chart with the title "Pages and Book totals" . 
+        It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data. 
+        Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.
+        Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. 
 
-Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side.
--
-+ Add a filter so the user can change the x axis to adjust the range of dates by year on the x axis, using a slider to range from Jan 2023 to Dec 2024.
+        Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side. 
 
-On the third page, for each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book.
-Use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.
-"""
+        + Add a filter so the user can adjust the range of dates by year on the x axis.
 
-```
+        On the third page, for each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book.
+        Use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.
+        """
 
-## Interactive Vizro dashboards on PyCafe
+        ```
 
+## Interactive Vizro dashboards on PyCafe
 At this point, we have a Notebook with code to call Vizro-AI to build a prototype Vizro dashboard with a set of three pages and three charts, plus a control to filter the view.
 
-As we've already seen, the code generated by Vizro-AI can vary from run to run, and calling OpenAI each time a dashboard is needed can get costly.
+As we've already seen, the code generated by Vizro-AI can vary from run to run, and calling OpenAI each time a dashboard is needed can get costly. 
 
 The project isn't particularly easy to share at present either: sharing a Notebook requires every user to have an OpenAI key and set up an environment.
 
@@ -239,18 +258,16 @@ At this point, to share and iterate the prototype the best course of action is t
 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:
-
-    ```
-     - If you are building your own PyCafe project, you can download the dataset from the Vizro GitHub repository and upload it to the PyCafe project.
-     - Alternatively, you can use the code specified below to read the dataset directly from online storage.
-    ```
-
-!!! example "First dashboard"
-    === "app.py"
+2. Add `Vizro().build(model).run()` at the end of the code block
+3. Uncomment the data manager code and replace it with code needed to access the dataset:
+    - If you are building your own PyCafe project, you can download the dataset from the Vizro GitHub repository and upload it to the PyCafe project.
+    - Alternatively, you can 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.
+	
+	
+??? example "Click to expand"
+    === "PyCafe project based on Notebook code"
         ```{.python pycafe-link}
         from vizro import Vizro
         import vizro.models as vm
@@ -258,7 +275,7 @@ There are three changes to the Notebook code needed for it to run on PyCafe:
         import pandas as pd
         import plotly.graph_objects as go
         from vizro.models.types import capture
-
+		
         ####### Function definitions ######
         @capture("graph")
         def sequence_reading(data_frame):
@@ -420,7 +437,13 @@ There are three changes to the Notebook code needed for it to run on PyCafe:
                 ),
             ],
             title="Book Reading Analysis Dashboard",
-        )
+        )        		
 
         Vizro().build(model).run()
         ```
+
+ 
+ 
+ 
+ 
+ 

From 9c8849e8231d75569233f0b0a08309f738544d89 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 15 Jan 2025 14:20:22 +0000
Subject: [PATCH 07/44] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 .../docs/pages/tutorials/project-tutorial.md  | 180 +++++++++---------
 1 file changed, 88 insertions(+), 92 deletions(-)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index fc64e7f80..6b6a0659f 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -3,50 +3,52 @@
 This tutorial uses Vizro-AI to build a prototype dashboard with three charts that illustrate a simple dataset. We first show how to create individual charts with Vizro-AI and then move on to learn how to use Vizro-AI to build a dashboard. The tutorial concludes by moving the prototype code generated by Vizro-AI into a project on PyCafe, for others to use and extend.
 
 ## Project background and data
+
 The dataset for this project was a set of books data [exported from a personal Goodreads account](https://www.goodreads.com/review/import). If you use Goodreads, you can export your own data in CSV format and use it with the code for this project.
 
 The dataset was filtered to retain only books with an ISBN, since that 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 used can be downloaded from the [Vizro repository](filtered_books.csv).
 
 ## OpenAI
-This tutorial uses OpenAI with Vizro-AI. To run through the steps, you must have an account with paid-for credits available. None of the free accounts will suffice. [Check the OpenAI models and pricing on their website](https://platform.openai.com/docs/models). 
+
+This tutorial uses OpenAI with Vizro-AI. To run through the steps, you must have an account with paid-for credits available. None of the free accounts will suffice. [Check the OpenAI models and pricing on their website](https://platform.openai.com/docs/models).
 
 !!! note
-    
     Before using a model, please review OpenAI's guidelines on risk mitigation to understand potential model limitations and best practices. [See the OpenAI site for more details on responsible usage](https://platform.openai.com/docs/guides/safety-best-practices).
 
-
 ## Individual chart generation with Vizro-AI
+
 In this step, we'll show a UI on a hosted version of Vizro-AI, at [https://py.cafe/app/vizro-official/vizro-ai-charts](https://py.cafe/app/vizro-official/vizro-ai-charts). Use your browser to navigate to the site which looks as follows:
 
 ![](../../assets/tutorials/project/user-interface-hosted-vizro-ai.png)
 
 ### Settings
+
 You'll notice a cog icon at the top right hand corner for access to your settings, which look as follows:
 
 ![](../../assets/tutorials/project/user-interface-settings-hosted-vizro-ai.png)
 
-Add the API key for your chosen vendor. At the time of writing, you can use OpenAI, Anthropic, Mistral, or xAI. 
+Add the API key for your chosen vendor. At the time of writing, you can use OpenAI, Anthropic, Mistral, or xAI.
 
-Once the API key is set, return to the main screen and upload the data for the project. 
+Once the API key is set, return to the main screen and upload the data for the project.
 
 We can now dive use Vizro-AI to build some charts by iterating text to write effective prompts.
 
 ### Chart 1: Books timeline
+
 To ask Vizro-AI to build a chart, describe what you want to see. This chart should show an ordered horiontal timeline to illustrate the sequence of reading the books.
 
 > Plot a chart with the title "Sequence of reading" . It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
 
 You can adjust the model used: the chart below was generated from `gpt-4o-mini`. The chart displays on the right hand side of the screen and the Plotly code to generate the chart is below the prompt.
 
-The plot this code returns looks as follows. Hovering over each point gave the date the book was read, but not the title of the book. You'll also notice that the points are spaced evenly rather than proportionately:
-![](../../assets/tutorials/project/chart1.png)
+The plot this code returns looks as follows. Hovering over each point gave the date the book was read, but not the title of the book. You'll also notice that the points are spaced evenly rather than proportionately: ![](../../assets/tutorials/project/chart1.png)
 
 ??? example "Click to expand"
     === "Generated by Vizro-AI"
         ```
         import plotly.graph_objects as go
         from vizro.models.types import capture
-		
+
         @capture("graph")
         def custom_chart(data_frame):
         fig = go.Figure()
@@ -56,70 +58,69 @@ The plot this code returns looks as follows. Hovering over each point gave the d
         ```
 
 ### Chart 2: Reading velocity
+
 The second chart should plot the cumulative total of pages read and the cumulative total of books read per year. The prompt took a few iterations to refine:
 
 > Plot a chart with the title "Pages and Book totals" . It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data. Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.
 
-> Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side. 
+> Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side.
 
 The Plotly code generated when run with `gpt-4-turbo` was as follows:
 
 ??? example "Click to expand"
     === "Generated by Vizro-AI"
-		```python
-		import pandas as pd
-		import plotly.graph_objects as go
-		from plotly.subplots import make_subplots
-		from vizro.models.types import capture
-		
-		
-		@capture("graph")
-		def custom_chart(data_frame):
-		    # Convert Date Read to datetime
-		    data_frame["Date Read"] = pd.to_datetime(data_frame["Date Read"], dayfirst=True)
-		
-		    # Group by year and sum pages
-		    pages_per_year = (
-		        data_frame.groupby(data_frame["Date Read"].dt.year)["Number of Pages"].sum().cumsum()
-		    )
-		
-		    # Count books per year
-		    books_per_year = data_frame.groupby(data_frame["Date Read"].dt.year).size()
-		
-		    # Create subplot
-		    fig = make_subplots(specs=[[{"secondary_y": True}]])
-		
-		    # Add line for cumulative pages
-		    fig.add_trace(
-		        go.Scatter(
-		            x=pages_per_year.index,
-		            y=pages_per_year,
-		            mode="lines",
-		            name="Cumulative Pages",
-		        ),
-		        secondary_y=False,
-		    )
-		
-		    # Add bar for books count
-		    fig.add_trace(
-		        go.Bar(x=books_per_year.index, y=books_per_year, name="Total Books"),
-		        secondary_y=True,
-		    )
-		
-		    # Set y-axes titles
-		    fig.update_yaxes(title_text="Cumulative Pages", secondary_y=False)
-		    fig.update_yaxes(title_text="Total Books", secondary_y=True)
-		
-		    # Set layout
-		    fig.update_layout(title="Pages and Book totals", xaxis_title="Year", showlegend=True)
-		
-		    return fig
-		```
-
-The plot this code returns looks as follows:
-![](../../assets/tutorials/project/chart2.png)
+        ```python
+        import pandas as pd
+        import plotly.graph_objects as go
+        from plotly.subplots import make_subplots
+        from vizro.models.types import capture
+
+
+        @capture("graph")
+        def custom_chart(data_frame):
+            # Convert Date Read to datetime
+            data_frame["Date Read"] = pd.to_datetime(data_frame["Date Read"], dayfirst=True)
+
+            # Group by year and sum pages
+            pages_per_year = data_frame.groupby(data_frame["Date Read"].dt.year)["Number of Pages"].sum().cumsum()
+
+            # Count books per year
+            books_per_year = data_frame.groupby(data_frame["Date Read"].dt.year).size()
+
+            # Create subplot
+            fig = make_subplots(specs=[[{"secondary_y": True}]])
+
+            # Add line for cumulative pages
+            fig.add_trace(
+                go.Scatter(
+                    x=pages_per_year.index,
+                    y=pages_per_year,
+                    mode="lines",
+                    name="Cumulative Pages",
+                ),
+                secondary_y=False,
+            )
+
+            # Add bar for books count
+            fig.add_trace(
+                go.Bar(x=books_per_year.index, y=books_per_year, name="Total Books"),
+                secondary_y=True,
+            )
+
+            # Set y-axes titles
+            fig.update_yaxes(title_text="Cumulative Pages", secondary_y=False)
+            fig.update_yaxes(title_text="Total Books", secondary_y=True)
+
+            # Set layout
+            fig.update_layout(title="Pages and Book totals", xaxis_title="Year", showlegend=True)
+
+            return fig
+        ```
+
+The plot this code returns looks as follows: ![](../../assets/tutorials/project/chart2.png)
 
 ### 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. It was run several times before it colored each line differently, which is a key learning when using generative AI: your results will vary from run to run.
 
 > For each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book - use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.
@@ -167,13 +168,12 @@ The third chart should illustrate the difference between the rating the Goodread
             return fig
         ```
 
-The plot this code returns looks as follows:
-![](../../assets/tutorials/project/chart3.png)
-
+The plot this code returns looks as follows: ![](../../assets/tutorials/project/chart3.png)
 
 ## Dashboard generation with Vizro-AI
 
 ### Set up a Jupyter Notebook in which to use Vizro-AI
+
 At this point, we have prototypes for three plotly charts for the Goodreads data. To display these as an interactive dashboard, we need some additional code and Vizro-AI can generate this for us, but not through the PyCafe host at the time of writing. We'll use a Jupyter Notebook
 
 Before running the Notebook code, you need to [set up Vizro-AI](https://vizro.readthedocs.io/projects/vizro-ai/en/latest/pages/user-guides/install/) inside a virtual environment with Python 3.10 or later. Install the package with `pip install vizro_ai`.
@@ -181,24 +181,25 @@ Before running the Notebook code, you need to [set up Vizro-AI](https://vizro.re
 You need to give Vizro-AI your API key to access OpenAI by adding it to your environment so the code you write in the next step can access it to successfully call OpenAI. There are some [straightforward instructions in the OpenAI docs](https://platform.openai.com/docs/quickstart/step-2-set-up-your-api-key), and the process is also covered in the our [LLM setup guide](https://vizro.readthedocs.io/projects/vizro-ai/en/latest/pages/user-guides/install/#set-up-access-to-a-large-language-model).
 
 ### Build a dashboard
+
 At this point you can open a Jupyter Notebook to make the dashboard. We'll submit a prompt that combines the three prompts listed above, with some small edits to ask for a dashboard that has three pages: one for each chart.
 
-The following code shows the code to make the request to Vizro-AI to build and display the dashboard.  The Notebook is available for download on [Vizro's GitHub repository](TO DO):
+The following code shows the code to make the request to Vizro-AI to build and display the dashboard. The Notebook is available for download on \[Vizro's GitHub repository\](TO DO):
 
 ??? example "Click to expand"
     === "Generated by Vizro-AI"
         ```
         user_question = """
-        Create a dashboard with 3 pages, one for each chart. 
+        Create a dashboard with 3 pages, one for each chart.
 
-        On the first page, plot a chart with the title "Sequence of reading" . 
+        On the first page, plot a chart with the title "Sequence of reading" .
         It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
 
-        On the second page, lot a chart with the title "Pages and Book totals" . 
-        It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data. 
+        On the second page, lot a chart with the title "Pages and Book totals" .
+        It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data.
         Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.
-        Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. 
-        Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side. 
+        Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column.
+        Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side.
 
         On the third page, for each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book.
         Use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.
@@ -218,25 +219,26 @@ The charts generated were similar to those created by the PyCafe host above, alt
 ![](../../assets/tutorials/project/chart1v2.png)
 
 ### Dashboard interactivity
-To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the code for a control. As a simple example, we can extend the prompt to ask for a filter to modify the time period displayed. 
+
+To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the code for a control. As a simple example, we can extend the prompt to ask for a filter to modify the time period displayed.
 
 ??? example "Click to expand"
     === "Generated by Vizro-AI"
         ```diff
         user_question = """
-        Create a dashboard with 3 pages, one for each chart. 
+        Create a dashboard with 3 pages, one for each chart.
 
-        On the first page, plot a chart with the title "Sequence of reading" . 
+        On the first page, plot a chart with the title "Sequence of reading" .
         It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
 
         + Add a filter so the user can adjust the range of dates by year on the x axis.
 
-        On the second page, plot a chart with the title "Pages and Book totals" . 
-        It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data. 
+        On the second page, plot a chart with the title "Pages and Book totals" .
+        It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data.
         Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.
-        Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. 
+        Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column.
 
-        Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side. 
+        Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side.
 
         + Add a filter so the user can adjust the range of dates by year on the x axis.
 
@@ -247,9 +249,10 @@ To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the co
         ```
 
 ## Interactive Vizro dashboards on PyCafe
+
 At this point, we have a Notebook with code to call Vizro-AI to build a prototype Vizro dashboard with a set of three pages and three charts, plus a control to filter the view.
 
-As we've already seen, the code generated by Vizro-AI can vary from run to run, and calling OpenAI each time a dashboard is needed can get costly. 
+As we've already seen, the code generated by Vizro-AI can vary from run to run, and calling OpenAI each time a dashboard is needed can get costly.
 
 The project isn't particularly easy to share at present either: sharing a Notebook requires every user to have an OpenAI key and set up an environment.
 
@@ -258,14 +261,13 @@ At this point, to share and iterate the prototype the best course of action is t
 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
-2. Add `Vizro().build(model).run()` at the end of the code block
-3. Uncomment the data manager code and replace it with code needed to access the dataset:
+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:
     - If you are building your own PyCafe project, you can download the dataset from the Vizro GitHub repository and upload it to the PyCafe project.
     - Alternatively, you can 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.
-	
-	
+Follow the link at the bottom of the code snippet titled **☕️ Run and edit this code in PyCafe** to use and edit the dashboard.
+
 ??? example "Click to expand"
     === "PyCafe project based on Notebook code"
         ```{.python pycafe-link}
@@ -275,7 +277,7 @@ Follow the link at the bottom of the code snippet titled  **☕️ Run and edit
         import pandas as pd
         import plotly.graph_objects as go
         from vizro.models.types import capture
-		
+
         ####### Function definitions ######
         @capture("graph")
         def sequence_reading(data_frame):
@@ -437,13 +439,7 @@ Follow the link at the bottom of the code snippet titled  **☕️ Run and edit
                 ),
             ],
             title="Book Reading Analysis Dashboard",
-        )        		
+        )
 
         Vizro().build(model).run()
         ```
-
- 
- 
- 
- 
- 

From e4fe78843a95c06c29332d47caeeaa7d64202e58 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Wed, 15 Jan 2025 14:55:53 +0000
Subject: [PATCH 08/44] Update project-tutorial.md

---
 .../docs/pages/tutorials/project-tutorial.md    | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index 6b6a0659f..b37e4f5eb 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -6,7 +6,8 @@ This tutorial uses Vizro-AI to build a prototype dashboard with three charts tha
 
 The dataset for this project was a set of books data [exported from a personal Goodreads account](https://www.goodreads.com/review/import). If you use Goodreads, you can export your own data in CSV format and use it with the code for this project.
 
-The dataset was filtered to retain only books with an ISBN, since that 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 used can be downloaded from the [Vizro repository](filtered_books.csv).
+The dataset was filtered to retain only books with an ISBN, since that 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 used can be downloaded from the [Vizro repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv). <!--TO DO -- This will change when data is uploaded to Vizro repo-->
+
 
 ## OpenAI
 
@@ -15,7 +16,7 @@ This tutorial uses OpenAI with Vizro-AI. To run through the steps, you must have
 !!! note
     Before using a model, please review OpenAI's guidelines on risk mitigation to understand potential model limitations and best practices. [See the OpenAI site for more details on responsible usage](https://platform.openai.com/docs/guides/safety-best-practices).
 
-## Individual chart generation with Vizro-AI
+## Chart generation with Vizro-AI
 
 In this step, we'll show a UI on a hosted version of Vizro-AI, at [https://py.cafe/app/vizro-official/vizro-ai-charts](https://py.cafe/app/vizro-official/vizro-ai-charts). Use your browser to navigate to the site which looks as follows:
 
@@ -45,7 +46,7 @@ The plot this code returns looks as follows. Hovering over each point gave the d
 
 ??? example "Click to expand"
     === "Generated by Vizro-AI"
-        ```
+        ```python
         import plotly.graph_objects as go
         from vizro.models.types import capture
 
@@ -127,7 +128,7 @@ The third chart should illustrate the difference between the rating the Goodread
 
 ??? example "Click to expand"
     === "Generated by Vizro-AI"
-        ```
+        ```python
         import plotly.graph_objects as go
         from vizro.models.types import capture
 
@@ -172,7 +173,7 @@ The plot this code returns looks as follows: ![](../../assets/tutorials/project/
 
 ## Dashboard generation with Vizro-AI
 
-### Set up a Jupyter Notebook in which to use Vizro-AI
+### Set up a Jupyter Notebook
 
 At this point, we have prototypes for three plotly charts for the Goodreads data. To display these as an interactive dashboard, we need some additional code and Vizro-AI can generate this for us, but not through the PyCafe host at the time of writing. We'll use a Jupyter Notebook
 
@@ -188,7 +189,7 @@ The following code shows the code to make the request to Vizro-AI to build and d
 
 ??? example "Click to expand"
     === "Generated by Vizro-AI"
-        ```
+        ```python
         user_question = """
         Create a dashboard with 3 pages, one for each chart.
 
@@ -218,12 +219,12 @@ The charts generated were similar to those created by the PyCafe host above, alt
 
 ![](../../assets/tutorials/project/chart1v2.png)
 
-### Dashboard interactivity
+### Add dashboard interactivity
 
 To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the code for a control. As a simple example, we can extend the prompt to ask for a filter to modify the time period displayed.
 
 ??? example "Click to expand"
-    === "Generated by Vizro-AI"
+    === "Extend the prompt to add a control"
         ```diff
         user_question = """
         Create a dashboard with 3 pages, one for each chart.

From e0a4317a072f07a1071de799830c5c92ce02e9c2 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 15 Jan 2025 14:57:20 +0000
Subject: [PATCH 09/44] [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 | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index b37e4f5eb..39d0aca4f 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -6,8 +6,7 @@ This tutorial uses Vizro-AI to build a prototype dashboard with three charts tha
 
 The dataset for this project was a set of books data [exported from a personal Goodreads account](https://www.goodreads.com/review/import). If you use Goodreads, you can export your own data in CSV format and use it with the code for this project.
 
