Revamping the "Understanding GUI" tutorial

docs/tutorials/articles/sales_dashboard/index.md

@@ -0,0 +1,56 @@
+---
+title: Creating a Sales Dashboard
+category: fundamentals
+data-keywords: gui vizelement chart navbar table layout part menu state multi-page callback
+short-description: Understand basic knowledge of Taipy by creating a multi-page sales dashboard.
+order: 1.5
+img: sales_dashboard/images/thumbnail.png
+---
+
+!!! note "Supported Python versions"
+    Taipy requires **Python 3.9** or newer.
+
+This tutorial focuses on creating a simple sales dashboard application. You'll learn about visual elements,
+interaction, styling, and multi-page applications.
+
+![Final Application](images/final_app.png){width=90% .tp-image-border}
+
+### Why Taipy?
+
+- **Speed:** Quickly develop robust applications.
+- **Simplicity:** Easy management of variables and events.
+- **Visualization:** Intuitive and clear visual elements.
+
+Each step in this **Tutorial** builds on the previous one. By the end, you'll be ready to
+create your own Taipy applications.
+
+This tutorial is also available in video format:
+
+<p align="center">
+  <a href="https://youtu.be/4F-266YnTkM" target="_blank">
+    <img src="images/yt-thumbnail.png" alt="Youtube Tutorial" width="50%"/>
+  </a>
+</p>
+
+### Installation
+
+Ensure you have Python 3.9 or newer, then install Taipy and Plotly:
+
+```bash
+pip install taipy plotly
+```
+
+!!! info
+    Use `pip install taipy` for the latest stable version. Need help with pip? Check out
+    the [installation guide](http://docs.python-guide.org/en/latest/starting/installation/).
+
+The dataset used in this tutorial is the 
+[SuperStore Sales dataset](https://www.kaggle.com/datasets/rohitsahoo/sales-forecasting) 
+available [here](https://github.com/Avaiga/taipy-course-gui/blob/develop/data.csv).
+
+## Tutorial Steps
+
+1. [Visual Elements](step_01/step_01.md)
+2. [Styling](step_02/step_02.md)
+3. [Charts](step_03/step_03.md)
+4. [Multipage](step_04/step_04.md)

docs/tutorials/articles/sales_dashboard/step_01/step_01.md

@@ -0,0 +1,163 @@
+---
+hide:
+  - toc
+---
+
+The full code for this step is available 
+[here](https://github.com/Avaiga/taipy-course-gui/blob/develop/2_visual_elements/main.py){: .tp-btn target='blank' }
+
+Let's start by creating a simple page with 3 components: a selector to select a category of items, 
+a bar chart which displays the sales of the top 10 countries for this category and 
+a table which displays data for the selected category
+
+![Step 1 Application](images/simple_app.png){ width=90% : .tp-image-border }
+
+Let's start by importing the necessary libraries:
+
+=== "Python"
+    ```python
+    from taipy.gui import Gui
+    import taipy.gui.builder as tgb
+    import pandas as pd
+    ```
+=== "Markdown"
+    ```python
+    from taipy.gui import Gui
+    import pandas as pd
+    ```
+
+We can now start creating the page. We will first add a [selector](../../../../refmans/gui/viselements/generic/selector.md).
+
+=== "Python"
+    ```python
+    with tgb.Page() as page:
+        tgb.selector(value="{selected_category}", lov="{categories}", on_change=change_category)
+    ```
+=== "Markdown"
+    ```python
+    page = """
+    <|{selected_category}|selector|lov={categories}|on_change=change_category|>
+    """
+    ```
+
+Taipy [visual elements](../../../../refmans/gui/viselements/index.md) take many properties. 
+Note that dynamic properties use a quote and brackets syntax. We use `value="{selected_category}"` 
+to signal to Taipy that `selected_category` should change when the user uses the selector. 
+Likewise, if `categories` changes, the selector will get updated with the new values. 
+
+Here, selector needs an associated string variable which will change when a user selects a value, 
+a list of values (lov) to choose from, and a callback function to call when the value changes. 
+We can define them above:
+
+```python
+data = pd.read_csv("data.csv")
+selected_category = "Furniture"
+categories = list(data["Category"].unique())
+
+def change_category(state):
+    # Do nothing for now, we will implement this later
+    return None
+```
+
+We can now add a chart to display the sales of the top 10 countries for the selected category.
+
+=== "Python"
+    ```python
+        tgb.chart(
+            data="{chart_data}",
+            x="State",
+            y="Sales",
+            type="bar",
+            layout="{layout}",
+        )
+    ```
+=== "Markdown"
+    ```
+    <|{chart_data}|chart|x=State|y=Sales|type=bar|layout={layout}|>
+    ```
+
+Taipy charts have many properties. You can create multiple traces, add styling, change the type of chart, etc.
+
+=== "Python"
+    ```python
+    data = {"x_col": [0, 1, 2], "y_col1": [4, 1, 2], "y_col_2": [3, 1, 2]}
+    with tgb.Page() as page:
+        tgb.chart("{data}", x="x_col", y__1="y_col1", y__2="y_col_2", type__1="bar", color__2="red")
+    ```
+=== "Markdown"
+    ```python
+    data = {"x_col": [0, 1, 2], "y_col_1": [4, 2, 1], "y_col_2":[3, 1, 2]}
+    Gui("<|{data}|chart|x=x_col|y[1]=y_col_1|y[2]=y_col_2|type[1]=bar|color[2]=red|>").run()
+    ```
+
+
+You can check the syntax for charts [here](../../../../refmans/gui/viselements/generic/chart.md). 
+
+You 
+can also directly embed Plotly charts using the `figure` property as we will do in [Step 3](../step_03/step_03.md).
+
+Here we need to provide a Pandas Dataframe with the data to display, the x and y columns to use, the type of chart,
+and a layout dictionary with additional properties.
+
+```python
+chart_data = (
+    data.groupby("State")["Sales"]
+    .sum()
+    .sort_values(ascending=False)
+    .head(10)
+    .reset_index()
+)
+
+layout = {"yaxis": {"title": "Revenue (USD)"}, "title": "Sales by State"}
+```
+
+Lastly, we can add a table to display the data for the selected category.
+
+=== "Python"
+    ```python
+        tgb.table(data="{data}")
+    ```
+=== "Markdown"
+    ```
+    <|{data}|table|>
+    ```
+
+We can now run the application using:
+
+```python
+if __name__ == "__main__":
+    Gui(page=page).run(title="Sales", dark_mode=False, debug=True)
+```
+
+`debug=True` will display a stack trace of the errors if any occur. 
+You can also set `use_reloader=True` to automatically reload the page 
+when you save changes to the code and `port=XXXX` to change the server port.
+
+The application runs but has no interaction. We need to code the callback function
+to update the chart and table when the user selects a category.
+
+```python
+def change_category(state):
+    state.data = data[data["Category"] == state.selected_category]
+    state.chart_data = (
+        state.data.groupby("State")["Sales"]
+        .sum()
+        .sort_values(ascending=False)
+        .head(10)
+        .reset_index()
+    )
+    state.layout = {
+        "yaxis": {"title": "Revenue (USD)"},
+        "title": f"Sales by State for {state.selected_category}",
+    }
+```
+
+## State
+
+Taipy uses a `state` object to store the variables per client.
+The syntax to update a variable will always be `state.variable = new_value`.
+
+State holds the value of all the variables used in the user interface for one specific connection.
+
+Modifying `state.data` will update data for one specific user, without modifying `state.data` for other users
+or the global `data` variable. You can test this by opening the application in a separate incognito window.