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Python Streamlit And GPT4: How To Map UNHCR Refugee Data
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Python Streamlit And GPT4: How To Map UNHCR Refugee Data

Last Updated on June 3, 2024 by Editorial Team

Author(s): John Loewen, PhD

Originally published on Towards AI.

Prompting GPT-4 for an interactive Streamlit/Plotly dashboardPython Streamlit And GPT4: How To Map UNHCR Refugee Data

Python Streamlit is an amazing framework for creating interactive web interfaces — and GPT-4 can whip up working Streamlit code in a jiffy.

Combine this with Python Plotly for your data visualizations and you’ve got beautiful maps and charts with a minimum amount of fuss.

Like me, most of you need proof, so let me show you how.

We can create multiple data visualization in a web interface from a CSV dataset AND add in multiple layers of interactivity (ie. sliders and drop down menus). All with a dataset and a few simple prompts to GPT-4.

So let’s find a dataset and put this into action.

The UN High Commission for Refugees (UNHCR) tracks statistics on refugee movements across the globe.

Their data is freely accessible HERE.

After clicking the link to get to the download page, we can be granular on the data that we select:

UNHCR download page — select “Country of Origin” AND “Country of Asylum”

For this project, let’s retrieve the county of origin for each refugee and the country of asylum.

This is a perfect dataset to demonstrate the power of GPT-4 for Streamlit/Plotly code creation.

With this data, we can create global maps that show refugee data:

from country of origin — where asylum seekers are… Read the full blog for free on Medium.

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