-The dataset was filtered to retain only books with an ISBN, since that 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 used can be downloaded from the [Vizro repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv). <!--TO DO -- This will change when data is uploaded to Vizro repo-->
-
+The dataset was filtered to retain only books with an ISBN, since that 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 used can be downloaded from the [Vizro repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv). \<!--TO DO -- This will change when data is uploaded to Vizro repo-->
 
 ## OpenAI
 

From 02bd48e3057354474f8072eec8ca1ca49b9f960a Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Wed, 15 Jan 2025 16:17:27 +0000
Subject: [PATCH 10/44] Update project-tutorial.md

---
 .../docs/pages/tutorials/project-tutorial.md  | 227 +++++++++++++++++-
 1 file changed, 217 insertions(+), 10 deletions(-)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index 39d0aca4f..146487f4d 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -35,11 +35,11 @@ We can now dive use Vizro-AI to build some charts by iterating text to write eff
 
 ### Chart 1: Books timeline
 
-To ask Vizro-AI to build a chart, describe what you want to see. This chart should show an ordered horiontal timeline to illustrate the sequence of reading the books.
+To ask Vizro-AI to build a chart, we describe what we want to see. This chart should show an ordered horiontal timeline to illustrate the sequence of reading the books.
 
 > Plot a chart with the title "Sequence of reading" . It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
 
-You can adjust the model used: the chart below was generated from `gpt-4o-mini`. The chart displays on the right hand side of the screen and the Plotly code to generate the chart is below the prompt.
+We can adjust the model used: the chart below was generated from `gpt-4o-mini`. The chart displays on the right hand side of the screen and the Plotly code to generate the chart is below the prompt.
 
 The plot this code returns looks as follows. Hovering over each point gave the date the book was read, but not the title of the book. You'll also notice that the points are spaced evenly rather than proportionately: ![](../../assets/tutorials/project/chart1.png)
 
@@ -176,15 +176,15 @@ The plot this code returns looks as follows: ![](../../assets/tutorials/project/
 
 At this point, we have prototypes for three plotly charts for the Goodreads data. To display these as an interactive dashboard, we need some additional code and Vizro-AI can generate this for us, but not through the PyCafe host at the time of writing. We'll use a Jupyter Notebook
 
-Before running the Notebook code, you need to [set up Vizro-AI](https://vizro.readthedocs.io/projects/vizro-ai/en/latest/pages/user-guides/install/) inside a virtual environment with Python 3.10 or later. Install the package with `pip install vizro_ai`.
+Before running the Notebook code, [set up Vizro-AI](https://vizro.readthedocs.io/projects/vizro-ai/en/latest/pages/user-guides/install/) inside a virtual environment with Python 3.10 or later. Install the package with `pip install vizro_ai`.
 
 You need to give Vizro-AI your API key to access OpenAI by adding it to your environment so the code you write in the next step can access it to successfully call OpenAI. There are some [straightforward instructions in the OpenAI docs](https://platform.openai.com/docs/quickstart/step-2-set-up-your-api-key), and the process is also covered in the our [LLM setup guide](https://vizro.readthedocs.io/projects/vizro-ai/en/latest/pages/user-guides/install/#set-up-access-to-a-large-language-model).
 
 ### Build a dashboard
 
-At this point you can open a Jupyter Notebook to make the dashboard. We'll submit a prompt that combines the three prompts listed above, with some small edits to ask for a dashboard that has three pages: one for each chart.
+Now we can open a Jupyter Notebook to make the dashboard. We'll submit a prompt that combines the three prompts listed above, with some small edits to ask for a dashboard that has three pages: one for each chart.
 
-The following code shows the code to make the request to Vizro-AI to build and display the dashboard. The Notebook is available for download on \[Vizro's GitHub repository\](TO DO):
+The following code shows the code to make the request to Vizro-AI to build and display the dashboard. The Notebook is available for download from [Vizro's GitHub repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv): <!-- TO DO change this when uploaded -->
 
 ??? example "Click to expand"
     === "Generated by Vizro-AI"
@@ -212,7 +212,7 @@ The following code shows the code to make the request to Vizro-AI to build and d
 
 Using `gpt-4-turbo`, Vizro-AI generates a set of plotly chart code and the necessary Vizro support code to build a dashboard. The generated code is displayed as output in the Notebook with the dashboard, although the dashboard is better viewed at `http://localhost:8006/`.
 
-You can see the code output in the Notebook stored on the Vizro GitHub repository, or generate similar output by running it yourself.
+You can see the code output in the [Notebook stored on the Vizro GitHub repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv) <!--TO DO change this when uploaded-->, or generate similar output by running it yourself.
 
 The charts generated were similar to those created by the PyCafe host above, although the first chart was improved by spacing the books proportionate to the date read and rolling over each point gave the book title as well as the date read.
 
@@ -250,21 +250,21 @@ To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the co
 
 ## Interactive Vizro dashboards on PyCafe
 
-At this point, we have a Notebook with code to call Vizro-AI to build a prototype Vizro dashboard with a set of three pages and three charts, plus a control to filter the view.
+Now we have a Notebook with code to call Vizro-AI to build a prototype Vizro dashboard with a set of three pages and three charts, plus a control to filter the view.
 
 As we've already seen, the code generated by Vizro-AI can vary from run to run, and calling OpenAI each time a dashboard is needed can get costly.
 
 The project isn't particularly easy to share at present either: sharing a Notebook requires every user to have an OpenAI key and set up an environment.
 
-At this point, to share and iterate the prototype the best course of action is to transfer the generated code from the output of Vizro-AI in the Notebook into a PyCafe project.
+To share and iterate the prototype, we can transfer the generated code from the output of Vizro-AI in the Notebook into a PyCafe project.
 
 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:
-    - If you are building your own PyCafe project, you can download the dataset from the Vizro GitHub repository and upload it to the PyCafe project.
-    - Alternatively, you can use the code added to the snippet below to read the dataset directly from online storage.
+    - either download the dataset from the [Vizro GitHub repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv) \<!-- TO DO -- change this --> 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.
 
@@ -443,3 +443,210 @@ Follow the link at the bottom of the code snippet titled **☕️ Run and edit t
 
         Vizro().build(model).run()
         ```
+
+## Summary
+
+In this tutorial, we first used Vizro-AI to generate a set of charts by iterating prompts, hosted in PyCafe.
+
+We then converted the successful prompts to build a Vizro dashboard using Vizro-AI in a Notebook using a few lines of support code.
+
+Finally, we converted the Vizro-AI generated Python to a PyCafe project with a few additional lines of support code. At this point, the Vizro project is not using generative AI, so it is easy to share and iterate.
+
+The code generated by Vizro-AI does not make a perfect dashboard, but it has been very easy to get it to a good standard without a great deal of coding or design experience. There are a few improvements that are easily applied to the Plotly code to further improve the charts, as shown below in a separate PyCafe project.
+
+### Improvements to generated code
+
+In this version of the dashboard, the first chart that show the sequence of books read has been modified to improve the information supplied when hovering over a point, and the opacity of the points has been altered to make it more attractive.
+
+In the second chart that shows the cumulative total of pages and books read, the line chart has been explicitly plotted on top of the bar chart.
+
+In the third chart that shows the ratings comparison, the color scheme has been updated to make it clearer which is the `My Rating` value compared to the `Average Rating`.
+
+??? example "Click to expand"
+    === "PyCafe project based on Notebook code"
+        ```{.python pycafe-link}
+        from vizro import Vizro
+        import vizro.models as vm
+        from vizro.models.types import capture
+        import pandas as pd
+        import plotly.graph_objects as go
+        from vizro.models.types import capture
+
+        ####### Function definitions ######
+        @capture("graph")
+        def sequence_reading(data_frame):
+            fig = go.Figure()
+
+            # Iterate through the filtered DataFrame to add traces
+            for index, row in data_frame.iterrows():
+                fig.add_trace(
+                    go.Scatter(
+                        x=[row["Date Read"], row["Date Read"]],
+                        y=[1],
+                        customdata=[[row["Title"], row["Author"], row["My Rating"]]],
+                        showlegend=False,
+                        marker=dict(color="#00b4ff", size=20, opacity=0.4),
+                        hovertemplate="<b>Title:</b> %{customdata[0]}<br>"
+                        + "<b>Author:</b> %{customdata[1]}<br>"
+                        + "<b>My Rating:</b> %{customdata[2]}<br>"
+                        + "<b>Date:</b> %{x}"
+                        + "<extra></extra>",
+                    )
+                )
+            fig.update_layout(
+                title="Sequence of reading",
+                xaxis_title="Date Read",
+                yaxis_title="Sequence",
+                yaxis=dict(showticklabels=False, showgrid=False),
+                xaxis=dict(tickangle=-45),
+            )
+            return fig
+
+
+        @capture("graph")
+        def pages_books_totals(data_frame):
+            # Prepare data
+            data_frame["Date Read"] = pd.to_datetime(data_frame["Date Read"])
+            data_frame.sort_values("Date Read", inplace=True)
+            data_frame["Cumulative Pages"] = data_frame["Number of Pages"].cumsum()
+
+            # Aggregate data by year for total books read
+            yearly_books = data_frame.groupby(data_frame["Date Read"].dt.year).size()
+
+            # Create figure with secondary y-axis
+            fig = go.Figure()
+
+            # Add line for cumulative pages
+            fig.add_trace(
+                go.Scatter(
+                    x=data_frame["Date Read"],
+                    y=data_frame["Cumulative Pages"],
+                    mode="lines",
+                    name="Cumulative Pages Read",
+                    zorder=1,  # Ensure the line chart is rendered on top of the bar chart
+                )
+            )
+
+            # Add bars for books read per year
+            fig.add_trace(
+                go.Bar(
+                    x=yearly_books.index, y=yearly_books, name="Books Read per Year", yaxis="y2"
+                )
+            )
+
+            # Set up the layout
+            fig.update_layout(
+                title="Cumulative Pages Read and Books Read per Year",
+                xaxis_title="Date",
+                yaxis=dict(title="Number of Pages"),
+                yaxis2=dict(title="Total Books", overlaying="y", side="right"),
+            )
+
+            return fig
+
+
+        @capture("graph")
+        def rating_comparison(data_frame):
+            # Filter out rows where 'My Rating' is 0
+            df = data_frame[data_frame["My Rating"] != 0]
+            # Sort by decreasing average rating
+            df = df.sort_values(by="Average Rating", ascending=False)
+
+            # Create a figure
+            fig = go.Figure()
+
+            # Add dumbbell traces
+            for index, row in df.iterrows():
+                fig.add_trace(
+                    go.Scatter(
+                        x=[row["My Rating"], row["Average Rating"]],
+                        y=[row["Title"], row["Title"]],
+                        mode="markers+lines",
+                        line=dict(color="gray", width=2),
+                        marker=dict(color=["#00b4ff", "#ff9222"], size=10),
+                        showlegend=False,
+                    )
+                )
+
+            # Update layout
+            fig.update_layout(
+                title="Comparison of My Rating (blue) vs Average Rating (orange)",
+                xaxis_title="Rating",
+                yaxis_title="Book Title",
+                showlegend=False,
+            )
+
+            return fig
+
+        ########### Read data ############
+        # Define the URL of the raw CSV file
+        # TO DO -- This will change when data is uploaded to Vizro repo
+        import requests
+        url = "https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv"
+
+        # Fetch the CSV file
+        response = requests.get(url)
+        response.raise_for_status()  # Ensure we notice bad responses
+
+        # Load the CSV data into a pandas DataFrame
+        from io import StringIO
+        csv_data = StringIO(response.text)
+        df = pd.read_csv(csv_data)
+        df["Date Read"] = pd.to_datetime(df['Date Read'],dayfirst=True)
+
+
+        ########### Model code ############
+        model = vm.Dashboard(
+            pages=[
+                vm.Page(
+                    components=[
+                        vm.Graph(
+                            id="sequence_reading",
+                            figure=sequence_reading(df),
+                        )
+                    ],
+                    title="Sequence of Reading",
+                    layout=vm.Layout(grid=[[0]]),
+                    controls=[
+                        vm.Filter(
+                            column="Year Published",
+                            targets=["sequence_reading"],
+                            selector=vm.RangeSlider(type="range_slider"),
+                        )
+                    ],
+                ),
+                vm.Page(
+                    components=[
+                        vm.Graph(
+                            id="pages_books_totals",
+                            figure=pages_books_totals(df),
+                        )
+                    ],
+                    title="Pages and Book Totals",
+                    layout=vm.Layout(grid=[[0]]),
+                    controls=[
+                        vm.Filter(
+                            column="Year Published",
+                            targets=["pages_books_totals"],
+                            selector=vm.RangeSlider(type="range_slider"),
+                        )
+                    ],
+                ),
+                vm.Page(
+                    components=[
+                        vm.Graph(
+                            id="rating_comparison",
+                            figure=rating_comparison(df),
+                        )
+                    ],
+                    title="Rating Comparison",
+                    layout=vm.Layout(grid=[[0]]),
+                    controls=[],
+                ),
+            ],
+            title="Book Reading Analysis Dashboard",
+        )
+
+        Vizro().build(model).run()
+
+        ```

From b15c8fb6dcab7963f2660a8a1eb19cd682048a7b Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Wed, 15 Jan 2025 16:23:11 +0000
Subject: [PATCH 11/44] Update project-tutorial.md

---
 vizro-ai/docs/pages/tutorials/project-tutorial.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index 146487f4d..46c24ad54 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -212,7 +212,7 @@ The following code shows the code to make the request to Vizro-AI to build and d
 
 Using `gpt-4-turbo`, Vizro-AI generates a set of plotly chart code and the necessary Vizro support code to build a dashboard. The generated code is displayed as output in the Notebook with the dashboard, although the dashboard is better viewed at `http://localhost:8006/`.
 
-You can see the code output in the [Notebook stored on the Vizro GitHub repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv) <!--TO DO change this when uploaded-->, or generate similar output by running it yourself.
+You can see the code output in the [Notebook stored on the Vizro GitHub repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/goodreads.ipynb) <!--TO DO change this when uploaded-->, or generate similar output by running it yourself.
 
 The charts generated were similar to those created by the PyCafe host above, although the first chart was improved by spacing the books proportionate to the date read and rolling over each point gave the book title as well as the date read.
 

From c4912a0e4141c021ec476ebdf094150a6025cf41 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Wed, 15 Jan 2025 16:31:00 +0000
Subject: [PATCH 12/44] Update project-tutorial.md

---
 vizro-ai/docs/pages/tutorials/project-tutorial.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index 46c24ad54..4ac846bad 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -462,6 +462,8 @@ In the second chart that shows the cumulative total of pages and books read, the
 
 In the third chart that shows the ratings comparison, the color scheme has been updated to make it clearer which is the `My Rating` value compared to the `Average Rating`.
 
+Follow the link at the bottom of the code snippet titled **☕️ Run and edit this code in PyCafe** to use and edit the dashboard.
+
 ??? example "Click to expand"
     === "PyCafe project based on Notebook code"
         ```{.python pycafe-link}

From 37a104ca5d52d2f6a2e87e4e125e3fa168cfd35b Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 16 Jan 2025 09:20:40 +0000
Subject: [PATCH 13/44] Update
 vizro-ai/docs/pages/tutorials/project-tutorial.md

Co-authored-by: Lingyi Zhang <lingyi_zhang@mckinsey.com>
---
 vizro-ai/docs/pages/tutorials/project-tutorial.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index 4ac846bad..f6654c32c 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -10,7 +10,7 @@ The dataset was filtered to retain only books with an ISBN, since that can be us
 
 ## OpenAI
 
-This tutorial uses OpenAI with Vizro-AI. To run through the steps, you must have an account with paid-for credits available. None of the free accounts will suffice. [Check the OpenAI models and pricing on their website](https://platform.openai.com/docs/models).
+This tutorial uses OpenAI models with Vizro-AI. To run through the steps, you must have an account with paid-for credits available. None of the free accounts will suffice. [Check the OpenAI models and pricing on their website](https://platform.openai.com/docs/models).
 
 !!! note
     Before using a model, please review OpenAI's guidelines on risk mitigation to understand potential model limitations and best practices. [See the OpenAI site for more details on responsible usage](https://platform.openai.com/docs/guides/safety-best-practices).

From 54934ee1dcb1dca3a9f7dc07df92ae04cabfa490 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 16 Jan 2025 09:24:56 +0000
Subject: [PATCH 14/44] Update
 vizro-ai/docs/pages/tutorials/project-tutorial.md

Co-authored-by: Maximilian Schulz <83698606+maxschulz-COL@users.noreply.github.com>
---
 vizro-ai/docs/pages/tutorials/project-tutorial.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index f6654c32c..da3f87892 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -174,7 +174,7 @@ The plot this code returns looks as follows: ![](../../assets/tutorials/project/
 
 ### Set up a Jupyter Notebook
 
-At this point, we have prototypes for three plotly charts for the Goodreads data. To display these as an interactive dashboard, we need some additional code and Vizro-AI can generate this for us, but not through the PyCafe host at the time of writing. We'll use a Jupyter Notebook
+At this point, we have prototypes for three plotly charts for the Goodreads data. To display these as an interactive dashboard, we need some additional code and Vizro-AI can generate this for us, but not through the application hosted on PyCafe at the time of writing. We'll use a Jupyter Notebook
 
 Before running the Notebook code, [set up Vizro-AI](https://vizro.readthedocs.io/projects/vizro-ai/en/latest/pages/user-guides/install/) inside a virtual environment with Python 3.10 or later. Install the package with `pip install vizro_ai`.
 

From 9374244ec64352499822e995bb66020154a411b6 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 16 Jan 2025 09:45:43 +0000
Subject: [PATCH 15/44] Put prompts in copyable blocks of text

---
 .../docs/pages/tutorials/project-tutorial.md  | 26 ++++++++++++++++---
 1 file changed, 22 insertions(+), 4 deletions(-)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index da3f87892..ff59e25d3 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -37,7 +37,11 @@ We can now dive use Vizro-AI to build some charts by iterating text to write eff
 
 To ask Vizro-AI to build a chart, we describe what we want to see. This chart should show an ordered horiontal timeline to illustrate the sequence of reading the books.
 
-> Plot a chart with the title "Sequence of reading" . It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
+```
+Plot a chart with the title "Sequence of reading" . 
+It is a scatter chart. Use the x axis to show the date a book was read. 
+Plot it at y=1.
+```
 
 We can adjust the model used: the chart below was generated from `gpt-4o-mini`. The chart displays on the right hand side of the screen and the Plotly code to generate the chart is below the prompt.
 
@@ -61,9 +65,18 @@ The plot this code returns looks as follows. Hovering over each point gave the d
 
 The second chart should plot the cumulative total of pages read and the cumulative total of books read per year. The prompt took a few iterations to refine:
 
-> Plot a chart with the title "Pages and Book totals" . It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data. Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.
+```
+Plot a chart with the title "Pages and Book totals" . 
+It shows the cumulative total number of pages read by summing the 
+Number of Pages of each book read in each year, using the Date Read data. 
+Plot date on the x axis and the number of pages on the y axis 
+using a scale on the left hand side of the chart.
 
-> Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side.
+Superimpose a bar chart showing the total books read for each year, 
+taking data from the Date Read column. 
+Show the total books read using the right hand side of the chart, 
+which can be a different scale to the y axis shown on the left hand side.
+```
 
 The Plotly code generated when run with `gpt-4-turbo` was as follows:
 
@@ -123,7 +136,12 @@ The plot this code returns looks as follows: ![](../../assets/tutorials/project/
 
 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. It was run several times before it colored each line differently, which is a key learning when using generative AI: your results will vary from run to run.
 
-> For each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book - use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.
+```
+For each row, create a dumbbell chart to show the difference 
+between My Rating and Average Rating for each book.
+Use shapes to add the horizontal lines between markers. 
+Omit the legend. Don't show any row where My Rating is 0.
+```
 
 ??? example "Click to expand"
     === "Generated by Vizro-AI"

From d8312131b40e52cdc9420cc82b659c4c559b2c76 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 09:47:35 +0000
Subject: [PATCH 16/44] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 .../docs/pages/tutorials/project-tutorial.md  | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index ff59e25d3..fcf0c045a 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -38,8 +38,8 @@ We can now dive use Vizro-AI to build some charts by iterating text to write eff
 To ask Vizro-AI to build a chart, we describe what we want to see. This chart should show an ordered horiontal timeline to illustrate the sequence of reading the books.
 
