Welcome to DIVA’s documentation!

DIVA Screenshot DIVA Screenshot

DIVA: DestinE Intelligent Visualisation Artificial Intelligence

Demonstration

Explore DIVA’s capabilities through our online demonstration: → DIVA Demonstrator

Overview

DIVA (DestinE Intelligent Visualisation Artificial Intelligence) is an innovative prompt-to-chart language model designed to assist journalists in illustrating climate change data. By converting natural language prompts into detailed visualizations, DIVA provides past and future localized climate information. This tool empowers journalists to create impactful graphics that enhance storytelling and engage audiences by providing both historical context and future projections of climate change scenarios.

Features

  • Prompt-to-Chart Conversion: Enter natural language prompts to generate detailed climate visualizations.

  • Localized Data: Access climate information specific to your region, allowing for precise and relevant reporting.

  • Future Projections: Illustrate potential climate scenarios, helping readers understand possible future changes.

  • Past Trends: Showcase historical climate data to provide context and depth to your reporting.

  • Multi-language Support: Select your preferred language from the menu on the left, making DIVA accessible to a global audience.

  • Interactive Graphics: Engage users with interactive visualizations that offer deeper insights into the data.

  • Variety of Chart Types: Choose from a range of chart types, including line charts, bar charts, histograms, color stripes, and box plots, to best represent your data.

  • Brief Summary of Displayed Graph: Automatically generate a concise summary of the graph just displayed, providing an overview of key insights.

  • Export Options: Export the visualisations as images or download the underlying data in CSV format for further analysis.

Example Use Case

Illustrating Climate Change Forecasts for Journalism

As a journalist, you can leverage DIVA to create visual representations of climate change forecasts for your articles. For example, you might want to illustrate how temperature and precipitation patterns are expected to change in a specific city over the next 50 years. With DIVA, you can easily generate charts that communicate these scenarios effectively, enhancing the storytelling of your piece and engaging your audience with visually compelling data.

Installation and Usage

Prerequisites

Before you begin, ensure you have the following installed:

  • Python 3.x

  • Required Python packages (listed in requirements.txt)

  • Shell access for script execution + Admin right to allow port forwarding

The LLM is not supplied in the packaging because of its size. You can download it from HugginFace :

  • https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1

Launching the Project

To launch the DIVA service, navigate to the project’s root directory and execute the following command:

cd src
python -m streamlit run diva/gui/run_app.py

Example Conversation

🙋: Show me the temperature from January to September 2016.

🖥️: Thank you for your request. I can give you a graphical view of the temperature. Could you please specify where you want to make the study?

🙋: I want it in Frascati.

🖥️: #Display Graph

DIVA Screenshot
🙋: Give it to me by monthly average

🖥️: #Display Graph

DIVA Screenshot

This conversation illustrates the interactive nature of DIVA, allowing users to request specific data visualizations and receive tailored results.

Available data

Four variables are available in DIVA :

  • Temperature (2m from the surface)

  • Wind Speed

  • Precipitation

  • Surface Pressure

The historical data is extracted for now from ERA5 :

  • https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview

The dataset reanalysis-era5-single-levels contains multiple variables about the climate from 1940 to present, although diva only uses data from 1970 to present. The data are provided on a regular latitude-longitude grid with daily update frequency.

The available zone:

  • Longitude : from -14.0 to 45.7

  • Latitude : from 35.0 to 66.70

(A map showing this zone is available from the app (Information tab))

Used model

Diva only uses open-source european large language models. The current version relies especially on Mistral-7B-v1-Instruct and on the NLP libraries SpaCy and NLTK.

License

This project is licensed under the Apache License 2. See the LICENSE file for more details.

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