AI21 Labs & Streamlit: Build a Sport Guessing App Tutorial

Discovering AI21 and Streamlit for AI/NLP Applications

In the ever-evolving field of artificial intelligence and natural language processing, AI21 Labs and Streamlit are revolutionizing how developers can create innovative applications. AI21 Labs provides a rich set of tools that allow access to advanced models via its official API and SDK, making it easier for developers to integrate AI into their projects.

Understanding AI21 Labs

AI21 Labs specializes in developing powerful AI models that can understand and generate human-like text. Their suite of tools enables developers to build applications that leverage these cutting-edge technologies, facilitating everything from simple text manipulation to complex AI-driven solutions.

Streamlit: The Open-Source Solution for Web Apps

Streamlit is an open-source Python library that allows developers to create customizable web applications quickly. With Streamlit, you can bring data science and machine learning projects to life with ease, transforming scripts into engaging user interfaces in no time.

Creating a Sports Recognition App with AI21 and Streamlit

In this article, we'll demonstrate how to build a simple application that identifies a sport based on its description using AI21's model and Streamlit.

Setting Up the Project

1. Create a new directory for the project and navigate to it.
2. Set up a virtual environment:

python -m venv myenv

3. Install the required dependencies:

pip install streamlit requests python-dotenv

4. Create a .env file to store your API key safely.

Building the Application

After setting up your environment, it's time to start coding!

1. Define the API key in your .env file.

2. Import the necessary libraries in your main.py file:

import streamlit as st
import requests
dotenv.load_dotenv()

3. Create a function that accepts a sport description and returns the corresponding sport name:

def get_sport_name(description):
    # Define your AI21 model parameters
    response = requests.post(, json={'prompt': description})
    return response.json()['output']

4. Develop the Streamlit interface to get user input for the sport description:

sport_description = st.text_input('Enter a description of a sport:')
if st.button('Identify Sport'):
    sport_name = get_sport_name(sport_description)
    st.write(f'The identified sport is: {sport_name}')

Running the Application

To run the app, use the command:

streamlit run main.py

Your application will open in a new tab, where you can test it! Simply enter a description of a sport, and the app will identify it based on the provided text.

Conclusion

This tutorial highlights the simplicity and power of using AI21 and Streamlit to build a functional AI application. With basic programming skills and the wealth of documentation available, you can create your projects with ease.

If you want to delve deeper into generative AI models or explore more about building applications with various AI technologies, visit our AI tutorials page.

Consider joining our upcoming AI21 Hackathon to apply the skills you've learned and further your expertise in AI development!

Join the community at lablab.ai and start building your AI-powered applications today!