 ```
-Plot a chart with the title "Sequence of reading" . 
-It is a scatter chart. Use the x axis to show the date a book was read. 
+Plot a chart with the title "Sequence of reading" .
+It is a scatter chart. Use the x axis to show the date a book was read.
 Plot it at y=1.
 ```
 
@@ -66,15 +66,15 @@ The plot this code returns looks as follows. Hovering over each point gave the d
 The second chart should plot the cumulative total of pages read and the cumulative total of books read per year. The prompt took a few iterations to refine:
 
 ```
-Plot a chart with the title "Pages and Book totals" . 
-It shows the cumulative total number of pages read by summing the 
-Number of Pages of each book read in each year, using the Date Read data. 
-Plot date on the x axis and the number of pages on the y axis 
+Plot a chart with the title "Pages and Book totals" .
+It shows the cumulative total number of pages read by summing the
+Number of Pages of each book read in each year, using the Date Read data.
+Plot date on the x axis and the number of pages on the y axis
 using a scale on the left hand side of the chart.
 
-Superimpose a bar chart showing the total books read for each year, 
-taking data from the Date Read column. 
-Show the total books read using the right hand side of the chart, 
+Superimpose a bar chart showing the total books read for each year,
+taking data from the Date Read column.
+Show the total books read using the right hand side of the chart,
 which can be a different scale to the y axis shown on the left hand side.
 ```
 
@@ -137,9 +137,9 @@ The plot this code returns looks as follows: ![](../../assets/tutorials/project/
 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. It was run several times before it colored each line differently, which is a key learning when using generative AI: your results will vary from run to run.
 
 ```
-For each row, create a dumbbell chart to show the difference 
+For each row, create a dumbbell chart to show the difference
 between My Rating and Average Rating for each book.
-Use shapes to add the horizontal lines between markers. 
+Use shapes to add the horizontal lines between markers.
 Omit the legend. Don't show any row where My Rating is 0.
 ```
 

From f225c7937d71a77a7a32a539d1761d613138c57c Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 16 Jan 2025 10:38:17 +0000
Subject: [PATCH 17/44] Few copy edit improvements

---
 .../docs/pages/tutorials/project-tutorial.md  | 40 +++++++++----------
 1 file changed, 19 insertions(+), 21 deletions(-)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index ff59e25d3..aff692e19 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -2,11 +2,11 @@
 
 This tutorial uses Vizro-AI to build a prototype dashboard with three charts that illustrate a simple dataset. We first show how to create individual charts with Vizro-AI and then move on to learn how to use Vizro-AI to build a dashboard. The tutorial concludes by moving the prototype code generated by Vizro-AI into a project on PyCafe, for others to use and extend.
 
-## Project background and data
+## 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). If you use Goodreads, you can export your own data in CSV format and use 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). <!--TO DO -- This will change when data is uploaded to Vizro repo-->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, since that 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 used can be downloaded from the [Vizro repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv). \<!--TO DO -- This will change when data is uploaded to Vizro repo-->
+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
 
@@ -17,25 +17,23 @@ This tutorial uses OpenAI models with Vizro-AI. To run through the steps, you mu
 
 ## Chart generation with Vizro-AI
 
-In this step, we'll show a UI on a hosted version of Vizro-AI, at [https://py.cafe/app/vizro-official/vizro-ai-charts](https://py.cafe/app/vizro-official/vizro-ai-charts). Use your browser to navigate to the site which looks as follows:
+In this step, we use a UI on a hosted version of Vizro-AI, found at [https://py.cafe/app/vizro-official/vizro-ai-charts](https://py.cafe/app/vizro-official/vizro-ai-charts). Use your browser to navigate to the site which looks as follows:
 
 ![](../../assets/tutorials/project/user-interface-hosted-vizro-ai.png)
 
 ### Settings
 
-You'll notice a cog icon at the top right hand corner for access to your settings, which look as follows:
+The link will open with a settings pane where you can set the API key for your chosen vendor. At the time of writing, you can use OpenAI, Anthropic, Mistral, or xAI:
 
 ![](../../assets/tutorials/project/user-interface-settings-hosted-vizro-ai.png)
 
-Add the API key for your chosen vendor. At the time of writing, you can use OpenAI, Anthropic, Mistral, or xAI.
+To return to these settings at any time, you'll notice a cog icon at the top right hand corner for access to them. Once the API key is set, return to the main screen and upload the data for the project.
 
-Once the API key is set, return to the main screen and upload the data for the project.
-
-We can now dive use Vizro-AI to build some charts by iterating text to write effective prompts.
+We can now use Vizro-AI to build some charts by iterating text to form effective prompts.
 
 ### Chart 1: Books timeline
 
-To ask Vizro-AI to build a chart, we describe what we want to see. This chart should show an ordered horiontal timeline to illustrate the sequence of reading the books.
+To ask Vizro-AI to build a chart, we describe what we want to see. The first chart should show an ordered horiontal timeline to illustrate the sequence of reading the books.
 
 ```
 Plot a chart with the title "Sequence of reading" . 
@@ -43,9 +41,9 @@ It is a scatter chart. Use the x axis to show the date a book was read.
 Plot it at y=1.
 ```
 
-We can adjust the model used: the chart below was generated from `gpt-4o-mini`. The chart displays on the right hand side of the screen and the Plotly code to generate the chart is below the prompt.
+We can adjust the model used depending on the results, since more complex prompts need more powerful models. This chart was generated from `gpt-4o-mini`. It displays on the right hand side of the screen and the Plotly code to generate the chart is shown below the prompt.
 
-The plot this code returns looks as follows. Hovering over each point gave the date the book was read, but not the title of the book. You'll also notice that the points are spaced evenly rather than proportionately: ![](../../assets/tutorials/project/chart1.png)
+The plot this code returns looks as follows. Hovering over each point gave the date the book was read, but not the title of the book, although this could be achieved by tweaking the prompt to ask explicitly for particular fields in the hover text. You'll also notice that the points are spaced evenly rather than proportionately: ![](../../assets/tutorials/project/chart1.png)
 
 ??? example "Click to expand"
     === "Generated by Vizro-AI"
@@ -78,7 +76,7 @@ Show the total books read using the right hand side of the chart,
 which can be a different scale to the y axis shown on the left hand side.
 ```
 
-The Plotly code generated when run with `gpt-4-turbo` was as follows:
+The Plotly code generated when run with `gpt-4-turbo` was as follows. The chart could be improved by bringing the line graph on top of the bar chart, but varying the prompt to make this explicit did not have the desired results:
 
 ??? example "Click to expand"
     === "Generated by Vizro-AI"
@@ -134,7 +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. It was run several times before it colored each line differently, which is a key learning when using generative AI: your results will vary from run to run.
+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 
@@ -192,7 +190,7 @@ The plot this code returns looks as follows: ![](../../assets/tutorials/project/
 
 ### Set up a Jupyter Notebook
 
-At this point, we have prototypes for three plotly charts for the Goodreads data. To display these as an interactive dashboard, we need some additional code and Vizro-AI can generate this for us, but not through the application hosted on PyCafe at the time of writing. We'll use a Jupyter Notebook
+At this point, we have prototypes for three plotly charts for the Goodreads data. To display these as an interactive dashboard, we need some additional code and Vizro-AI can generate this for us, but not through the application hosted on PyCafe at the time of writing. We'll use a Jupyter Notebook instead.
 
 Before running the Notebook code, [set up Vizro-AI](https://vizro.readthedocs.io/projects/vizro-ai/en/latest/pages/user-guides/install/) inside a virtual environment with Python 3.10 or later. Install the package with `pip install vizro_ai`.
 
@@ -200,9 +198,9 @@ You need to give Vizro-AI your API key to access OpenAI by adding it to your env
 
 ### Build a dashboard
 
-Now we can open a Jupyter Notebook to make the dashboard. We'll submit a prompt that combines the three prompts listed above, with some small edits to ask for a dashboard that has three pages: one for each chart.
+Now we can open a Jupyter Notebook to make the dashboard. We'll submit a single prompt that combines the three prompts listed above, with some small edits to ask for a dashboard that has three pages: one for each chart.
 
-The following code shows the code to make the request to Vizro-AI to build and display the dashboard. The Notebook is available for download from [Vizro's GitHub repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv): <!-- TO DO change this when uploaded -->
+The following shows the code to make the request to Vizro-AI to build and display the dashboard. The Notebook is available for download from [Vizro's GitHub repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv): <!-- TO DO change this when uploaded -->
 
 ??? example "Click to expand"
     === "Generated by Vizro-AI"
@@ -230,9 +228,9 @@ The following code shows the code to make the request to Vizro-AI to build and d
 
 Using `gpt-4-turbo`, Vizro-AI generates a set of plotly chart code and the necessary Vizro support code to build a dashboard. The generated code is displayed as output in the Notebook with the dashboard, although the dashboard is better viewed at `http://localhost:8006/`.
 
-You can see the code output in the [Notebook stored on the Vizro GitHub repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/goodreads.ipynb) <!--TO DO change this when uploaded-->, or generate similar output by running it yourself.
+You can see the code output in the [Notebook stored on the Vizro GitHub repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/goodreads.ipynb)<!--TO DO change this when uploaded-->, or generate similar output by running it yourself, although it will not necessarily be identical because of the variability of restuls returned from generative AI.
 
-The charts generated were similar to those created by the PyCafe host above, although the first chart was improved by spacing the books proportionate to the date read and rolling over each point gave the book title as well as the date read.
+The charts generated were similar to those created by the PyCafe host above, although the first chart was improved by spacing the books proportionate to the date read and the hover text included the book title as well as the date read, without an explicit request to do so.
 
 ![](../../assets/tutorials/project/chart1v2.png)
 
@@ -281,7 +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) \<!-- TO DO -- change this --> 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)<!-- TO DO -- change this --> 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.
@@ -472,7 +470,7 @@ Finally, we converted the Vizro-AI generated Python to a PyCafe project with a f
 
 The code generated by Vizro-AI does not make a perfect dashboard, but it has been very easy to get it to a good standard without a great deal of coding or design experience. There are a few improvements that are easily applied to the Plotly code to further improve the charts, as shown below in a separate PyCafe project.
 
-### Improvements to generated code
+### Improvements to generated Plotly code
 
 In this version of the dashboard, the first chart that show the sequence of books read has been modified to improve the information supplied when hovering over a point, and the opacity of the points has been altered to make it more attractive.
 

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 18/44] [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). <!--TO DO -- This will change when data is uploaded to Vizro repo-->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). \<!--TO DO -- This will change when data is uploaded to Vizro repo-->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)<!-- TO DO -- change this --> 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)\<!-- TO DO -- change this --> 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.

From 21b2b5a125e4cd0b7a9c7aabe5daab2d553e6226 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 16 Jan 2025 10:39:29 +0000
Subject: [PATCH 19/44] Linting

---
 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). <!--TO DO -- This will change when data is uploaded to Vizro repo-->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). \<!--TO DO -- This will change when data is uploaded to Vizro repo-->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)<!-- TO DO -- change this --> 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)\<!-- TO DO -- change this --> 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.

From 4e4996c89e6ed235bc4dd76bc557230d8abeb1b3 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 16 Jan 2025 12:46:39 +0000
Subject: [PATCH 20/44] Change rules of repo for data files

---
 .gitignore                   |  1 +
 tools/check_for_datafiles.py | 48 ------------------------------------
 2 files changed, 1 insertion(+), 48 deletions(-)
 delete mode 100644 tools/check_for_datafiles.py

diff --git a/.gitignore b/.gitignore
index 79a54175f..5fc87c51c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -150,6 +150,7 @@ cython_debug/
 
 # Ignore data files except for white-listed folders
 *.csv
+!vizro-ai/examples/goodreads_tutorial/*.csv
 *.xls
 *.xlsx
 *.parquet
diff --git a/tools/check_for_datafiles.py b/tools/check_for_datafiles.py
deleted file mode 100644
index c34114ff6..000000000
--- a/tools/check_for_datafiles.py
+++ /dev/null
@@ -1,48 +0,0 @@
-"""FUNCTION TO CHECK FOR DATA FILES IN NON-WHITELISTED FOLDERS."""
-
-import glob
-from pathlib import Path
-
-file_extensions = [
-    "csv",
-    "xlsx",
-    "xls",
-    "parquet",
-    "tsv",
-    "hdf5",
-    "h5",
-    "pickle",
-    "pkl",
-    "db",
-    "sqlite",
-    "sqlite3",
-    "orc",
-]
-whitelist_folders = [
-    "/venv",
-    "/vizro-core/docs",
-    "/vizro-ai/tests/integration/reports",
-]  # starting from project root dir
-
-
-def check_for_data_files():
-    """Recursively finds all data files in non-whitelisted folders.
-
-    Raises:
-        AssertionError if data files are present in non-whitelisted folders.
-
-    """
-    project_dir = str(Path(__file__).parent.parent)
-    whitelist_dir = {f"{project_dir}{dir}" for dir in whitelist_folders}
-
-    found_files = {file for ext in file_extensions for file in glob.glob(project_dir + f"/**/*.{ext}", recursive=True)}
-    whitelisted_files = {files for dir in whitelist_dir for files in found_files if files.startswith(dir)}
-    to_be_removed_files = found_files - whitelisted_files
-
-    assert (
-        len(to_be_removed_files) == 0
-    ), f"Caution! Please remove your data files {to_be_removed_files} before merging!"
-
-
-if __name__ == "__main__":
-    check_for_data_files()

From 834c8d9c198420e8b315c0dc045b7aa12534719d Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 16 Jan 2025 13:16:15 +0000
Subject: [PATCH 21/44] Add Goodreads project and data to examples

---
 .../goodreads_tutorial/filtered_books.csv     |  57 ++
 .../goodreads_tutorial/goodreads.ipynb        | 682 ++++++++++++++++++
 2 files changed, 739 insertions(+)
 create mode 100644 vizro-ai/examples/goodreads_tutorial/filtered_books.csv
 create mode 100644 vizro-ai/examples/goodreads_tutorial/goodreads.ipynb

