Automated generation of data visualizations and infographics using large language models and diffusion models
Systems and methods are provided for generating visualization data associated with raw data using a machine learning model. For example, the machine learning model may automatically generate a set of candidate analytics and/or a scenario for visualizing the raw data based on summary data. Given the summary data and answers to prompts for visualizing data, the generated candidate analytics may reflect a context of the raw data as intended by the user. A visualization code scaffold according to a visualization specification may be used to generate programmatic output that corresponds to the candidate analytics, which may thus be used to generate a visualization accordingly. In some examples, an infographic may further be generated based on the visualization and a prompt using a diffusion model.
1 . A method for generating visualization data based on a set of data, comprising:
receiving a set of raw data;
generating, based on the set of raw data, summary data using a first machine learning model;
generating, using a second machine learning model, one or more candidate analytics for the set of raw data that specify a context associated with a visualization of the set of raw data;
generating, based on a visualization code scaffold, the one or more candidate analytics, and the set of raw data, and using a third machine learning model, visualization programmatic code for rendering at least a part of the set of raw data according to the summary data;
generating visualization data associated with the set of raw data using visualization programmatic code; and
providing the visualization data for display to a user.
2 . The method of claim 1 , further comprising:
generating, based on the visualization data and a prompt, infographic data using a diffusion model.
3 . The method of claim 2 , wherein the prompt is received from a computing device of the user.
4 . The method of claim 1 , wherein the one or more candidate analytics are generated based on the summary data.
5 . The method of claim 1 , further comprising receiving a user selection of a candidate analytic from the generated one or more candidate analytics for the set of raw data.
6 . The method of claim 1 , wherein the third machine learning model is a multimodal generative machine learning model.
7 . The method of claim 1 , wherein the summary data comprises a compacted representation of the set of raw data.
8 . A system for generating visualization data based on a set of data, the system comprising:
a memory; and
a processor configured to execute steps comprising:
receiving a set of raw data;
generating, based on the set of raw data, summary data using a first machine learning model;
generating, using a second machine learning model, one or more candidate analytics for the set of raw data that specify a context associated with a visualization of the set of raw data;
generating, based on a visualization code scaffold, the one or more candidate analytics, and the set of raw data, and using a third machine learning model, visualization programmatic code for rendering at least a part of the set of raw data according to the summary data;
generating visualization data associated with the set of raw data using visualization programmatic code; and
providing the visualization data for display to a user.
9 . The system of claim 8 , the processor further configured to execute steps comprising:
generating, based on the visualization data and a prompt, infographic data using a diffusion model.
10 . The system of claim 9 , wherein the prompt is received from a computing device of the user.
11 . The system of claim 8 , wherein the one or more candidate analytics are generated based on the summary data.
12 . The system of claim 8 , the processor further configured to execute steps comprising:
receiving a user selection of a candidate analytic from the generated one or more candidate analytics for the set of raw data.
13 . The system of claim 8 , wherein the third machine learning model is a multimodal generative machine learning model.
14 . The system of claim 8 , wherein the summary data comprises a compacted representation of the set of raw data.
15 . A device for generating visualization data based on a set of data, the device comprising:
a memory;
a processor configured to execute a method comprising:
receiving a set of raw data;
generating, based on the set of raw data, summary data using a first machine learning model;
generating, using a second machine learning model, one or more candidate analytics for the set of raw data that specify a context associated with a visualization of the set of raw data;
generating, based on a visualization code scaffold, the one or more candidate analytics, and the set of raw data, and using a third machine learning model, visualization programmatic code for rendering at least a part of the set of raw data according to the summary data;
generating visualization data associated with the set of raw data using visualization programmatic code; and
providing the visualization data for display to a user.
16 . The device of claim 15 , the processor further configured to execute a method comprising:
generating, based on the visualization data and a prompt, infographic data using a diffusion model.
17 . The device of claim 16 , wherein the prompt is received from a computing device of the user.
18 . The device of claim 15 , wherein the one or more candidate analytics are generated based on the summary data.
19 . The device of claim 15 , the processor further configured to execute a method comprising:
receiving a user selection of a candidate analytic from the generated one or more candidate analytics for the set of raw data.
20 . The device of claim 15 , wherein the third machine learning model is a multimodal generative machine learning model, and
wherein the summary data comprises a compacted representation of the set of raw data.