How to run Vizro-AI

This guide offers insights into different ways of running Vizro-AI code, including within a Jupyter Notebook, as a Python script, or through integration into an application.

Jupyter Notebook

To run Vizro-AI code in a Jupyter Notebook, create a new cell and execute the code below to render the described visualization. You should see the chart as output.

Note: API key

Make sure you have followed the LLM setup guide and your api key is set up in a .env file in the same folder as your Notebook file (.ipynb).

Bar chart

Code for the cellResult

import vizro.plotly.express as px
from vizro_ai import VizroAI

vizro_ai = VizroAI()

df = px.data.gapminder()
vizro_ai.plot(df, "visualize the life expectancy per continent and color each continent")

Note that the chart's appearance may not precisely resemble the one displayed below, as it is generated by a generative AI and can vary.

Python script

You can use Vizro-AI in any standard development environment by creating a .py file and executing the following code. As a result, the rendered chart will display in a browser window.

Note: API key

Make sure you have followed LLM setup guide and your API key is set up in the environment where your .py script is running with command as below:

export OPENAI_API_KEY="your api key"

Line chart

example.pyResult

import vizro.plotly.express as px
from vizro_ai import VizroAI

vizro_ai = VizroAI()

df = px.data.gapminder()
fig = vizro_ai.plot(df, "describe life expectancy per continent over time")
fig.show()

Application integration

You may prefer to integrate Vizro-AI into an application with a UI that users use to input prompts using a text field.

There are two ways to integrate Vizro-AI into an application, directly and by accessing the chart code behind a fig object.

1. Vizro-AI's plot method returns a plotly.graph_objects object (fig) that can be used directly within a Vizro dashboard.

   Direct application integration

   app.py

   import vizro.plotly.express as px
   from vizro_ai import VizroAI
   
   vizro_ai = VizroAI()
   
   df = px.data.gapminder()
   fig = vizro_ai.plot(df, "describe life expectancy per continent over time")
   

2. Vizro-AI's _get_chart_code method returns a string of Python code that manipulates the data and creates the visualization. Vizro-AI validates the code to ensure that it is executable and can be integrated.

   Application integration via chart code

   app.pycode_string

   import vizro.plotly.express as px
   from vizro_ai import VizroAI
   
   vizro_ai = VizroAI()
   
   df = px.data.gapminder()
   code_string = vizro_ai._get_chart_code(df, "describe life expectancy per continent over time")
   

   

   The returned code_string can be used to dynamically render charts within your application. You may have the option to encapsulate the chart within a fig object or convert the figure into a JSON string for further integration.

   To use the insights or code explanation, you can use vizro_ai._run_plot_tasks(df, ..., explain=True), which returns a dictionary containing the code explanation and chart insights alongside the code.

How to use max_debug_retry parameter in plot function

• Default Value: 3
• Type: int
• Brief: By default, the max_debug_retry is set to 3, the function will try to debug errors up to three times. If the errors are not resolved after the maximum number of retries, the function will stop further debugging retries. For example, if you would like adjust to 5 retries, you can set max_debug_retry = 5 in the plot function:

vizro_ai.plot(df = df, user_input = "your user input", max_debug_retry= 5)