diff --git a/vizro-ai/examples/goodreads_tutorial/filtered_books.csv b/vizro-ai/examples/goodreads_tutorial/filtered_books.csv
new file mode 100644
index 000000000..a93c927ed
--- /dev/null
+++ b/vizro-ai/examples/goodreads_tutorial/filtered_books.csv
@@ -0,0 +1,57 @@
+Title,Author,Author l-f,Additional Authors,ISBN,My Rating,Average Rating,Number of Pages,Year Published,Original Publication Year,Date Read
+Milk Teeth,Jessica  Andrews,"Andrews, Jessica",,1473682835,0,3.66,251,2022,2022,01/01/2023
+The Mad Women's Ball,Victoria Mas,"Mas, Victoria",Frank Wynne,1473575672,0,3.78,214,2021,2019,11/01/2023
+The Followers,Rebecca Wait,"Wait, Rebecca",,1447224760,0,3.9,347,2015,2015,15/01/2023
+In Little Stars,Linda Green,"Green, Linda",,1787478777,0,4.47,387,2022,,22/01/2023
+The Beloved Girls,Harriet Evans,"Evans, Harriet",,1472251075,3,3.65,520,2021,2021,02/02/2023
+The Vorrh,Brian Catling,"Catling, Brian",,1473606608,0,3.46,514,2015,2012,09/02/2023
+Kololo Hill,Neema Shah,"Shah, Neema",,1529030528,0,3.91,353,2021,,24/02/2023
+Small Pleasures,Clare Chambers,"Chambers, Clare",,1474613918,5,3.77,301,2020,2021,15/03/2023
+Greenwich Park,Katherine Faulkner,"Faulkner, Katherine",,152662625X,2,3.84,383,2021,2021,31/03/2023
+Don't Close Your Eyes,P.S. Cunliffe,"Cunliffe, P.S.",,1471413209,3,4.01,396,2023,2023,10/04/2023
+Among the Lesser Gods,Margo Catts,"Catts, Margo",,1628727403,4,4.12,338,2017,2017,16/04/2023
+Winter People,Gráinne Murphy,"Murphy, Gráinne",,1915054362,4,4.1,272,2022,,27/04/2023
+We Had to Remove This Post,Hanna Bervoets,"Bervoets, Hanna",Emma Rault,1529087252,4,2.93,144,2022,2021,02/05/2023
+The Schoolhouse,Sophie Ward,"Ward, Sophie",,1472156293,3,3.29,304,2023,2022,28/05/2023
+Victoria Park,Gemma Reeves,"Reeves, Gemma",,176087406X,3,3.6,227,2021,2021,04/06/2023
+Your Child is Not Broken: Parent Your Neurodivergent Child Without Losing Your Marbles,Heidi Mavir,"Mavir, Heidi",,1035030985,5,4.41,281,2023,,07/06/2023
+Sea Defences,Hilary  Taylor,"Taylor, Hilary",,178563366X,5,4.3,337,2022,,27/06/2023
+No One Saw a Thing,Andrea Mara,"Mara, Andrea",,1529197899,1,3.98,378,2023,2023,07/07/2023
+Nobber,Oisín Fagan,"Fagan, Oisín",,1529389100,0,3.58,304,2019,2019,17/07/2023
+Ginger and Me,Elissa Soave,"Soave, Elissa",,000845843X,5,3.85,384,2022,2022,07/08/2023
+The Queen of Dirt Island,Donal Ryan,"Ryan, Donal",,1473544602,5,4.08,248,2022,2022,17/08/2023
+The Island of Longing,Anne  Griffin,"Griffin, Anne",,1529372062,4,4.15,307,2023,2023,26/08/2023
+The Secret of Elephants,Vasundra Tailor,"Tailor, Vasundra",,1542038200,0,4.09,305,2022,2022,29/09/2023
+Strange Flowers,Donal Ryan,"Ryan, Donal",,1473544610,4,3.81,218,2020,2020,11/09/2023
+Mrs England,Stacey Halls,"Halls, Stacey",,1838772898,0,3.91,338,2021,2021,02/10/2023
+The Promise,Lucy Diamond,"Diamond, Lucy",,1529027055,3,4.02,480,2021,2020,07/10/2023
+Haven,Emma Donoghue,"Donoghue, Emma",,1529091179,4,3.42,289,2022,2022,10/10/2023
+The Lost Apothecary,Sarah Penner,"Penner, Sarah",,1789558980,2,3.75,320,2021,2021,27/10/2023
+Prize Women,Caroline Lea,"Lea, Caroline",,140594899X,0,3.93,438,2023,2023,11/11/2023
+Daughters of Night,Laura Shepherd-Robinson,"Shepherd-Robinson, Laura",,1509880852,4,4.17,498,2021,2021,22/11/2023
+Saltwater,Jessica  Andrews,"Andrews, Jessica",,1473682797,0,3.79,304,2019,2019,27/11/2023
+Shuggie Bain,Douglas   Stuart,"Stuart, Douglas",,1529019303,0,4.31,449,2020,2020,29/11/2023
+The Other Mothers,Katherine Faulkner,"Faulkner, Katherine",,1526626284,4,3.84,356,2023,2023,17/12/2023
+The Fell,Sarah Moss,"Moss, Sarah",,1529083257,0,3.63,193,2021,2021,29/12/2023
+Co-Active Coaching: The proven framework for transformative conversations at work and in life,Henry Kimsey-House,"Kimsey-House, Henry","Karen Kimsey-House, Phillip Sandahl, Laura Whitworth, Alexis Phillips",1473691133,5,4.2,241,2018,1998,29/12/2023
+The Step-by-Step Guide to Sourdough,Carroll Pellegrinelli,"Pellegrinelli, Carroll",,1641521651,2,4.03,226,2019,,01/01/2024
+Heartsick,Jessie Stephens,"Stephens, Jessie",,1529084229,2,4,333,2022,2021,14/01/2024
+The Echo Chamber,John Boyne,"Boyne, John",,1473563321,1,3.87,432,2021,2021,03/03/2024
+The Turnglass,Gareth Rubin,"Rubin, Gareth",,1398514519,4,3.49,512,2023,2023,30/03/2024
+Green Dot,Madeleine Gray,"Gray, Madeleine",,1399612794,4,3.7,376,2024,2024,09/04/2024
+In Memoriam,Alice  Winn,"Winn, Alice",,024156784X,0,4.53,380,2023,2023,14/04/2024
+This Could Be Us,Claire McGowan,"McGowan, Claire",,1472157206,0,3.97,336,2023,,07/05/2024
+One of the Good Guys,Araminta Hall,"Hall, Araminta",,1035018136,0,3.51,324,2024,2024,27/05/2024
+Everyone Here Is Lying,Shari Lapena,"Lapena, Shari",,1473598567,0,3.8,404,2023,2023,07/06/2024
+The Man Who Didn't Call,Rosie Walsh,"Walsh, Rosie",,150982829X,3,3.75,353,2018,2018,22/06/2024
+The Wrong Daughter,Dandy Smith,"Smith, Dandy",,1471414639,3,4.09,353,2024,2024,29/06/2024
+Earth,John Boyne,"Boyne, John",,152991650X,4,4.31,168,2024,,30/06/2024
+The Satsuma Complex,Bob Mortimer,"Mortimer, Bob",,1398521213,0,3.68,301,2022,2022,07/07/2024
+Verity,Colleen Hoover,"Hoover, Colleen",,1408726610,0,4.31,333,2021,2018,09/07/2024
+Talking at Night,Claire Daverley,"Daverley, Claire",,1405953861,5,3.96,389,2023,2023,07/08/2024
+Maybe in Another Life,Taylor Jenkins Reid,"Reid, Taylor Jenkins",,139851666X,2,3.79,349,2022,2015,20/08/2024
+"Are You There, God? It's Me, Margaret",Judy Blume,"Blume, Judy",,1447280350,4,3.94,224,2014,1970,01/09/2024
+None of This Is True,Lisa Jewell,"Jewell, Lisa",,1804940224,1,4.12,400,2023,2023,07/09/2024
+Africa Is Not A Country: Breaking Stereotypes of Modern Africa,Dipo Faloyin,"Faloyin, Dipo",,1473593433,4,4.43,340,2022,2022,09/09/2024
+After the Funeral,Tessa Hadley,"Hadley, Tessa",,1473595290,4,3.82,226,2023,2023,21/09/2024
+Strong Female Character,Fern  Brady,"Brady, Fern",,1914240464,4,4.47,288,2023,2023,31/10/2024
\ No newline at end of file
diff --git a/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb b/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb
new file mode 100644
index 000000000..a1d48e716
--- /dev/null
+++ b/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb
@@ -0,0 +1,682 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "150de2e4-622f-4d0f-91a2-ad47dd16c53a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Vizro-AI setup\n",
+    "\n",
+    "from vizro import Vizro\n",
+    "import vizro_ai\n",
+    "from vizro_ai import VizroAI\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "# Ensure the API key is in .env\n",
+    "load_dotenv()\n",
+    "\n",
+    "# Choose your model\n",
+    "\n",
+    "vizro_ai = VizroAI(model=\"gpt-4-turbo\")\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv('filtered_books.csv')\n",
+    "df[\"Date Read\"] = pd.to_datetime(df['Date Read'],dayfirst=True)\n",
+    "\n",
+    "# Data cleanup \n",
+    "# Specify columns to check for missing values\n",
+    "columns_to_check = ['Title', 'Author', 'ISBN', 'My Rating', 'Average Rating', 'Number of Pages', 'Original Publication Year', 'Date Read']\n",
+    "df_cleaned = df.dropna(subset=columns_to_check)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1cbbea13-28f1-47fc-9348-9e41ba178463",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specifiy the prompt here\n",
+    "user_question = \"\"\"\n",
+    "Create a dashboard with 3 pages, one for each chart. \n",
+    "\n",
+    "On the first page, plot a chart with the title \"Sequence of reading\" . \n",
+    "It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.\n",
+    "Add a filter so the user can adjust the range of dates by year on the x axis.\n",
+    "\n",
+    "On the second page, plot a chart with the title \"Pages and Book totals\" . \n",
+    "It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data. \n",
+    "Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.\n",
+    "Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. \n",
+    "Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side. \n",
+    "Add a filter so the user can adjust the range of dates by year on the x axis.\n",
+    "\n",
+    "On the third page, for each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book.\n",
+    "Use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "14e9f5de-1ace-44cd-8b3a-3f1ee805fe2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72dc4d9d9a6748d4b651dc76197bc8b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Store df info:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df_name: book_reading_data\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67e5561620f4492b8557bf061d889223",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generate dashboard plan:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e08ef3dd117d4d6e8294677745c3f730",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Building page: Rating Comparison:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b60b963928d4395963c8f06ad1f8bd5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Building page: Sequence of Reading:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f948c5ba88341f99adbd748ec3f351b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Building page: Pages and Book Totals:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16f97a6a06c0443592412812b500a3af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7e77b4c32c9446ab4f94ca1e5173f52",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9781bf14e6d44840ae2d58171b205f63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1294cfce21f247f7953d9f143c26fe90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Currently Building ... [Page] <Rating Comparison> components:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9733c9c278045fea3a58e710c97f01a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Currently Building ... [Page] <Pages and Book Totals> components:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b58b1a6868134a7ebc0114a774563c1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Currently Building ... [Page] <Sequence of Reading> components:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "164b169d92124c68a27560ce1bfd193f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Currently Building ... [Page] <Sequence of Reading> controls:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pydantic/v1/schema.py:631: UserWarning:\n",
+      "\n",
+      "CapturedCallable function is excluded from the schema.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a13b9b44161e4d94a3bc301263b42f64",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Currently Building ... [Page] <Rating Comparison> controls: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5055cf7208594dbe8bf0db4621ffff25",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Currently Building ... [Page] <Pages and Book Totals> controls:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pydantic/v1/schema.py:631: UserWarning:\n",
+      "\n",
+      "CapturedCallable function is excluded from the schema.\n",
+      "\n",
+      "WARNING:root:Could not extract source for <function sequence_reading at 0x144bd04c0>. Definition will not be included.\n",
+      "WARNING:root:Could not extract source for <function rating_comparison at 0x144c7be20>. Definition will not be included.\n",
+      "WARNING:root:Could not extract source for <function pages_books_totals at 0x144cebc70>. Definition will not be included.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"100%\"\n",
+       "            height=\"650\"\n",
+       "            src=\"http://127.0.0.1:8006/\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x144cbbd30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "############ Imports ##############\n",
+      "import vizro.models as vm\n",
+      "from vizro.models.types import capture\n",
+      "import pandas as pd\n",
+      "import plotly.graph_objects as go\n",
+      "from vizro.models.types import capture\n",
+      "\n",
+      "\n",
+      "####### Function definitions ######\n",
+      "@capture(\"graph\")\n",
+      "def sequence_reading(data_frame):\n",
+      "    fig = go.Figure()\n",
+      "    fig.add_trace(\n",
+      "        go.Scatter(\n",
+      "            x=data_frame[\"Date Read\"],\n",
+      "            y=[1] * len(data_frame),\n",
+      "            mode=\"markers\",\n",
+      "            marker=dict(size=10, color=\"blue\"),\n",
+      "        )\n",
+      "    )\n",
+      "    fig.update_layout(\n",
+      "        title=\"Sequence of reading\",\n",
+      "        xaxis_title=\"Date Read\",\n",
+      "        yaxis_title=\"Sequence\",\n",
+      "        yaxis=dict(showticklabels=False, showgrid=False),\n",
+      "        xaxis=dict(tickangle=-45),\n",
+      "    )\n",
+      "    return fig\n",
+      "\n",
+      "\n",
+      "@capture(\"graph\")\n",
+      "def pages_books_totals(data_frame):\n",
+      "    # Prepare data\n",
+      "    data_frame[\"Date Read\"] = pd.to_datetime(data_frame[\"Date Read\"])\n",
+      "    data_frame.sort_values(\"Date Read\", inplace=True)\n",
+      "    data_frame[\"Cumulative Pages\"] = data_frame[\"Number of Pages\"].cumsum()\n",
+      "\n",
+      "    # Aggregate data by year for total books read\n",
+      "    yearly_books = data_frame.groupby(data_frame[\"Date Read\"].dt.year).size()\n",
+      "\n",
+      "    # Create figure with secondary y-axis\n",
+      "    fig = go.Figure()\n",
+      "\n",
+      "    # Add line for cumulative pages\n",
+      "    fig.add_trace(\n",
+      "        go.Scatter(\n",
+      "            x=data_frame[\"Date Read\"],\n",
+      "            y=data_frame[\"Cumulative Pages\"],\n",
+      "            mode=\"lines\",\n",
+      "            name=\"Cumulative Pages Read\",\n",
+      "        )\n",
+      "    )\n",
+      "\n",
+      "    # Add bars for books read per year\n",
+      "    fig.add_trace(\n",
+      "        go.Bar(\n",
+      "            x=yearly_books.index, y=yearly_books, name=\"Books Read per Year\", yaxis=\"y2\"\n",
+      "        )\n",
+      "    )\n",
+      "\n",
+      "    # Set up the layout\n",
+      "    fig.update_layout(\n",
+      "        title=\"Cumulative Pages Read and Books Read per Year\",\n",
+      "        xaxis_title=\"Date\",\n",
+      "        yaxis=dict(title=\"Number of Pages\"),\n",
+      "        yaxis2=dict(title=\"Total Books\", overlaying=\"y\", side=\"right\"),\n",
+      "    )\n",
+      "\n",
+      "    return fig\n",
+      "\n",
+      "\n",
+      "@capture(\"graph\")\n",
+      "def rating_comparison(data_frame):\n",
+      "    # Filter out rows where 'My Rating' is 0\n",
+      "    df = data_frame[data_frame[\"My Rating\"] != 0]\n",
+      "\n",
+      "    # Create a figure\n",
+      "    fig = go.Figure()\n",
+      "\n",
+      "    # Add dumbbell traces\n",
+      "    for index, row in df.iterrows():\n",
+      "        fig.add_trace(\n",
+      "            go.Scatter(\n",
+      "                x=[row[\"My Rating\"], row[\"Average Rating\"]],\n",
+      "                y=[row[\"Title\"], row[\"Title\"]],\n",
+      "                mode=\"markers+lines\",\n",
+      "                name=row[\"Title\"],\n",
+      "                marker=dict(size=10),\n",
+      "                line=dict(width=2),\n",
+      "            )\n",
+      "        )\n",
+      "\n",
+      "    # Update layout\n",
+      "    fig.update_layout(\n",
+      "        title=\"Comparison of My Rating vs Average Rating\",\n",
+      "        xaxis_title=\"Rating\",\n",
+      "        yaxis_title=\"Book Title\",\n",
+      "        showlegend=False,\n",
+      "    )\n",
+      "\n",
+      "    return fig\n",
+      "\n",
+      "\n",
+      "####### Data Manager Settings #####\n",
+      "#######!!! UNCOMMENT BELOW !!!#####\n",
+      "# from vizro.managers import data_manager\n",
+      "# data_manager[\"book_reading_data\"] = ===> Fill in here <===\n",
+      "\n",
+      "\n",
+      "########### Model code ############\n",
+      "model = vm.Dashboard(\n",
+      "    pages=[\n",
+      "        vm.Page(\n",
+      "            components=[\n",
+      "                vm.Graph(\n",
+      "                    id=\"sequence_reading\",\n",
+      "                    figure=sequence_reading(data_frame=\"book_reading_data\"),\n",
+      "                )\n",
+      "            ],\n",
+      "            title=\"Sequence of Reading\",\n",
+      "            layout=vm.Layout(grid=[[0]]),\n",
+      "            controls=[\n",
+      "                vm.Filter(\n",
+      "                    column=\"Year Published\",\n",
+      "                    targets=[\"sequence_reading\"],\n",
+      "                    selector=vm.RangeSlider(type=\"range_slider\"),\n",
+      "                )\n",
+      "            ],\n",
+      "        ),\n",
+      "        vm.Page(\n",
+      "            components=[\n",
+      "                vm.Graph(\n",
+      "                    id=\"pages_books_totals\",\n",
+      "                    figure=pages_books_totals(data_frame=\"book_reading_data\"),\n",
+      "                )\n",
+      "            ],\n",
+      "            title=\"Pages and Book Totals\",\n",
+      "            layout=vm.Layout(grid=[[0]]),\n",
+      "            controls=[\n",
+      "                vm.Filter(\n",
+      "                    column=\"Year Published\",\n",
+      "                    targets=[\"pages_books_totals\"],\n",
+      "                    selector=vm.RangeSlider(type=\"range_slider\"),\n",
+      "                )\n",
+      "            ],\n",
+      "        ),\n",
+      "        vm.Page(\n",
+      "            components=[\n",
+      "                vm.Graph(\n",
+      "                    id=\"rating_comparison\",\n",
+      "                    figure=rating_comparison(data_frame=\"book_reading_data\"),\n",
+      "                )\n",
+      "            ],\n",
+      "            title=\"Rating Comparison\",\n",
+      "            layout=vm.Layout(grid=[[0]]),\n",
+      "            controls=[],\n",
+      "        ),\n",
+      "    ],\n",
+      "    title=\"Book Reading Analysis Dashboard\",\n",
+      ")\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR:__main__:Exception on /_dash-update-component [POST]\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/flask/app.py\", line 1473, in wsgi_app\n",
+      "    response = self.full_dispatch_request()\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/flask/app.py\", line 882, in full_dispatch_request\n",
+      "    rv = self.handle_user_exception(e)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/flask/app.py\", line 880, in full_dispatch_request\n",
+      "    rv = self.dispatch_request()\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/flask/app.py\", line 865, in dispatch_request\n",
+      "    return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)  # type: ignore[no-any-return]\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/dash/dash.py\", line 1376, in dispatch\n",
+      "    ctx.run(\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/dash/_callback.py\", line 507, in add_context\n",
+      "    raise err\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/dash/_callback.py\", line 496, in add_context\n",
+      "    output_value = _invoke_callback(func, *func_args, **func_kwargs)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/dash/_callback.py\", line 43, in _invoke_callback\n",
+      "    return func(*args, **kwargs)  # %% callback invoked %%\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/models/_action/_action.py\", line 181, in callback_wrapper\n",
+      "    return_value = self._action_callback_function(inputs=external, outputs=callback_outputs.get(\"external\"))\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/models/_action/_action.py\", line 110, in _action_callback_function\n",
+      "    return_value = self.function(**inputs)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/models/types.py\", line 153, in __call__\n",
+      "    return self.__function(**{**self.__bound_arguments, **kwargs})\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/actions/_on_page_load_action.py\", line 25, in _on_page_load\n",
+      "    return _get_modified_page_figures(\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/actions/_actions_utils.py\", line 278, in _get_modified_page_figures\n",
+      "    filtered_data = _apply_filters(unfiltered_data, ctds_filter, ctds_filter_interaction, target)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/actions/_actions_utils.py\", line 224, in _apply_filters\n",
+      "    filtered_data = _apply_filter_controls(data_frame=data, ctds_filter=ctds_filter, target=target)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/actions/_actions_utils.py\", line 79, in _apply_filter_controls\n",
+      "    data_frame = data_frame[_filter_function(data_frame[_filter_column], _filter_value)]\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/models/_controls/filter.py\", line 62, in _filter_between\n",
+      "    return series.between(value[0], value[1], inclusive=\"both\")\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/series.py\", line 5638, in between\n",
+      "    lmask = self >= left\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/ops/common.py\", line 76, in new_method\n",
+      "    return method(self, other)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/arraylike.py\", line 60, in __ge__\n",
+      "    return self._cmp_method(other, operator.ge)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/series.py\", line 6119, in _cmp_method\n",
+      "    res_values = ops.comparison_op(lvalues, rvalues, op)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/ops/array_ops.py\", line 341, in comparison_op\n",
+      "    return invalid_comparison(lvalues, rvalues, op)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/ops/invalid.py\", line 40, in invalid_comparison\n",
+      "    raise TypeError(f\"Invalid comparison between dtype={left.dtype} and {typ}\")\n",
+      "TypeError: Invalid comparison between dtype=int64 and str\n",
+      "ERROR:__main__:Exception on /_dash-update-component [POST]\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/flask/app.py\", line 1473, in wsgi_app\n",
+      "    response = self.full_dispatch_request()\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/flask/app.py\", line 882, in full_dispatch_request\n",
+      "    rv = self.handle_user_exception(e)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/flask/app.py\", line 880, in full_dispatch_request\n",
+      "    rv = self.dispatch_request()\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/flask/app.py\", line 865, in dispatch_request\n",
+      "    return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)  # type: ignore[no-any-return]\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/dash/dash.py\", line 1376, in dispatch\n",
+      "    ctx.run(\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/dash/_callback.py\", line 507, in add_context\n",
+      "    raise err\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/dash/_callback.py\", line 496, in add_context\n",
+      "    output_value = _invoke_callback(func, *func_args, **func_kwargs)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/dash/_callback.py\", line 43, in _invoke_callback\n",
+      "    return func(*args, **kwargs)  # %% callback invoked %%\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/models/_action/_action.py\", line 181, in callback_wrapper\n",
+      "    return_value = self._action_callback_function(inputs=external, outputs=callback_outputs.get(\"external\"))\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/models/_action/_action.py\", line 110, in _action_callback_function\n",
+      "    return_value = self.function(**inputs)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/models/types.py\", line 153, in __call__\n",
+      "    return self.__function(**{**self.__bound_arguments, **kwargs})\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/actions/_on_page_load_action.py\", line 25, in _on_page_load\n",
+      "    return _get_modified_page_figures(\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/actions/_actions_utils.py\", line 278, in _get_modified_page_figures\n",
+      "    filtered_data = _apply_filters(unfiltered_data, ctds_filter, ctds_filter_interaction, target)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/actions/_actions_utils.py\", line 224, in _apply_filters\n",
+      "    filtered_data = _apply_filter_controls(data_frame=data, ctds_filter=ctds_filter, target=target)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/actions/_actions_utils.py\", line 79, in _apply_filter_controls\n",
+      "    data_frame = data_frame[_filter_function(data_frame[_filter_column], _filter_value)]\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/models/_controls/filter.py\", line 62, in _filter_between\n",
+      "    return series.between(value[0], value[1], inclusive=\"both\")\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/series.py\", line 5638, in between\n",
+      "    lmask = self >= left\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/ops/common.py\", line 76, in new_method\n",
+      "    return method(self, other)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/arraylike.py\", line 60, in __ge__\n",
+      "    return self._cmp_method(other, operator.ge)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/series.py\", line 6119, in _cmp_method\n",
+      "    res_values = ops.comparison_op(lvalues, rvalues, op)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/ops/array_ops.py\", line 341, in comparison_op\n",
+      "    return invalid_comparison(lvalues, rvalues, op)\n",
+      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/ops/invalid.py\", line 40, in invalid_comparison\n",
+      "    raise TypeError(f\"Invalid comparison between dtype={left.dtype} and {typ}\")\n",
+      "TypeError: Invalid comparison between dtype=int64 and str\n",
+      "<string>:9: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "<string>:10: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "<string>:11: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "<string>:9: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "<string>:10: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "<string>:11: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "<string>:9: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "<string>:10: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "<string>:11: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = vizro_ai.dashboard([df_cleaned], user_question, return_elements=True)\n",
+    "Vizro().build(result.dashboard).run(port=8006)\n",
+    "print(result.code)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c893170d-756b-4ca8-a3a2-d3646ac8e595",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From c846698a8b852a1d15e7789c60e49b1111254f8e 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 13:16:43 +0000
Subject: [PATCH 22/44] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 .../goodreads_tutorial/filtered_books.csv     |  2 +-
 .../goodreads_tutorial/goodreads.ipynb        | 23 +++++++++++++------
 2 files changed, 17 insertions(+), 8 deletions(-)

diff --git a/vizro-ai/examples/goodreads_tutorial/filtered_books.csv b/vizro-ai/examples/goodreads_tutorial/filtered_books.csv
index a93c927ed..33086702b 100644
--- a/vizro-ai/examples/goodreads_tutorial/filtered_books.csv
+++ b/vizro-ai/examples/goodreads_tutorial/filtered_books.csv
@@ -54,4 +54,4 @@ Maybe in Another Life,Taylor Jenkins Reid,"Reid, Taylor Jenkins",,139851666X,2,3
 None of This Is True,Lisa Jewell,"Jewell, Lisa",,1804940224,1,4.12,400,2023,2023,07/09/2024
 Africa Is Not A Country: Breaking Stereotypes of Modern Africa,Dipo Faloyin,"Faloyin, Dipo",,1473593433,4,4.43,340,2022,2022,09/09/2024
 After the Funeral,Tessa Hadley,"Hadley, Tessa",,1473595290,4,3.82,226,2023,2023,21/09/2024
-Strong Female Character,Fern  Brady,"Brady, Fern",,1914240464,4,4.47,288,2023,2023,31/10/2024
\ No newline at end of file
+Strong Female Character,Fern  Brady,"Brady, Fern",,1914240464,4,4.47,288,2023,2023,31/10/2024
diff --git a/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb b/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb
index a1d48e716..03abc0a09 100644
--- a/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb
+++ b/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb
@@ -9,10 +9,10 @@
    "source": [
     "# Vizro-AI setup\n",
     "\n",
-    "from vizro import Vizro\n",
     "import vizro_ai\n",
-    "from vizro_ai import VizroAI\n",
     "from dotenv import load_dotenv\n",
+    "from vizro import Vizro\n",
+    "from vizro_ai import VizroAI\n",
     "\n",
     "# Ensure the API key is in .env\n",
     "load_dotenv()\n",
@@ -23,13 +23,22 @@
     "\n",
     "import pandas as pd\n",
     "\n",
-    "df = pd.read_csv('filtered_books.csv')\n",
-    "df[\"Date Read\"] = pd.to_datetime(df['Date Read'],dayfirst=True)\n",
+    "df = pd.read_csv(\"filtered_books.csv\")\n",
+    "df[\"Date Read\"] = pd.to_datetime(df[\"Date Read\"], dayfirst=True)\n",
     "\n",
-    "# Data cleanup \n",
+    "# Data cleanup\n",
     "# Specify columns to check for missing values\n",
-    "columns_to_check = ['Title', 'Author', 'ISBN', 'My Rating', 'Average Rating', 'Number of Pages', 'Original Publication Year', 'Date Read']\n",
-    "df_cleaned = df.dropna(subset=columns_to_check)\n"
+    "columns_to_check = [\n",
+    "    \"Title\",\n",
+    "    \"Author\",\n",
+    "    \"ISBN\",\n",
+    "    \"My Rating\",\n",
+    "    \"Average Rating\",\n",
+    "    \"Number of Pages\",\n",
+    "    \"Original Publication Year\",\n",
+    "    \"Date Read\",\n",
+    "]\n",
+    "df_cleaned = df.dropna(subset=columns_to_check)"
    ]
   },
   {

