Introduction to DeepSeek

In this article, we will be exploring the capabilities of DeepSeek, a large language model that is gaining significant attention for its ability to rival OpenAI's ChatGPT. We will be putting the DeepSeek-R1 model to the test by using it for a simple mapping task, and we will be evaluating how it holds up.

Introduction to the Task

Introduction to the task The task at hand is to plot the 20 most visited cities in the world on an interactive map. We will be using DeepSeek to retrieve the necessary data from web searches and then generate the map using the Folium library in Python.

Setting Up DeepSeek

Setting up DeepSeek To start, we need to set up DeepSeek and specify the task we want it to perform. We can do this by heading over to the DeepSeek website and providing the necessary instructions. In this case, we want DeepSeek to plot the 20 most visited cities in the world on an interactive map.

Retrieving Data from Web Searches

Retrieving data from web searches DeepSeek's R1 model retrieves data from web searches based on the specific request. This allows it to gather the most up-to-date information available. In this case, we are asking DeepSeek to find the 20 most visited cities in the world, along with their corresponding ranks and number of visitors.

Using the DeepSeek-R1 Reasoning Model

Using the DeepSeek-R1 reasoning model The DeepSeek-R1 reasoning model is a key component of the DeepSeek platform. It allows the model to analyze and select the most relevant data, even when conflicting information is present. This is especially useful in tasks like web searches, where multiple sources may provide different information.

Generating the Map

Generating the map Once DeepSeek has retrieved the necessary data, it can generate the map using the Folium library in Python. The map will display the 20 most visited cities in the world, along with their corresponding ranks and number of visitors.

Using Folium for Interactive Maps

Using Folium for interactive maps Folium is a powerful library for creating interactive maps in Python. It allows us to create a map that is not only visually appealing but also interactive, with features like hover-over text and clickable markers.

Creating a Pandas Data Frame

Creating a pandas data frame DeepSeek creates a pandas data frame to store the data it has retrieved. This data frame contains the names of the cities, their ranks, and the number of visitors.

Plotting the Map

Plotting the map With the data frame in place, DeepSeek can now plot the map using Folium. The map displays the 20 most visited cities in the world, with markers indicating the location of each city.

Adding Interactivity to the Map

Adding interactivity to the map DeepSeek adds interactivity to the map by allowing users to click on the markers to view more information about each city. This includes the name of the city, its rank, and the number of visitors.

Color-Coding the Markers

Color-coding the markers To make the map more visually appealing, DeepSeek color-codes the markers to indicate the rank of each city. The top five most visited cities are colored red, the next five are colored blue, and the remaining cities are colored green and yellow.

Displaying the Map

Displaying the map Finally, DeepSeek displays the map, complete with interactive markers and color-coded rankings. The map provides a clear visual representation of the 20 most visited cities in the world, making it easy to compare and contrast the different cities.

Conclusion

In conclusion, DeepSeek is a powerful tool for creating interactive maps and retrieving data from web searches. Its R1 reasoning model allows it to analyze and select the most relevant data, even when conflicting information is present. By using DeepSeek, we can create visually appealing and interactive maps that provide valuable insights into the data. Whether you're a developer, a researcher, or simply someone interested in exploring the capabilities of large language models, DeepSeek is definitely worth checking out.