From 2e33e0f87c75c8adb808a7da03828299643e233e Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 16 Jan 2025 13:35:45 +0000
Subject: [PATCH 23/44] Pass precommit

---
 .pre-commit-config.yaml | 10 +---------
 vizro-ai/pyproject.toml |  1 +
 2 files changed, 2 insertions(+), 9 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 1107ba26c..ada17ac1c 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -12,14 +12,6 @@ repos:
       - id: trailing-whitespace
       - id: end-of-file-fixer
 
-  - repo: local
-    hooks:
-      - id: check-datafiles
-        name: check-datafiles
-        entry: python tools/check_for_datafiles.py
-        language: python
-        pass_filenames: false
-
   - repo: https://github.com/codespell-project/codespell
     rev: v2.3.0
     hooks:
@@ -36,7 +28,7 @@ repos:
         language: node
         types: [text]
         additional_dependencies: ["prettier@3.3.3"]
-        exclude_types: [markdown]
+        exclude_types: [markdown, csv]
 
   - repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks
     rev: v2.14.0
diff --git a/vizro-ai/pyproject.toml b/vizro-ai/pyproject.toml
index 791d2ab72..8393e7e88 100644
--- a/vizro-ai/pyproject.toml
+++ b/vizro-ai/pyproject.toml
@@ -77,3 +77,4 @@ src = ["src"]
 # Ignore print, line length, and import violations in Colab notebooks
 "examples/chart_by_vizro_ai.ipynb" = ["T201", "E501"]
 "examples/dashboard_by_vizro_ai.ipynb" = ["T201", "E402", "E501"]
+"examples/goodreads_tutorial/goodreads.ipynb" = ["T201", "E402", "E501", "W291", "F811"]

From fd2fe3dc545e2fd225306e5ad991adcf67d40d99 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 16 Jan 2025 13:36:41 +0000
Subject: [PATCH 24/44] Minor tweaks to data/notebook

---
 .../goodreads_tutorial/filtered_books.csv     |  2 +-
 .../goodreads_tutorial/goodreads.ipynb        | 25 +++++++++++++------
 2 files changed, 18 insertions(+), 9 deletions(-)

diff --git a/vizro-ai/examples/goodreads_tutorial/filtered_books.csv b/vizro-ai/examples/goodreads_tutorial/filtered_books.csv
index a93c927ed..33086702b 100644
--- a/vizro-ai/examples/goodreads_tutorial/filtered_books.csv
+++ b/vizro-ai/examples/goodreads_tutorial/filtered_books.csv
@@ -54,4 +54,4 @@ Maybe in Another Life,Taylor Jenkins Reid,"Reid, Taylor Jenkins",,139851666X,2,3
 None of This Is True,Lisa Jewell,"Jewell, Lisa",,1804940224,1,4.12,400,2023,2023,07/09/2024
 Africa Is Not A Country: Breaking Stereotypes of Modern Africa,Dipo Faloyin,"Faloyin, Dipo",,1473593433,4,4.43,340,2022,2022,09/09/2024
 After the Funeral,Tessa Hadley,"Hadley, Tessa",,1473595290,4,3.82,226,2023,2023,21/09/2024
-Strong Female Character,Fern  Brady,"Brady, Fern",,1914240464,4,4.47,288,2023,2023,31/10/2024
\ No newline at end of file
+Strong Female Character,Fern  Brady,"Brady, Fern",,1914240464,4,4.47,288,2023,2023,31/10/2024
diff --git a/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb b/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb
index a1d48e716..969efea35 100644
--- a/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb
+++ b/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb
@@ -9,10 +9,10 @@
    "source": [
     "# Vizro-AI setup\n",
     "\n",
-    "from vizro import Vizro\n",
     "import vizro_ai\n",
-    "from vizro_ai import VizroAI\n",
     "from dotenv import load_dotenv\n",
+    "from vizro import Vizro\n",
+    "from vizro_ai import VizroAI\n",
     "\n",
     "# Ensure the API key is in .env\n",
     "load_dotenv()\n",
@@ -23,13 +23,22 @@
     "\n",
     "import pandas as pd\n",
     "\n",
-    "df = pd.read_csv('filtered_books.csv')\n",
-    "df[\"Date Read\"] = pd.to_datetime(df['Date Read'],dayfirst=True)\n",
+    "df = pd.read_csv(\"filtered_books.csv\")\n",
+    "df[\"Date Read\"] = pd.to_datetime(df[\"Date Read\"], dayfirst=True)\n",
     "\n",
-    "# Data cleanup \n",
+    "# Data cleanup\n",
     "# Specify columns to check for missing values\n",
-    "columns_to_check = ['Title', 'Author', 'ISBN', 'My Rating', 'Average Rating', 'Number of Pages', 'Original Publication Year', 'Date Read']\n",
-    "df_cleaned = df.dropna(subset=columns_to_check)\n"
+    "columns_to_check = [\n",
+    "    \"Title\",\n",
+    "    \"Author\",\n",
+    "    \"ISBN\",\n",
+    "    \"My Rating\",\n",
+    "    \"Average Rating\",\n",
+    "    \"Number of Pages\",\n",
+    "    \"Original Publication Year\",\n",
+    "    \"Date Read\",\n",
+    "]\n",
+    "df_cleaned = df.dropna(subset=columns_to_check)"
    ]
   },
   {
@@ -39,7 +48,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Specifiy the prompt here\n",
+    "# Specify the prompt here\n",
     "user_question = \"\"\"\n",
     "Create a dashboard with 3 pages, one for each chart. \n",
     "\n",

From 174202be31bcef4f597a8b76d2439eac21359c4a Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 16 Jan 2025 13:36:47 +0000
Subject: [PATCH 25/44] Update project-tutorial.md

---
 .../docs/pages/tutorials/project-tutorial.md   | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index 7afc62c0b..2cabed77f 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -4,7 +4,7 @@ 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). \<!--TO DO -- This will change when data is uploaded to Vizro repo-->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/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/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.
 
@@ -192,15 +192,15 @@ The plot this code returns looks as follows: ![](../../assets/tutorials/project/
 
 At this point, we have prototypes for three plotly charts for the Goodreads data. To display these as an interactive dashboard, we need some additional code and Vizro-AI can generate this for us, but not through the application hosted on PyCafe at the time of writing. We'll use a Jupyter Notebook instead.
 
-Before running the Notebook code, [set up Vizro-AI](https://vizro.readthedocs.io/projects/vizro-ai/en/latest/pages/user-guides/install/) inside a virtual environment with Python 3.10 or later. Install the package with `pip install vizro_ai`.
+Before running the Notebook code, [set up Vizro-AI](../user-guides/install.md) inside a virtual environment with Python 3.10 or later. Install the package with `pip install vizro_ai`.
 
-You need to give Vizro-AI your API key to access OpenAI by adding it to your environment so the code you write in the next step can access it to successfully call OpenAI. There are some [straightforward instructions in the OpenAI docs](https://platform.openai.com/docs/quickstart/step-2-set-up-your-api-key), and the process is also covered in the our [LLM setup guide](https://vizro.readthedocs.io/projects/vizro-ai/en/latest/pages/user-guides/install/#set-up-access-to-a-large-language-model).
+You need to give Vizro-AI your API key to access OpenAI by adding it to your environment so the code you write in the next step can access it to successfully call OpenAI. There are some [straightforward instructions in the OpenAI docs](https://platform.openai.com/docs/quickstart/step-2-set-up-your-api-key), and the process is also covered in the our [LLM setup guide](../user-guides/install.md#set-up-access-to-a-large-language-model).
 
 ### Build a dashboard
 
 Now we can open a Jupyter Notebook to make the dashboard. We'll submit a single prompt that combines the three prompts listed above, with some small edits to ask for a dashboard that has three pages: one for each chart.
 
-The following shows the code to make the request to Vizro-AI to build and display the dashboard. The Notebook is available for download from [Vizro's GitHub repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv): <!-- TO DO change this when uploaded -->
+The following shows the code to make the request to Vizro-AI to build and display the dashboard. The Notebook is available for download from [Vizro's GitHub repository](https://raw.githubusercontent.com/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb):
 
 ??? example "Click to expand"
     === "Generated by Vizro-AI"
@@ -228,7 +228,7 @@ The following shows the code to make the request to Vizro-AI to build and displa
 
 Using `gpt-4-turbo`, Vizro-AI generates a set of plotly chart code and the necessary Vizro support code to build a dashboard. The generated code is displayed as output in the Notebook with the dashboard, although the dashboard is better viewed at `http://localhost:8006/`.
 
-You can see the code output in the [Notebook stored on the Vizro GitHub repository](https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/goodreads.ipynb)<!--TO DO change this when uploaded-->, or generate similar output by running it yourself, although it will not necessarily be identical because of the variability of restuls returned from generative AI.
+You can see the code output in the [Notebook stored on the Vizro GitHub repository](https://raw.githubusercontent.com/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb), or generate similar output by running it yourself, although it will not necessarily be identical because of the variability of restuls returned from generative AI.
 
 The charts generated were similar to those created by the PyCafe host above, although the first chart was improved by spacing the books proportionate to the date read and the hover text included the book title as well as the date read, without an explicit request to do so.
 
@@ -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)\<!-- TO DO -- change this --> and upload it to the PyCafe project.
+    - either download the dataset from the [Vizro GitHub repository](https://raw.githubusercontent.com/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/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.
@@ -390,9 +390,8 @@ Follow the link at the bottom of the code snippet titled **☕️ Run and edit t
 
         ########### Read data ############
         # Define the URL of the raw CSV file
-        # TO DO -- This will change when data is uploaded to Vizro repo
         import requests
-        url = "https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv"
+        url = "https://raw.githubusercontent.com/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/filtered_books.csv"
 
         # Fetch the CSV file
         response = requests.get(url)
@@ -598,9 +597,8 @@ Follow the link at the bottom of the code snippet titled **☕️ Run and edit t
 
         ########### Read data ############
         # Define the URL of the raw CSV file
-        # TO DO -- This will change when data is uploaded to Vizro repo
         import requests
-        url = "https://raw.githubusercontent.com/stichbury/vizro_projects/refs/heads/main/goodreads_project/filtered_books.csv"
+        url = "https://raw.githubusercontent.com/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/filtered_books.csv"
 
         # Fetch the CSV file
         response = requests.get(url)

From 5ea2d7e039850b48777fca30a6878f61023c757a Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 16 Jan 2025 13:44:26 +0000
Subject: [PATCH 26/44] Update .pre-commit-config.yaml

---
 .pre-commit-config.yaml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index ada17ac1c..96827dae2 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -18,6 +18,7 @@ repos:
       - id: codespell
         additional_dependencies:
           - tomli
+        exclude_types: [csv]
 
   - repo: local
     hooks:
@@ -28,7 +29,7 @@ repos:
         language: node
         types: [text]
         additional_dependencies: ["prettier@3.3.3"]
-        exclude_types: [markdown, csv]
+        exclude_types: [markdown]
 
   - repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks
     rev: v2.14.0

From 5291fdb27744d1091b27e263c3e7fbd6fd6e3cd4 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 16 Jan 2025 17:28:26 +0000
Subject: [PATCH 27/44] Update for control

---
 .../docs/pages/tutorials/project-tutorial.md  |  19 +-
 .../goodreads_tutorial/goodreads.ipynb        | 359 +++++-------------
 2 files changed, 105 insertions(+), 273 deletions(-)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index 2cabed77f..4554a0556 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -228,12 +228,6 @@ The following shows the code to make the request to Vizro-AI to build and displa
 
 Using `gpt-4-turbo`, Vizro-AI generates a set of plotly chart code and the necessary Vizro support code to build a dashboard. The generated code is displayed as output in the Notebook with the dashboard, although the dashboard is better viewed at `http://localhost:8006/`.
 
-You can see the code output in the [Notebook stored on the Vizro GitHub repository](https://raw.githubusercontent.com/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb), or generate similar output by running it yourself, although it will not necessarily be identical because of the variability of restuls returned from generative AI.
-
-The charts generated were similar to those created by the PyCafe host above, although the first chart was improved by spacing the books proportionate to the date read and the hover text included the book title as well as the date read, without an explicit request to do so.
-
-![](../../assets/tutorials/project/chart1v2.png)
-
 ### Add dashboard interactivity
 
 To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the code for a control. As a simple example, we can extend the prompt to ask for a filter to modify the time period displayed.
@@ -247,8 +241,8 @@ To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the co
         On the first page, plot a chart with the title "Sequence of reading" .
         It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
 
-        + Add a filter so the user can adjust the range of dates by year on the x axis.
-
+        + Add a date picker filter so the user can adjust the range of dates for the Date Read on the x axis.
+        
         On the second page, plot a chart with the title "Pages and Book totals" .
         It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data.
         Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.
@@ -256,7 +250,7 @@ To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the co
 
         Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side.
 
-        + Add a filter so the user can adjust the range of dates by year on the x axis.
+        + Add a date picker filter so the user can adjust the range of dates for the Date Read on the x axis.
 
         On the third page, for each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book.
         Use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.
@@ -264,6 +258,13 @@ To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the co
 
         ```
 
+### Get the Notebook
+You can see the code output in the [Notebook stored on the Vizro GitHub repository](https://raw.githubusercontent.com/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb), and generate similar output by running it yourself, although it will not necessarily be identical because of the variability of restuls returned from generative AI.
+
+The charts generated were similar to those created by the PyCafe host above, although the first chart was improved by spacing the books proportionate to the date read and the hover text included the book title as well as the date read, without an explicit request to do so.
+
+![](../../assets/tutorials/project/chart1v2.png)
+
 ## Interactive Vizro dashboards on PyCafe
 
 Now we have a Notebook with code to call Vizro-AI to build a prototype Vizro dashboard with a set of three pages and three charts, plus a control to filter the view.
diff --git a/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb b/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb
index 969efea35..8d77f518b 100644
--- a/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb
+++ b/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb
@@ -50,18 +50,21 @@
    "source": [
     "# Specify the prompt here\n",
     "user_question = \"\"\"\n",
-    "Create a dashboard with 3 pages, one for each chart. \n",
+    "Create a dashboard with 3 pages, one for each chart.\n",
     "\n",
-    "On the first page, plot a chart with the title \"Sequence of reading\" . \n",
+    "On the first page, plot a chart with the title \"Sequence of reading\" .\n",
     "It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.\n",
-    "Add a filter so the user can adjust the range of dates by year on the x axis.\n",
     "\n",
-    "On the second page, plot a chart with the title \"Pages and Book totals\" . \n",
-    "It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data. \n",
+    "Add a date picker filter so the user can adjust the range of dates for the Date Read on the x axis.\n",
+    "\n",
+    "On the second page, plot a chart with the title \"Pages and Book totals\" .\n",
+    "It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data.\n",
     "Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.\n",
-    "Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column. \n",
-    "Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side. \n",
-    "Add a filter so the user can adjust the range of dates by year on the x axis.\n",
+    "Superimpose a bar chart showing the total books read for each year, taking data from the Date Read column.\n",
+    "\n",
+    "Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side.\n",
+    "\n",
+    "Add a date picker filter so the user can adjust the range of dates for the Date Read on the x axis.\n",
     "\n",
     "On the third page, for each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book.\n",
     "Use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.\n",
@@ -77,7 +80,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "72dc4d9d9a6748d4b651dc76197bc8b5",
+       "model_id": "6305989192314eda92689d52ba554f23",
        "version_major": 2,
        "version_minor": 0
       },
@@ -98,7 +101,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "67e5561620f4492b8557bf061d889223",
+       "model_id": "93da13f7dbaa47df80d68a1995bc5e94",
        "version_major": 2,
        "version_minor": 0
       },
@@ -112,12 +115,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e08ef3dd117d4d6e8294677745c3f730",
+       "model_id": "1f986ae0199a40c19dd17e8e33789df3",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Building page: Rating Comparison:   0%|          | 0/5 [00:00<?, ?it/s]"
+       "Building page: Sequence of Reading:   0%|          | 0/5 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -126,12 +129,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4b60b963928d4395963c8f06ad1f8bd5",
+       "model_id": "e3cef89472b645428fd3380f94d4af67",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Building page: Sequence of Reading:   0%|          | 0/5 [00:00<?, ?it/s]"
+       "Building page: Rating Comparison:   0%|          | 0/5 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -140,7 +143,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0f948c5ba88341f99adbd748ec3f351b",
+       "model_id": "9a20326a7c4e4eceafc86704c9275837",
        "version_major": 2,
        "version_minor": 0
       },
@@ -154,7 +157,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "16f97a6a06c0443592412812b500a3af",
+       "model_id": "f6991ba201184115928d77f81f65ed49",
        "version_major": 2,
        "version_minor": 0
       },
@@ -166,7 +169,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f7e77b4c32c9446ab4f94ca1e5173f52",
+       "model_id": "52d6b47cfb474a92996a1810730f586b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -178,7 +181,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9781bf14e6d44840ae2d58171b205f63",
+       "model_id": "f0a89599fb2e4b41bd9ea9a161cc8521",
        "version_major": 2,
        "version_minor": 0
       },
@@ -190,12 +193,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1294cfce21f247f7953d9f143c26fe90",
+       "model_id": "80e337e9811a4731a83d6cf460b89975",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Currently Building ... [Page] <Rating Comparison> components:   0%|          | 0/1 [00:00<?, ?it/s]"
+       "Currently Building ... [Page] <Sequence of Reading> components:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -204,7 +207,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f9733c9c278045fea3a58e710c97f01a",
+       "model_id": "34692b7dcdf249ddb4e4fa7d9b593864",
        "version_major": 2,
        "version_minor": 0
       },
@@ -218,12 +221,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b58b1a6868134a7ebc0114a774563c1b",
+       "model_id": "cdc65cd346c44e23baa60edb4d3fa591",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Currently Building ... [Page] <Sequence of Reading> components:   0%|          | 0/1 [00:00<?, ?it/s]"
+       "Currently Building ... [Page] <Rating Comparison> components:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -232,7 +235,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "164b169d92124c68a27560ce1bfd193f",
+       "model_id": "dd30f5a69b474309a8dcb8110525fb9c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -256,7 +259,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a13b9b44161e4d94a3bc301263b42f64",
+       "model_id": "abafb517df4649679bb0309a391de695",
        "version_major": 2,
        "version_minor": 0
       },
@@ -270,7 +273,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5055cf7208594dbe8bf0db4621ffff25",
+       "model_id": "e48cfbe18cd74971801554df65947e13",
        "version_major": 2,
        "version_minor": 0
       },
@@ -289,9 +292,9 @@
       "\n",
       "CapturedCallable function is excluded from the schema.\n",
       "\n",
-      "WARNING:root:Could not extract source for <function sequence_reading at 0x144bd04c0>. Definition will not be included.\n",
-      "WARNING:root:Could not extract source for <function rating_comparison at 0x144c7be20>. Definition will not be included.\n",
-      "WARNING:root:Could not extract source for <function pages_books_totals at 0x144cebc70>. Definition will not be included.\n"
+      "WARNING:root:Could not extract source for <function sequence_chart at 0x14ecc70a0>. Definition will not be included.\n",
+      "WARNING:root:Could not extract source for <function rating_comparison_chart at 0x14ed768c0>. Definition will not be included.\n",
+      "WARNING:root:Could not extract source for <function pages_books_chart at 0x14edde290>. Definition will not be included.\n"
      ]
     },
     {
@@ -309,7 +312,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x144cbbd30>"
+       "<IPython.lib.display.IFrame at 0x14e9ac8e0>"
       ]
      },
      "metadata": {},
@@ -329,7 +332,7 @@
       "\n",
       "####### Function definitions ######\n",
       "@capture(\"graph\")\n",
-      "def sequence_reading(data_frame):\n",
+      "def sequence_chart(data_frame):\n",
       "    fig = go.Figure()\n",
       "    fig.add_trace(\n",
       "        go.Scatter(\n",
@@ -337,69 +340,28 @@
       "            y=[1] * len(data_frame),\n",
       "            mode=\"markers\",\n",
       "            marker=dict(size=10, color=\"blue\"),\n",
+      "            text=data_frame[\"Title\"],\n",
       "        )\n",
       "    )\n",
       "    fig.update_layout(\n",
-      "        title=\"Sequence of reading\",\n",
+      "        title=\"Sequence of Books Read Over Time\",\n",
       "        xaxis_title=\"Date Read\",\n",
       "        yaxis_title=\"Sequence\",\n",
-      "        yaxis=dict(showticklabels=False, showgrid=False),\n",
-      "        xaxis=dict(tickangle=-45),\n",
+      "        yaxis=dict(showticklabels=False),\n",
       "    )\n",
       "    return fig\n",
       "\n",
       "\n",
       "@capture(\"graph\")\n",
-      "def pages_books_totals(data_frame):\n",
-      "    # Prepare data\n",
-      "    data_frame[\"Date Read\"] = pd.to_datetime(data_frame[\"Date Read\"])\n",
-      "    data_frame.sort_values(\"Date Read\", inplace=True)\n",
-      "    data_frame[\"Cumulative Pages\"] = data_frame[\"Number of Pages\"].cumsum()\n",
-      "\n",
-      "    # Aggregate data by year for total books read\n",
-      "    yearly_books = data_frame.groupby(data_frame[\"Date Read\"].dt.year).size()\n",
-      "\n",
-      "    # Create figure with secondary y-axis\n",
-      "    fig = go.Figure()\n",
-      "\n",
-      "    # Add line for cumulative pages\n",
-      "    fig.add_trace(\n",
-      "        go.Scatter(\n",
-      "            x=data_frame[\"Date Read\"],\n",
-      "            y=data_frame[\"Cumulative Pages\"],\n",
-      "            mode=\"lines\",\n",
-      "            name=\"Cumulative Pages Read\",\n",
-      "        )\n",
-      "    )\n",
-      "\n",
-      "    # Add bars for books read per year\n",
-      "    fig.add_trace(\n",
-      "        go.Bar(\n",
-      "            x=yearly_books.index, y=yearly_books, name=\"Books Read per Year\", yaxis=\"y2\"\n",
-      "        )\n",
-      "    )\n",
-      "\n",
-      "    # Set up the layout\n",
-      "    fig.update_layout(\n",
-      "        title=\"Cumulative Pages Read and Books Read per Year\",\n",
-      "        xaxis_title=\"Date\",\n",
-      "        yaxis=dict(title=\"Number of Pages\"),\n",
-      "        yaxis2=dict(title=\"Total Books\", overlaying=\"y\", side=\"right\"),\n",
-      "    )\n",
-      "\n",
-      "    return fig\n",
-      "\n",
-      "\n",
-      "@capture(\"graph\")\n",
-      "def rating_comparison(data_frame):\n",
+      "def rating_comparison_chart(data_frame):\n",
       "    # Filter out rows where 'My Rating' is 0\n",
-      "    df = data_frame[data_frame[\"My Rating\"] != 0]\n",
+      "    df_filtered = data_frame[data_frame[\"My Rating\"] != 0]\n",
       "\n",
-      "    # Create a figure\n",
+      "    # Create the dumbbell chart\n",
       "    fig = go.Figure()\n",
       "\n",
-      "    # Add dumbbell traces\n",
-      "    for index, row in df.iterrows():\n",
+      "    # Add traces for each book\n",
+      "    for index, row in df_filtered.iterrows():\n",
       "        fig.add_trace(\n",
       "            go.Scatter(\n",
       "                x=[row[\"My Rating\"], row[\"Average Rating\"]],\n",
@@ -407,16 +369,52 @@
       "                mode=\"markers+lines\",\n",
       "                name=row[\"Title\"],\n",
       "                marker=dict(size=10),\n",
-      "                line=dict(width=2),\n",
       "            )\n",
       "        )\n",
       "\n",
       "    # Update layout\n",
       "    fig.update_layout(\n",
-      "        title=\"Comparison of My Rating vs Average Rating\",\n",
+      "        title=\"Comparison of My Rating vs Average Rating for Each Book\",\n",
       "        xaxis_title=\"Rating\",\n",
       "        yaxis_title=\"Book Title\",\n",
-      "        showlegend=False,\n",
+      "        yaxis={\"categoryorder\": \"total ascending\"},\n",
+      "    )\n",
+      "\n",
+      "    return fig\n",
+      "\n",
+      "\n",
+      "@capture(\"graph\")\n",
+      "def pages_books_chart(data_frame):\n",
+      "    # Prepare data\n",
+      "    data_frame[\"Date Read\"] = pd.to_datetime(data_frame[\"Date Read\"])\n",
+      "    data_frame[\"Year\"] = data_frame[\"Date Read\"].dt.year\n",
+      "    yearly_pages = data_frame.groupby(\"Year\")[\"Number of Pages\"].sum().cumsum()\n",
+      "    yearly_books = data_frame.groupby(\"Year\").size()\n",
+      "\n",
+      "    # Create figure with secondary y-axis\n",
+      "    fig = go.Figure()\n",
+      "\n",
+      "    # Add line for cumulative pages\n",
+      "    fig.add_trace(\n",
+      "        go.Scatter(\n",
+      "            x=yearly_pages.index,\n",
+      "            y=yearly_pages,\n",
+      "            name=\"Cumulative Pages\",\n",
+      "            mode=\"lines+markers\",\n",
+      "        )\n",
+      "    )\n",
+      "\n",
+      "    # Add bars for books read\n",
+      "    fig.add_trace(\n",
+      "        go.Bar(x=yearly_books.index, y=yearly_books, name=\"Books Read\", yaxis=\"y2\")\n",
+      "    )\n",
+      "\n",
+      "    # Layout adjustments\n",
+      "    fig.update_layout(\n",
+      "        title=\"Cumulative Pages Read and Books Read per Year\",\n",
+      "        xaxis_title=\"Year\",\n",
+      "        yaxis_title=\"Total Pages Read\",\n",
+      "        yaxis2=dict(title=\"Total Books Read\", overlaying=\"y\", side=\"right\"),\n",
       "    )\n",
       "\n",
       "    return fig\n",
@@ -434,42 +432,44 @@
       "        vm.Page(\n",
       "            components=[\n",
       "                vm.Graph(\n",
-      "                    id=\"sequence_reading\",\n",
-      "                    figure=sequence_reading(data_frame=\"book_reading_data\"),\n",
+      "                    id=\"sequence_chart\",\n",
+      "                    figure=sequence_chart(data_frame=\"book_reading_data\"),\n",
       "                )\n",
       "            ],\n",
       "            title=\"Sequence of Reading\",\n",
       "            layout=vm.Layout(grid=[[0]]),\n",
       "            controls=[\n",
       "                vm.Filter(\n",
-      "                    column=\"Year Published\",\n",
-      "                    targets=[\"sequence_reading\"],\n",
-      "                    selector=vm.RangeSlider(type=\"range_slider\"),\n",
+      "                    type=\"filter\",\n",
+      "                    column=\"Date Read\",\n",
+      "                    targets=[\"sequence_chart\"],\n",
+      "                    selector=vm.DatePicker(type=\"date_picker\", range=True),\n",
       "                )\n",
       "            ],\n",
       "        ),\n",
       "        vm.Page(\n",
       "            components=[\n",
       "                vm.Graph(\n",
-      "                    id=\"pages_books_totals\",\n",
-      "                    figure=pages_books_totals(data_frame=\"book_reading_data\"),\n",
+      "                    id=\"pages_books_chart\",\n",
+      "                    figure=pages_books_chart(data_frame=\"book_reading_data\"),\n",
       "                )\n",
       "            ],\n",
       "            title=\"Pages and Book Totals\",\n",
       "            layout=vm.Layout(grid=[[0]]),\n",
       "            controls=[\n",
       "                vm.Filter(\n",
-      "                    column=\"Year Published\",\n",
-      "                    targets=[\"pages_books_totals\"],\n",
-      "                    selector=vm.RangeSlider(type=\"range_slider\"),\n",
+      "                    type=\"filter\",\n",
+      "                    column=\"Date Read\",\n",
+      "                    targets=[\"pages_books_chart\"],\n",
+      "                    selector=vm.DatePicker(type=\"date_picker\", range=True),\n",
       "                )\n",
       "            ],\n",
       "        ),\n",
       "        vm.Page(\n",
       "            components=[\n",
       "                vm.Graph(\n",
-      "                    id=\"rating_comparison\",\n",
-      "                    figure=rating_comparison(data_frame=\"book_reading_data\"),\n",
+      "                    id=\"rating_comparison_chart\",\n",
+      "                    figure=rating_comparison_chart(data_frame=\"book_reading_data\"),\n",
       "                )\n",
       "            ],\n",
       "            title=\"Rating Comparison\",\n",
@@ -481,175 +481,6 @@
       ")\n",
       "\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ERROR:__main__:Exception on /_dash-update-component [POST]\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/flask/app.py\", line 1473, in wsgi_app\n",
-      "    response = self.full_dispatch_request()\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/flask/app.py\", line 882, in full_dispatch_request\n",
-      "    rv = self.handle_user_exception(e)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/flask/app.py\", line 880, in full_dispatch_request\n",
-      "    rv = self.dispatch_request()\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/flask/app.py\", line 865, in dispatch_request\n",
-      "    return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)  # type: ignore[no-any-return]\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/dash/dash.py\", line 1376, in dispatch\n",
-      "    ctx.run(\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/dash/_callback.py\", line 507, in add_context\n",
-      "    raise err\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/dash/_callback.py\", line 496, in add_context\n",
-      "    output_value = _invoke_callback(func, *func_args, **func_kwargs)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/dash/_callback.py\", line 43, in _invoke_callback\n",
-      "    return func(*args, **kwargs)  # %% callback invoked %%\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/models/_action/_action.py\", line 181, in callback_wrapper\n",
-      "    return_value = self._action_callback_function(inputs=external, outputs=callback_outputs.get(\"external\"))\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/models/_action/_action.py\", line 110, in _action_callback_function\n",
-      "    return_value = self.function(**inputs)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/models/types.py\", line 153, in __call__\n",
-      "    return self.__function(**{**self.__bound_arguments, **kwargs})\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/actions/_on_page_load_action.py\", line 25, in _on_page_load\n",
-      "    return _get_modified_page_figures(\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/actions/_actions_utils.py\", line 278, in _get_modified_page_figures\n",
-      "    filtered_data = _apply_filters(unfiltered_data, ctds_filter, ctds_filter_interaction, target)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/actions/_actions_utils.py\", line 224, in _apply_filters\n",
-      "    filtered_data = _apply_filter_controls(data_frame=data, ctds_filter=ctds_filter, target=target)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/actions/_actions_utils.py\", line 79, in _apply_filter_controls\n",
-      "    data_frame = data_frame[_filter_function(data_frame[_filter_column], _filter_value)]\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/models/_controls/filter.py\", line 62, in _filter_between\n",
-      "    return series.between(value[0], value[1], inclusive=\"both\")\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/series.py\", line 5638, in between\n",
-      "    lmask = self >= left\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/ops/common.py\", line 76, in new_method\n",
-      "    return method(self, other)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/arraylike.py\", line 60, in __ge__\n",
-      "    return self._cmp_method(other, operator.ge)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/series.py\", line 6119, in _cmp_method\n",
-      "    res_values = ops.comparison_op(lvalues, rvalues, op)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/ops/array_ops.py\", line 341, in comparison_op\n",
-      "    return invalid_comparison(lvalues, rvalues, op)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/ops/invalid.py\", line 40, in invalid_comparison\n",
-      "    raise TypeError(f\"Invalid comparison between dtype={left.dtype} and {typ}\")\n",
-      "TypeError: Invalid comparison between dtype=int64 and str\n",
-      "ERROR:__main__:Exception on /_dash-update-component [POST]\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/flask/app.py\", line 1473, in wsgi_app\n",
-      "    response = self.full_dispatch_request()\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/flask/app.py\", line 882, in full_dispatch_request\n",
-      "    rv = self.handle_user_exception(e)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/flask/app.py\", line 880, in full_dispatch_request\n",
-      "    rv = self.dispatch_request()\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/flask/app.py\", line 865, in dispatch_request\n",
-      "    return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)  # type: ignore[no-any-return]\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/dash/dash.py\", line 1376, in dispatch\n",
-      "    ctx.run(\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/dash/_callback.py\", line 507, in add_context\n",
-      "    raise err\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/dash/_callback.py\", line 496, in add_context\n",
-      "    output_value = _invoke_callback(func, *func_args, **func_kwargs)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/dash/_callback.py\", line 43, in _invoke_callback\n",
-      "    return func(*args, **kwargs)  # %% callback invoked %%\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/models/_action/_action.py\", line 181, in callback_wrapper\n",
-      "    return_value = self._action_callback_function(inputs=external, outputs=callback_outputs.get(\"external\"))\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/models/_action/_action.py\", line 110, in _action_callback_function\n",
-      "    return_value = self.function(**inputs)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/models/types.py\", line 153, in __call__\n",
-      "    return self.__function(**{**self.__bound_arguments, **kwargs})\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/actions/_on_page_load_action.py\", line 25, in _on_page_load\n",
-      "    return _get_modified_page_figures(\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/actions/_actions_utils.py\", line 278, in _get_modified_page_figures\n",
-      "    filtered_data = _apply_filters(unfiltered_data, ctds_filter, ctds_filter_interaction, target)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/actions/_actions_utils.py\", line 224, in _apply_filters\n",
-      "    filtered_data = _apply_filter_controls(data_frame=data, ctds_filter=ctds_filter, target=target)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/actions/_actions_utils.py\", line 79, in _apply_filter_controls\n",
-      "    data_frame = data_frame[_filter_function(data_frame[_filter_column], _filter_value)]\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/vizro/models/_controls/filter.py\", line 62, in _filter_between\n",
-      "    return series.between(value[0], value[1], inclusive=\"both\")\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/series.py\", line 5638, in between\n",
-      "    lmask = self >= left\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/ops/common.py\", line 76, in new_method\n",
-      "    return method(self, other)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/arraylike.py\", line 60, in __ge__\n",
-      "    return self._cmp_method(other, operator.ge)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/series.py\", line 6119, in _cmp_method\n",
-      "    res_values = ops.comparison_op(lvalues, rvalues, op)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/ops/array_ops.py\", line 341, in comparison_op\n",
-      "    return invalid_comparison(lvalues, rvalues, op)\n",
-      "  File \"/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pandas/core/ops/invalid.py\", line 40, in invalid_comparison\n",
-      "    raise TypeError(f\"Invalid comparison between dtype={left.dtype} and {typ}\")\n",
-      "TypeError: Invalid comparison between dtype=int64 and str\n",
-      "<string>:9: SettingWithCopyWarning:\n",
-      "\n",
-      "\n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "\n",
-      "<string>:10: SettingWithCopyWarning:\n",
-      "\n",
-      "\n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "\n",
-      "<string>:11: SettingWithCopyWarning:\n",
-      "\n",
-      "\n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "\n",
-      "<string>:9: SettingWithCopyWarning:\n",
-      "\n",
-      "\n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "\n",
-      "<string>:10: SettingWithCopyWarning:\n",
-      "\n",
-      "\n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "\n",
-      "<string>:11: SettingWithCopyWarning:\n",
-      "\n",
-      "\n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "\n",
-      "<string>:9: SettingWithCopyWarning:\n",
-      "\n",
-      "\n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "\n",
-      "<string>:10: SettingWithCopyWarning:\n",
-      "\n",
-      "\n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "\n",
-      "<string>:11: SettingWithCopyWarning:\n",
-      "\n",
-      "\n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "\n"
-     ]
     }
    ],
    "source": [

From 8776cfc76580ac7e719c32f4c44d2162d278aaee Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 16 Jan 2025 17:29:06 +0000
Subject: [PATCH 28/44] Update project-tutorial.md

---
 vizro-ai/docs/pages/tutorials/project-tutorial.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index 4554a0556..d67220c5d 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -242,7 +242,7 @@ To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the co
         It is a scatter chart. Use the x axis to show the date a book was read. Plot it at y=1.
 
         + Add a date picker filter so the user can adjust the range of dates for the Date Read on the x axis.
-        
+
         On the second page, plot a chart with the title "Pages and Book totals" .
         It shows the cumulative total number of pages read by summing the Number of Pages of each book read in each year, using the Date Read data.
         Plot date on the x axis and the number of pages on the y axis using a scale on the left hand side of the chart.
@@ -259,6 +259,7 @@ To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the co
         ```
 
 ### Get the Notebook
+
 You can see the code output in the [Notebook stored on the Vizro GitHub repository](https://raw.githubusercontent.com/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb), and generate similar output by running it yourself, although it will not necessarily be identical because of the variability of restuls returned from generative AI.
 
 The charts generated were similar to those created by the PyCafe host above, although the first chart was improved by spacing the books proportionate to the date read and the hover text included the book title as well as the date read, without an explicit request to do so.

From 5278e3362667568f92445c1f089cde771a1d6f8b Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 16 Jan 2025 17:57:07 +0000
Subject: [PATCH 29/44] Update the controls

---
 .../docs/pages/tutorials/project-tutorial.md  |  34 ++--
 .../goodreads_tutorial/goodreads.ipynb        | 174 ++++++++----------
 2 files changed, 92 insertions(+), 116 deletions(-)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/project-tutorial.md
index d67220c5d..1fbeebece 100644
--- a/vizro-ai/docs/pages/tutorials/project-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/project-tutorial.md
@@ -230,7 +230,7 @@ Using `gpt-4-turbo`, Vizro-AI generates a set of plotly chart code and the neces
 
 ### Add dashboard interactivity
 
-To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the code for a control. As a simple example, we can extend the prompt to ask for a filter to modify the time period displayed.
+To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the code for a control. As a simple example, we can extend the prompt to ask for a date picker control to modify the time period displayed for the `Date Read` column and change the scale on the x axis of the first chart.
 
 ??? example "Click to expand"
     === "Extend the prompt to add a control"
@@ -250,8 +250,6 @@ To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the co
 
         Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side.
 
-        + Add a date picker filter so the user can adjust the range of dates for the Date Read on the x axis.
-
         On the third page, for each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book.
         Use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.
         """
@@ -406,7 +404,7 @@ Follow the link at the bottom of the code snippet titled **☕️ Run and edit t
         df["Date Read"] = pd.to_datetime(df['Date Read'],dayfirst=True)
 
 
-        ########### Model code ############
+                ########### Model code ############
         model = vm.Dashboard(
             pages=[
                 vm.Page(
@@ -420,9 +418,10 @@ Follow the link at the bottom of the code snippet titled **☕️ Run and edit t
                     layout=vm.Layout(grid=[[0]]),
                     controls=[
                         vm.Filter(
-                            column="Year Published",
+                            type="filter",
+                            column="Date Read",
                             targets=["sequence_reading"],
-                            selector=vm.RangeSlider(type="range_slider"),
+                            selector=vm.DatePicker(type="date_picker", range=True),
                         )
                     ],
                 ),
@@ -435,13 +434,7 @@ Follow the link at the bottom of the code snippet titled **☕️ Run and edit t
                     ],
                     title="Pages and Book Totals",
                     layout=vm.Layout(grid=[[0]]),
-                    controls=[
-                        vm.Filter(
-                            column="Year Published",
-                            targets=["pages_books_totals"],
-                            selector=vm.RangeSlider(type="range_slider"),
-                        )
-                    ],
+                    controls=[],
                 ),
                 vm.Page(
                     components=[
@@ -473,7 +466,7 @@ The code generated by Vizro-AI does not make a perfect dashboard, but it has bee
 
 ### Improvements to generated Plotly code
 
-In this version of the dashboard, the first chart that show the sequence of books read has been modified to improve the information supplied when hovering over a point, and the opacity of the points has been altered to make it more attractive.
+In this version of the dashboard, the first chart that show the sequence of books read has been modified to improve the information supplied when hovering over a point, and the opacity of the points has been altered to make it more attractive. The control has been changed to a slider for the `Date Read` field.
 
 In the second chart that shows the cumulative total of pages and books read, the line chart has been explicitly plotted on top of the bar chart.
 
@@ -611,7 +604,8 @@ Follow the link at the bottom of the code snippet titled **☕️ Run and edit t
         csv_data = StringIO(response.text)
         df = pd.read_csv(csv_data)
         df["Date Read"] = pd.to_datetime(df['Date Read'],dayfirst=True)
-
+        # Extract the year from each Date Read and create a new column 'Year Read'
+        df['Year Read'] = df['Date Read'].dt.year
 
         ########### Model code ############
         model = vm.Dashboard(
@@ -627,7 +621,7 @@ Follow the link at the bottom of the code snippet titled **☕️ Run and edit t
                     layout=vm.Layout(grid=[[0]]),
                     controls=[
                         vm.Filter(
-                            column="Year Published",
+                            column="Year Read",
                             targets=["sequence_reading"],
                             selector=vm.RangeSlider(type="range_slider"),
                         )
@@ -642,13 +636,7 @@ Follow the link at the bottom of the code snippet titled **☕️ Run and edit t
                     ],
                     title="Pages and Book Totals",
                     layout=vm.Layout(grid=[[0]]),
-                    controls=[
-                        vm.Filter(
-                            column="Year Published",
-                            targets=["pages_books_totals"],
-                            selector=vm.RangeSlider(type="range_slider"),
-                        )
-                    ],
+                    controls=[],
                 ),
                 vm.Page(
                     components=[
diff --git a/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb b/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb
index 8d77f518b..b4cb036a3 100644
--- a/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb
+++ b/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb
@@ -64,8 +64,6 @@
     "\n",
     "Show the total books read using the right hand side of the chart which can be a different scale to the y axis shown on the left hand side.\n",
     "\n",
-    "Add a date picker filter so the user can adjust the range of dates for the Date Read on the x axis.\n",
-    "\n",
     "On the third page, for each row, create a dumbbell chart to show the difference between My Rating and Average Rating for each book.\n",
     "Use shapes to add the horizontal lines between markers. Omit the legend. Don't show any row where My Rating is 0.\n",
     "\"\"\""
@@ -80,7 +78,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6305989192314eda92689d52ba554f23",
+       "model_id": "6e9ee132cfb34f2580a909f391ad60dd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -101,7 +99,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "93da13f7dbaa47df80d68a1995bc5e94",
+       "model_id": "6695c7d33ed94a76b099df1b3a9e4d9b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -115,12 +113,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1f986ae0199a40c19dd17e8e33789df3",
+       "model_id": "fc86ba573acc474abd167410a975f5ca",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Building page: Sequence of Reading:   0%|          | 0/5 [00:00<?, ?it/s]"
+       "Building page: Rating Comparison:   0%|          | 0/5 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -129,12 +127,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e3cef89472b645428fd3380f94d4af67",
+       "model_id": "5c911c5bd1514ff7ba4a2bb40aeb85c9",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Building page: Rating Comparison:   0%|          | 0/5 [00:00<?, ?it/s]"
+       "Building page: Pages and Book Totals:   0%|          | 0/5 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -143,12 +141,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9a20326a7c4e4eceafc86704c9275837",
+       "model_id": "89e1a4f904b1497e8a334546d7f5c028",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Building page: Pages and Book Totals:   0%|          | 0/5 [00:00<?, ?it/s]"
+       "Building page: Sequence of Reading:   0%|          | 0/5 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -157,7 +155,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f6991ba201184115928d77f81f65ed49",
+       "model_id": "38daa73439c446b48d6b0c07b002e9f4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -169,7 +167,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "52d6b47cfb474a92996a1810730f586b",
+       "model_id": "539cc8f826b34366a45a1fc097670fb2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -181,7 +179,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f0a89599fb2e4b41bd9ea9a161cc8521",
+       "model_id": "74415162dee74c1b90e4156457f01bd9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -193,12 +191,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "80e337e9811a4731a83d6cf460b89975",
+       "model_id": "e829bf5da11b4f2faad09f1edfec0ee7",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Currently Building ... [Page] <Sequence of Reading> components:   0%|          | 0/1 [00:00<?, ?it/s]"
+       "Currently Building ... [Page] <Pages and Book Totals> components:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -207,12 +205,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "34692b7dcdf249ddb4e4fa7d9b593864",
+       "model_id": "28059fe9bf9441b1b8c78ed04a1a8cba",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Currently Building ... [Page] <Pages and Book Totals> components:   0%|          | 0/1 [00:00<?, ?it/s]"
+       "Currently Building ... [Page] <Rating Comparison> components:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -221,12 +219,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cdc65cd346c44e23baa60edb4d3fa591",
+       "model_id": "28b377d144574f52beb7091fd33ffbe3",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Currently Building ... [Page] <Rating Comparison> components:   0%|          | 0/1 [00:00<?, ?it/s]"
+       "Currently Building ... [Page] <Sequence of Reading> components:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -235,7 +233,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dd30f5a69b474309a8dcb8110525fb9c",
+       "model_id": "fd398990ab9f473c9b1919524d95c990",
        "version_major": 2,
        "version_minor": 0
       },
@@ -259,12 +257,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "abafb517df4649679bb0309a391de695",
+       "model_id": "a39e34543f4b4b119be7fa4846a45e96",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Currently Building ... [Page] <Rating Comparison> controls: 0it [00:00, ?it/s]"
+       "Currently Building ... [Page] <Pages and Book Totals> controls: 0it [00:00, ?it/s]"
       ]
      },
      "metadata": {},
@@ -273,12 +271,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e48cfbe18cd74971801554df65947e13",
+       "model_id": "3d9567ab8ad6439885f9387bf0f7ebfe",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Currently Building ... [Page] <Pages and Book Totals> controls:   0%|          | 0/1 [00:00<?, ?it/s]"
+       "Currently Building ... [Page] <Rating Comparison> controls: 0it [00:00, ?it/s]"
       ]
      },
      "metadata": {},
@@ -288,13 +286,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/jo_stichbury/Documents/GitHub/vizro/vizro-core/vizro/lib/python3.10/site-packages/pydantic/v1/schema.py:631: UserWarning:\n",
-      "\n",
-      "CapturedCallable function is excluded from the schema.\n",
-      "\n",
-      "WARNING:root:Could not extract source for <function sequence_chart at 0x14ecc70a0>. Definition will not be included.\n",
-      "WARNING:root:Could not extract source for <function rating_comparison_chart at 0x14ed768c0>. Definition will not be included.\n",
-      "WARNING:root:Could not extract source for <function pages_books_chart at 0x14edde290>. Definition will not be included.\n"
+      "WARNING:root:Could not extract source for <function sequence_reading at 0x13f4d2950>. Definition will not be included.\n",
+      "WARNING:root:Could not extract source for <function pages_books_totals at 0x13f5a9fc0>. Definition will not be included.\n",
+      "WARNING:root:Could not extract source for <function rating_comparison at 0x13f60dd80>. Definition will not be included.\n"
      ]
     },
     {
@@ -312,7 +306,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x14e9ac8e0>"
+       "<IPython.lib.display.IFrame at 0x13f5d8f40>"
       ]
      },
      "metadata": {},
@@ -332,89 +326,90 @@
       "\n",
       "####### Function definitions ######\n",
       "@capture(\"graph\")\n",
-      "def sequence_chart(data_frame):\n",
-      "    fig = go.Figure()\n",
-      "    fig.add_trace(\n",
-      "        go.Scatter(\n",
-      "            x=data_frame[\"Date Read\"],\n",
-      "            y=[1] * len(data_frame),\n",
-      "            mode=\"markers\",\n",
-      "            marker=dict(size=10, color=\"blue\"),\n",
-      "            text=data_frame[\"Title\"],\n",
-      "        )\n",
-      "    )\n",
-      "    fig.update_layout(\n",
-      "        title=\"Sequence of Books Read Over Time\",\n",
-      "        xaxis_title=\"Date Read\",\n",
-      "        yaxis_title=\"Sequence\",\n",
-      "        yaxis=dict(showticklabels=False),\n",
-      "    )\n",
-      "    return fig\n",
-      "\n",
-      "\n",
-      "@capture(\"graph\")\n",
-      "def rating_comparison_chart(data_frame):\n",
+      "def rating_comparison(data_frame):\n",
       "    # Filter out rows where 'My Rating' is 0\n",
       "    df_filtered = data_frame[data_frame[\"My Rating\"] != 0]\n",
       "\n",
-      "    # Create the dumbbell chart\n",
+      "    # Create a figure\n",
       "    fig = go.Figure()\n",
       "\n",
-      "    # Add traces for each book\n",
+      "    # Add traces for 'My Rating' and 'Average Rating'\n",
       "    for index, row in df_filtered.iterrows():\n",
       "        fig.add_trace(\n",
       "            go.Scatter(\n",
       "                x=[row[\"My Rating\"], row[\"Average Rating\"]],\n",
       "                y=[row[\"Title\"], row[\"Title\"]],\n",
-      "                mode=\"markers+lines\",\n",
-      "                name=row[\"Title\"],\n",
+      "                mode=\"lines+markers\",\n",
+      "                name=\"\",\n",
       "                marker=dict(size=10),\n",
+      "                line=dict(color=\"gray\", width=2),\n",
       "            )\n",
       "        )\n",
       "\n",
-      "    # Update layout\n",
+      "    # Update layout to hide the legend\n",
+      "    fig.update_layout(showlegend=False)\n",
+      "\n",
+      "    return fig\n",
+      "\n",
+      "\n",
+      "@capture(\"graph\")\n",
+      "def sequence_reading(data_frame):\n",
+      "    # Sorting data by 'Date Read'\n",
+      "    sorted_df = data_frame.sort_values(\"Date Read\")\n",
+      "    # Creating a scatter plot\n",
+      "    fig = go.Figure()\n",
+      "    fig.add_trace(\n",
+      "        go.Scatter(\n",
+      "            x=sorted_df[\"Date Read\"],\n",
+      "            y=[1] * len(sorted_df),\n",
+      "            mode=\"markers\",\n",
+      "            marker=dict(color=\"blue\", size=10),\n",
+      "            name=\"Books Read\",\n",
+      "        )\n",
+      "    )\n",
       "    fig.update_layout(\n",
-      "        title=\"Comparison of My Rating vs Average Rating for Each Book\",\n",
-      "        xaxis_title=\"Rating\",\n",
-      "        yaxis_title=\"Book Title\",\n",
-      "        yaxis={\"categoryorder\": \"total ascending\"},\n",
+      "        title=\"Sequence of Books Read Over Time\",\n",
+      "        xaxis_title=\"Date Read\",\n",
+      "        yaxis_title=\"Sequence\",\n",
+      "        yaxis=dict(showticklabels=False),\n",
       "    )\n",
-      "\n",
       "    return fig\n",
       "\n",
       "\n",
       "@capture(\"graph\")\n",
-      "def pages_books_chart(data_frame):\n",
+      "def pages_books_totals(data_frame):\n",
       "    # Prepare data\n",
       "    data_frame[\"Date Read\"] = pd.to_datetime(data_frame[\"Date Read\"])\n",
-      "    data_frame[\"Year\"] = data_frame[\"Date Read\"].dt.year\n",
-      "    yearly_pages = data_frame.groupby(\"Year\")[\"Number of Pages\"].sum().cumsum()\n",
-      "    yearly_books = data_frame.groupby(\"Year\").size()\n",
+      "    data_frame.sort_values(\"Date Read\", inplace=True)\n",
+      "    data_frame[\"Cumulative Pages\"] = data_frame[\"Number of Pages\"].cumsum()\n",
+      "    yearly_books = data_frame.groupby(data_frame[\"Date Read\"].dt.year).size()\n",
       "\n",
-      "    # Create figure with secondary y-axis\n",
+      "    # Create figure\n",
       "    fig = go.Figure()\n",
       "\n",
-      "    # Add line for cumulative pages\n",
+      "    # Add cumulative pages line\n",
       "    fig.add_trace(\n",
       "        go.Scatter(\n",
-      "            x=yearly_pages.index,\n",
-      "            y=yearly_pages,\n",
-      "            name=\"Cumulative Pages\",\n",
-      "            mode=\"lines+markers\",\n",
+      "            x=data_frame[\"Date Read\"],\n",
+      "            y=data_frame[\"Cumulative Pages\"],\n",
+      "            mode=\"lines\",\n",
+      "            name=\"Cumulative Pages Read\",\n",
       "        )\n",
       "    )\n",
       "\n",
-      "    # Add bars for books read\n",
+      "    # Add books read per year bar\n",
       "    fig.add_trace(\n",
-      "        go.Bar(x=yearly_books.index, y=yearly_books, name=\"Books Read\", yaxis=\"y2\")\n",
+      "        go.Bar(\n",
+      "            x=yearly_books.index, y=yearly_books, name=\"Books Read per Year\", yaxis=\"y2\"\n",
+      "        )\n",
       "    )\n",
       "\n",
       "    # Layout adjustments\n",
       "    fig.update_layout(\n",
       "        title=\"Cumulative Pages Read and Books Read per Year\",\n",
-      "        xaxis_title=\"Year\",\n",
+      "        xaxis_title=\"Date Read\",\n",
       "        yaxis_title=\"Total Pages Read\",\n",
-      "        yaxis2=dict(title=\"Total Books Read\", overlaying=\"y\", side=\"right\"),\n",
+      "        yaxis2=dict(title=\"Books Read per Year\", overlaying=\"y\", side=\"right\"),\n",
       "    )\n",
       "\n",
       "    return fig\n",
@@ -432,8 +427,8 @@
       "        vm.Page(\n",
       "            components=[\n",
       "                vm.Graph(\n",
-      "                    id=\"sequence_chart\",\n",
-      "                    figure=sequence_chart(data_frame=\"book_reading_data\"),\n",
+      "                    id=\"sequence_reading\",\n",
+      "                    figure=sequence_reading(data_frame=\"book_reading_data\"),\n",
       "                )\n",
       "            ],\n",
       "            title=\"Sequence of Reading\",\n",
@@ -442,7 +437,7 @@
       "                vm.Filter(\n",
       "                    type=\"filter\",\n",
       "                    column=\"Date Read\",\n",
-      "                    targets=[\"sequence_chart\"],\n",
+      "                    targets=[\"sequence_reading\"],\n",
       "                    selector=vm.DatePicker(type=\"date_picker\", range=True),\n",
       "                )\n",
       "            ],\n",
@@ -450,26 +445,19 @@
       "        vm.Page(\n",
       "            components=[\n",
       "                vm.Graph(\n",
-      "                    id=\"pages_books_chart\",\n",
-      "                    figure=pages_books_chart(data_frame=\"book_reading_data\"),\n",
+      "                    id=\"pages_books_totals\",\n",
+      "                    figure=pages_books_totals(data_frame=\"book_reading_data\"),\n",
       "                )\n",
       "            ],\n",
       "            title=\"Pages and Book Totals\",\n",
       "            layout=vm.Layout(grid=[[0]]),\n",
-      "            controls=[\n",
-      "                vm.Filter(\n",
-      "                    type=\"filter\",\n",
-      "                    column=\"Date Read\",\n",
-      "                    targets=[\"pages_books_chart\"],\n",
-      "                    selector=vm.DatePicker(type=\"date_picker\", range=True),\n",
-      "                )\n",
-      "            ],\n",
+      "            controls=[],\n",
       "        ),\n",
       "        vm.Page(\n",
       "            components=[\n",
       "                vm.Graph(\n",
-      "                    id=\"rating_comparison_chart\",\n",
-      "                    figure=rating_comparison_chart(data_frame=\"book_reading_data\"),\n",
+      "                    id=\"rating_comparison\",\n",
+      "                    figure=rating_comparison(data_frame=\"book_reading_data\"),\n",
       "                )\n",
       "            ],\n",
       "            title=\"Rating Comparison\",\n",

From 5cfd0f9d2b3fc57b1b2da5d408888232458e1d48 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 16 Jan 2025 17:57:31 +0000
Subject: [PATCH 30/44] Rename markdown file

---
 .../tutorials/{project-tutorial.md => goodreads-tutorial.md}      | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename vizro-ai/docs/pages/tutorials/{project-tutorial.md => goodreads-tutorial.md} (100%)

diff --git a/vizro-ai/docs/pages/tutorials/project-tutorial.md b/vizro-ai/docs/pages/tutorials/goodreads-tutorial.md
similarity index 100%
rename from vizro-ai/docs/pages/tutorials/project-tutorial.md
rename to vizro-ai/docs/pages/tutorials/goodreads-tutorial.md

From 1239b7a78666030134aa4774383ccfb974b2fa71 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 16 Jan 2025 17:57:39 +0000
Subject: [PATCH 31/44] Update mkdocs.yml

---
 vizro-ai/mkdocs.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vizro-ai/mkdocs.yml b/vizro-ai/mkdocs.yml
index bf1768567..092e90f38 100644
--- a/vizro-ai/mkdocs.yml
+++ b/vizro-ai/mkdocs.yml
@@ -5,7 +5,7 @@ nav:
   - Tutorials:
       - Chart generation: pages/tutorials/quickstart.md
       - Dashboard generation: pages/tutorials/quickstart-dashboard.md
-      - Goodreads tutorial: pages/tutorials/project-tutorial.md
+      - Goodreads tutorial: pages/tutorials/goodreads-tutorial.md
   - How-to guides:
       - FUNDAMENTALS:
           - Install Vizro-AI: pages/user-guides/install.md

From 433888942b177dda34f14737476e32d629331dbd Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Mon, 20 Jan 2025 10:31:14 +0000
Subject: [PATCH 32/44] Update .pre-commit-config.yaml

---
 .pre-commit-config.yaml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 96827dae2..03eedfda5 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -101,7 +101,6 @@ ci:
     - check-case-conflict
     - check-yaml
     - check-json
-    - check-datafiles
     - codespell
     - bandit
     - mypy

From dee27c3d272325154ed5f37323dd084525b41100 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Mon, 20 Jan 2025 15:13:00 +0000
Subject: [PATCH 33/44] Update goodreads-tutorial.md

---
 .../pages/tutorials/goodreads-tutorial.md     | 32 ++++++++++---------
 1 file changed, 17 insertions(+), 15 deletions(-)

diff --git a/vizro-ai/docs/pages/tutorials/goodreads-tutorial.md b/vizro-ai/docs/pages/tutorials/goodreads-tutorial.md
index 1fbeebece..457980a61 100644
--- a/vizro-ai/docs/pages/tutorials/goodreads-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/goodreads-tutorial.md
@@ -1,10 +1,10 @@
 # How to build a prototype dashboard with Vizro-AI
 
-This tutorial uses Vizro-AI to build a prototype dashboard with three charts that illustrate a simple dataset. We first show how to create individual charts with Vizro-AI and then move on to learn how to use Vizro-AI to build a dashboard. The tutorial concludes by moving the prototype code generated by Vizro-AI into a project on PyCafe, for others to use and extend.
+This tutorial uses Vizro-AI to build a prototype dashboard with three charts that illustrate a simple dataset. We first show how to create individual charts with Vizro-AI and then show how to use Vizro-AI to build a dashboard. The tutorial concludes by moving the prototype code generated by Vizro-AI into a project on PyCafe, for others to use and extend.
 
 ## 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/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/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 can be downloaded from the [Vizro repository](https://raw.githubusercontent.com/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/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.
 
@@ -17,7 +17,7 @@ This tutorial uses OpenAI models with Vizro-AI. To run through the steps, you mu
 
 ## Chart generation with Vizro-AI
 
-In this step, we use a UI on a hosted version of Vizro-AI, found at [https://py.cafe/app/vizro-official/vizro-ai-charts](https://py.cafe/app/vizro-official/vizro-ai-charts). Use your browser to navigate to the site which looks as follows:
+In this step, we use a hosted version of Vizro-AI, found at [https://py.cafe/app/vizro-official/vizro-ai-charts](https://py.cafe/app/vizro-official/vizro-ai-charts). Navigate with your browser to the site, which looks as follows:
 
 ![](../../assets/tutorials/project/user-interface-hosted-vizro-ai.png)
 
@@ -27,7 +27,9 @@ The link will open with a settings pane where you can set the API key for your c
 
 ![](../../assets/tutorials/project/user-interface-settings-hosted-vizro-ai.png)
 
-To return to these settings at any time, you'll notice a cog icon at the top right hand corner for access to them. Once the API key is set, return to the main screen and upload the data for the project.
+To return to these settings at any time, you'll notice a cog icon at the top right hand corner for access to them. 
+
+Once the API key is set, return to the main screen and [upload the data for the project](https://raw.githubusercontent.com/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/filtered_books.csv).
 
 We can now use Vizro-AI to build some charts by iterating text to form effective prompts.
 
@@ -76,7 +78,7 @@ Show the total books read using the right hand side of the chart,
 which can be a different scale to the y axis shown on the left hand side.
 ```
 
-The Plotly code generated when run with `gpt-4-turbo` was as follows. The chart could be improved by bringing the line graph on top of the bar chart, but varying the prompt to make this explicit did not have the desired results:
+The Plotly code generated when run with `gpt-4-turbo` was as follows. The chart could be improved by bringing the line graph on top of the bar chart, but varying the prompt to make this explicit did not generate the desired output:
 
 ??? example "Click to expand"
     === "Generated by Vizro-AI"
@@ -190,7 +192,7 @@ The plot this code returns looks as follows: ![](../../assets/tutorials/project/
 
 ### Set up a Jupyter Notebook
 
-At this point, we have prototypes for three plotly charts for the Goodreads data. To display these as an interactive dashboard, we need some additional code and Vizro-AI can generate this for us, but not through the application hosted on PyCafe at the time of writing. We'll use a Jupyter Notebook instead.
+Now we have prototypes for three plotly charts for the Goodreads data. To display these as an interactive dashboard, we need some additional code. Vizro-AI can generate this for us, but not through the application hosted on PyCafe at the time of writing. We'll use a Jupyter Notebook instead.
 
 Before running the Notebook code, [set up Vizro-AI](../user-guides/install.md) inside a virtual environment with Python 3.10 or later. Install the package with `pip install vizro_ai`.
 
@@ -198,9 +200,9 @@ You need to give Vizro-AI your API key to access OpenAI by adding it to your env
 
 ### Build a dashboard
 
-Now we can open a Jupyter Notebook to make the dashboard. We'll submit a single prompt that combines the three prompts listed above, with some small edits to ask for a dashboard that has three pages: one for each chart.
+In the Jupyter Notebook we'll submit a single prompt that combines the three prompts listed above, with some small edits to ask for a dashboard that has three pages: one for each chart.
 
-The following shows the code to make the request to Vizro-AI to build and display the dashboard. The Notebook is available for download from [Vizro's GitHub repository](https://raw.githubusercontent.com/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb):
+The following shows the code to make the request to Vizro-AI to build and display the dashboardted, with the data handling code omit. The cp,[;ete Notebook is available for download from [Vizro's GitHub repository](https://raw.githubusercontent.com/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb):
 
 ??? example "Click to expand"
     === "Generated by Vizro-AI"
@@ -258,9 +260,9 @@ To make the Vizro dashboards more interactive, we can ask Vizro-AI to add the co
 
 ### Get the Notebook
 
-You can see the code output in the [Notebook stored on the Vizro GitHub repository](https://raw.githubusercontent.com/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb), and generate similar output by running it yourself, although it will not necessarily be identical because of the variability of restuls returned from generative AI.
+You can see the code output in the [Notebook stored on the Vizro GitHub repository](https://raw.githubusercontent.com/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb), and generate similar output by running it yourself, although it will not necessarily be identical because of the variability of results returned from generative AI.
 
-The charts generated were similar to those created by the PyCafe host above, although the first chart was improved by spacing the books proportionate to the date read and the hover text included the book title as well as the date read, without an explicit request to do so.
+The charts generated were similar to those created by the PyCafe host above, although the first chart was improved by spacing the books proportionate to the date read. The hover text included the book title as well as the date read, without needing an explicit request to do so.
 
 ![](../../assets/tutorials/project/chart1v2.png)
 
@@ -272,7 +274,7 @@ As we've already seen, the code generated by Vizro-AI can vary from run to run,
 
 The project isn't particularly easy to share at present either: sharing a Notebook requires every user to have an OpenAI key and set up an environment.
 
-To share and iterate the prototype, we can transfer the generated code from the output of Vizro-AI in the Notebook into a PyCafe project.
+To share and iterate the prototype, we can transfer the **generated** code from the output of Vizro-AI in the Notebook into a PyCafe project.
 
 There are three changes to the Notebook code needed for it to run on PyCafe:
 
@@ -454,19 +456,19 @@ Follow the link at the bottom of the code snippet titled **☕️ Run and edit t
         Vizro().build(model).run()
         ```
 
-## Summary
+## Prototype summary
 
-In this tutorial, we first used Vizro-AI to generate a set of charts by iterating prompts, hosted in PyCafe.
+In this tutorial, we used Vizro-AI to generate a set of charts by iterating prompts, hosted in PyCafe.
 
 We then converted the successful prompts to build a Vizro dashboard using Vizro-AI in a Notebook using a few lines of support code.
 
-Finally, we converted the Vizro-AI generated Python to a PyCafe project with a few additional lines of support code. At this point, the Vizro project is not using generative AI, so it is easy to share and iterate.
+Finally, we converted the generated Python to a PyCafe project with a few additional lines of support code. The resulting Vizro project is not using generative AI, so it is easy to share and iterate.
 
 The code generated by Vizro-AI does not make a perfect dashboard, but it has been very easy to get it to a good standard without a great deal of coding or design experience. There are a few improvements that are easily applied to the Plotly code to further improve the charts, as shown below in a separate PyCafe project.
 
 ### Improvements to generated Plotly code
 
-In this version of the dashboard, the first chart that show the sequence of books read has been modified to improve the information supplied when hovering over a point, and the opacity of the points has been altered to make it more attractive. The control has been changed to a slider for the `Date Read` field.
+In this version of the dashboard, the first chart that shows the sequence of books read has been modified to improve the information supplied when hovering over a point, and the opacity of the points has been altered to make it more attractive. The control has been changed to a slider for the `Date Read` field.
 
 In the second chart that shows the cumulative total of pages and books read, the line chart has been explicitly plotted on top of the bar chart.
 

From a6af91d3820f91a9f5dbccd7a7f6c2805a8e39bf Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 20 Jan 2025 15:13:22 +0000
Subject: [PATCH 34/44] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 vizro-ai/docs/pages/tutorials/goodreads-tutorial.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/vizro-ai/docs/pages/tutorials/goodreads-tutorial.md b/vizro-ai/docs/pages/tutorials/goodreads-tutorial.md
index 457980a61..83132b142 100644
--- a/vizro-ai/docs/pages/tutorials/goodreads-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/goodreads-tutorial.md
@@ -27,7 +27,7 @@ The link will open with a settings pane where you can set the API key for your c
 
 ![](../../assets/tutorials/project/user-interface-settings-hosted-vizro-ai.png)
 
-To return to these settings at any time, you'll notice a cog icon at the top right hand corner for access to them. 
+To return to these settings at any time, you'll notice a cog icon at the top right hand corner for access to them.
 
 Once the API key is set, return to the main screen and [upload the data for the project](https://raw.githubusercontent.com/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/filtered_books.csv).
 
@@ -202,7 +202,7 @@ You need to give Vizro-AI your API key to access OpenAI by adding it to your env
 
 In the Jupyter Notebook we'll submit a single prompt that combines the three prompts listed above, with some small edits to ask for a dashboard that has three pages: one for each chart.
 
-The following shows the code to make the request to Vizro-AI to build and display the dashboardted, with the data handling code omit. The cp,[;ete Notebook is available for download from [Vizro's GitHub repository](https://raw.githubusercontent.com/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb):
+The following shows the code to make the request to Vizro-AI to build and display the dashboardted, with the data handling code omit. The cp,\[;ete Notebook is available for download from [Vizro's GitHub repository](https://raw.githubusercontent.com/mckinsey/vizro/834c8d9c198420e8b315c0dc045b7aa12534719d/vizro-ai/examples/goodreads_tutorial/goodreads.ipynb):
 
 ??? example "Click to expand"
     === "Generated by Vizro-AI"

From 5520d22d1ea40f7aee207668d26a3fcbeec94c8d Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 23 Jan 2025 11:03:02 +0000
Subject: [PATCH 35/44] Add extrahead for google site verification

---
 vizro-core/docs/overrides/main.html | 5 +++++
 vizro-core/mkdocs.yml               | 1 +
 2 files changed, 6 insertions(+)
 create mode 100644 vizro-core/docs/overrides/main.html

diff --git a/vizro-core/docs/overrides/main.html b/vizro-core/docs/overrides/main.html
new file mode 100644
index 000000000..686b40961
--- /dev/null
+++ b/vizro-core/docs/overrides/main.html
@@ -0,0 +1,5 @@
+{% extends "base.html" %}
+
+{% block extrahead %}
+  <meta name="google-site-verification" content="CYb3cxosCgsN2QDQVaSGQpMQCesqpsGQ3oTM02NtvkY" />
+{% endblock %}
\ No newline at end of file
diff --git a/vizro-core/mkdocs.yml b/vizro-core/mkdocs.yml
index 61d7bec98..8c4fd1d12 100644
--- a/vizro-core/mkdocs.yml
+++ b/vizro-core/mkdocs.yml
@@ -66,6 +66,7 @@ repo_name: mckinsey/vizro
 theme:
   name: material
   language: en
+  custom_dir: docs/overrides
   palette:
     - scheme: default
   font:

From d9e607a3fd27b841e1cefc7bff964adb8f6ca542 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 23 Jan 2025 11:05:00 +0000
Subject: [PATCH 36/44] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 vizro-core/docs/overrides/main.html | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/vizro-core/docs/overrides/main.html b/vizro-core/docs/overrides/main.html
index 686b40961..29b712f2d 100644
--- a/vizro-core/docs/overrides/main.html
+++ b/vizro-core/docs/overrides/main.html
@@ -1,5 +1,6 @@
-{% extends "base.html" %}
-
-{% block extrahead %}
-  <meta name="google-site-verification" content="CYb3cxosCgsN2QDQVaSGQpMQCesqpsGQ3oTM02NtvkY" />
-{% endblock %}
\ No newline at end of file
+{% extends "base.html" %} {% block extrahead %}
+<meta
+  name="google-site-verification"
+  content="CYb3cxosCgsN2QDQVaSGQpMQCesqpsGQ3oTM02NtvkY"
+/>
+{% endblock %}

From ff19b313213022c1e21bce4834569e2ea83e7954 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 23 Jan 2025 11:09:13 +0000
Subject: [PATCH 37/44] Reverting commit on wrong branch

---
 vizro-core/mkdocs.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/vizro-core/mkdocs.yml b/vizro-core/mkdocs.yml
index 8c4fd1d12..61d7bec98 100644
--- a/vizro-core/mkdocs.yml
+++ b/vizro-core/mkdocs.yml
@@ -66,7 +66,6 @@ repo_name: mckinsey/vizro
 theme:
   name: material
   language: en
-  custom_dir: docs/overrides
   palette:
     - scheme: default
   font:

From 313a989c53b2c1d4b46c8f251f195c041464c1c4 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Thu, 23 Jan 2025 11:14:08 +0000
Subject: [PATCH 38/44] Reverting commit on wrong branch

---
 vizro-core/docs/overrides/main.html | 6 ------
 1 file changed, 6 deletions(-)
 delete mode 100644 vizro-core/docs/overrides/main.html

diff --git a/vizro-core/docs/overrides/main.html b/vizro-core/docs/overrides/main.html
deleted file mode 100644
index 29b712f2d..000000000
--- a/vizro-core/docs/overrides/main.html
+++ /dev/null
@@ -1,6 +0,0 @@
-{% extends "base.html" %} {% block extrahead %}
-<meta
-  name="google-site-verification"
-  content="CYb3cxosCgsN2QDQVaSGQpMQCesqpsGQ3oTM02NtvkY"
-/>
-{% endblock %}

From c7ac504a006dc9e936f030734531ba8c8b9e5ce6 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Tue, 4 Feb 2025 14:17:14 +0000
Subject: [PATCH 39/44] Add canonical tag

---
 vizro-ai/docs/overrides/canonical_tag.py            | 13 +++++++++++++
 vizro-ai/docs/pages/tutorials/goodreads-tutorial.md |  4 ++++
 vizro-ai/mkdocs.yml                                 |  3 +++
 3 files changed, 20 insertions(+)
 create mode 100644 vizro-ai/docs/overrides/canonical_tag.py

diff --git a/vizro-ai/docs/overrides/canonical_tag.py b/vizro-ai/docs/overrides/canonical_tag.py
new file mode 100644
index 000000000..3da2dcd7b
--- /dev/null
+++ b/vizro-ai/docs/overrides/canonical_tag.py
@@ -0,0 +1,13 @@
+# Add a canonical tag
+
+def on_page_markdown(markdown, *, page, config, files):
+    
+    # Get key from front matter
+    canonical_url = page.meta.get("canonical_url")
+    
+    # Skip processing page when key not found
+    if canonical_url is None:
+        return
+        
+    # Assign the custom url to the page
+    page.canonical_url = canonical_url
\ No newline at end of file
diff --git a/vizro-ai/docs/pages/tutorials/goodreads-tutorial.md b/vizro-ai/docs/pages/tutorials/goodreads-tutorial.md
index 83132b142..5a598d129 100644
--- a/vizro-ai/docs/pages/tutorials/goodreads-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/goodreads-tutorial.md
@@ -1,3 +1,7 @@
+---
+canonical_url: https://medium.com/towards-data-science/how-to-build-a-data-dashboard-prototype-with-generative-ai-462f1d91690d
+---
+
 # How to build a prototype dashboard with Vizro-AI
 
 This tutorial uses Vizro-AI to build a prototype dashboard with three charts that illustrate a simple dataset. We first show how to create individual charts with Vizro-AI and then show how to use Vizro-AI to build a dashboard. The tutorial concludes by moving the prototype code generated by Vizro-AI into a project on PyCafe, for others to use and extend.
diff --git a/vizro-ai/mkdocs.yml b/vizro-ai/mkdocs.yml
index 092e90f38..bb139f4d9 100644
--- a/vizro-ai/mkdocs.yml
+++ b/vizro-ai/mkdocs.yml
@@ -118,6 +118,9 @@ plugins:
   - git-revision-date-localized:
       enable_creation_date: false
 
+hooks:
+  - docs/overrides/canonical_tag.py 
+  
 extra_css:
   - stylesheets/extra.css
 

From d239ff50e36633f4505bcb458ecfab98706c5f8f Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Tue, 4 Feb 2025 14:18:57 +0000
Subject: [PATCH 40/44] Update goodreads-tutorial.md

---
 vizro-ai/docs/pages/tutorials/goodreads-tutorial.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vizro-ai/docs/pages/tutorials/goodreads-tutorial.md b/vizro-ai/docs/pages/tutorials/goodreads-tutorial.md
index 5a598d129..fe2380217 100644
--- a/vizro-ai/docs/pages/tutorials/goodreads-tutorial.md
+++ b/vizro-ai/docs/pages/tutorials/goodreads-tutorial.md
@@ -4,7 +4,7 @@ canonical_url: https://medium.com/towards-data-science/how-to-build-a-data-dashb
 
 # How to build a prototype dashboard with Vizro-AI
 
-This tutorial uses Vizro-AI to build a prototype dashboard with three charts that illustrate a simple dataset. We first show how to create individual charts with Vizro-AI and then show how to use Vizro-AI to build a dashboard. The tutorial concludes by moving the prototype code generated by Vizro-AI into a project on PyCafe, for others to use and extend.
+This tutorial uses Vizro-AI to build a prototype dashboard with three charts that illustrate a simple dataset. We first show how to create individual charts with Vizro-AI and then show how to use Vizro-AI to build a dashboard. The tutorial concludes by moving the prototype code generated by Vizro-AI into a project on PyCafe, for others to use and extend. A version of this tutorial was first published as a [blog post on Towards Data Science in January 2025](https://medium.com/towards-data-science/how-to-build-a-data-dashboard-prototype-with-generative-ai-462f1d91690d).
 
 ## Project explanation
 

From b4c26d68c7ea6fa13c6a5153c10b450413bf1f43 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Tue, 4 Feb 2025 14:24:35 +0000
Subject: [PATCH 41/44] Create check_for_datafiles.py

---
 tools/check_for_datafiles.py | 48 ++++++++++++++++++++++++++++++++++++
 1 file changed, 48 insertions(+)
 create mode 100644 tools/check_for_datafiles.py

diff --git a/tools/check_for_datafiles.py b/tools/check_for_datafiles.py
new file mode 100644
index 000000000..7dcbb5738
--- /dev/null
+++ b/tools/check_for_datafiles.py
@@ -0,0 +1,48 @@
+"""FUNCTION TO CHECK FOR DATA FILES IN NON-WHITELISTED FOLDERS."""
+
+import glob
+from pathlib import Path
+
+file_extensions = [
+    "csv",
+    "xlsx",
+    "xls",
+    "parquet",
+    "tsv",
+    "hdf5",
+    "h5",
+    "pickle",
+    "pkl",
+    "db",
+    "sqlite",
+    "sqlite3",
+    "orc",
+]
+whitelist_folders = [
+    "/venv",
+    "/vizro-core/docs",
+    "/vizro-ai/tests/integration/reports",
+]  # starting from project root dir
+
+
+def check_for_data_files():
+    """Recursively finds all data files in non-whitelisted folders.
+
+    Raises:
+        AssertionError if data files are present in non-whitelisted folders.
+
+    """
+    project_dir = str(Path(__file__).parent.parent)
+    whitelist_dir = {f"{project_dir}{dir}" for dir in whitelist_folders}
+
+    found_files = {file for ext in file_extensions for file in glob.glob(project_dir + f"/**/*.{ext}", recursive=True)}
+    whitelisted_files = {files for dir in whitelist_dir for files in found_files if files.startswith(dir)}
+    to_be_removed_files = found_files - whitelisted_files
+
+    assert len(to_be_removed_files) == 0, (
+        f"Caution! Please remove your data files {to_be_removed_files} before merging!"
+    )
+
+
+if __name__ == "__main__":
+    check_for_data_files()

From e3e9b1c82fafa5a5705b0ca0a192be15cd9cc89c Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 4 Feb 2025 14:25:16 +0000
Subject: [PATCH 42/44] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 vizro-ai/docs/overrides/canonical_tag.py | 8 ++++----
 vizro-ai/mkdocs.yml                      | 4 ++--
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/vizro-ai/docs/overrides/canonical_tag.py b/vizro-ai/docs/overrides/canonical_tag.py
index 3da2dcd7b..b17ca6a09 100644
--- a/vizro-ai/docs/overrides/canonical_tag.py
+++ b/vizro-ai/docs/overrides/canonical_tag.py
@@ -1,13 +1,13 @@
 # Add a canonical tag
 
+
 def on_page_markdown(markdown, *, page, config, files):
-    
     # Get key from front matter
     canonical_url = page.meta.get("canonical_url")
-    
+
     # Skip processing page when key not found
     if canonical_url is None:
         return
-        
+
     # Assign the custom url to the page
-    page.canonical_url = canonical_url
\ No newline at end of file
+    page.canonical_url = canonical_url
diff --git a/vizro-ai/mkdocs.yml b/vizro-ai/mkdocs.yml
index bb139f4d9..252f51ce2 100644
--- a/vizro-ai/mkdocs.yml
+++ b/vizro-ai/mkdocs.yml
@@ -119,8 +119,8 @@ plugins:
       enable_creation_date: false
 
 hooks:
-  - docs/overrides/canonical_tag.py 
-  
+  - docs/overrides/canonical_tag.py
+
 extra_css:
   - stylesheets/extra.css
 

From 8ed60275cbff8838ee506f9b707c5404a90171b1 Mon Sep 17 00:00:00 2001
From: Jo Stichbury <jo_stichbury@mckinsey.com>
Date: Tue, 4 Feb 2025 14:41:19 +0000
Subject: [PATCH 43/44] Update canonical_tag.py

---
 vizro-ai/docs/overrides/canonical_tag.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/vizro-ai/docs/overrides/canonical_tag.py b/vizro-ai/docs/overrides/canonical_tag.py
index b17ca6a09..88e21683b 100644
--- a/vizro-ai/docs/overrides/canonical_tag.py
+++ b/vizro-ai/docs/overrides/canonical_tag.py
@@ -1,7 +1,8 @@
-# Add a canonical tag
+""" Add a canonical tag. """
 
 
 def on_page_markdown(markdown, *, page, config, files):
+    """ Add a canonical tag. """
     # Get key from front matter
     canonical_url = page.meta.get("canonical_url")
 

From d0c25e1d41e1878a2c9a1ff0196cff14f6a3e1ce Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 4 Feb 2025 14:42:08 +0000
Subject: [PATCH 44/44] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 vizro-ai/docs/overrides/canonical_tag.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/vizro-ai/docs/overrides/canonical_tag.py b/vizro-ai/docs/overrides/canonical_tag.py
index 88e21683b..64ebdceef 100644
--- a/vizro-ai/docs/overrides/canonical_tag.py
+++ b/vizro-ai/docs/overrides/canonical_tag.py
@@ -1,8 +1,8 @@
-""" Add a canonical tag. """
+"""Add a canonical tag."""
 
 
 def on_page_markdown(markdown, *, page, config, files):
-    """ Add a canonical tag. """
+    """Add a canonical tag."""
     # Get key from front matter
     canonical_url = page.meta.get("canonical_url")