Build an AI-Powered Sport Guessing App with AI21 Labs & Streamlit

Harnessing the Power of AI: Building a Sport Identifier App with AI21 and Streamlit

Artificial intelligence (AI) continues to redefine the tech landscape, providing innovative tools for developers to create smart applications. In this article, we will explore how you can leverage AI21 Labs and Streamlit to build a simple yet effective application that identifies sports based on their descriptions. This project will not only highlight the ease of using these technologies but also provide practical insights on AI implementation.

What is AI21 Labs?

AI21 Labs offers a suite of powerful tools designed for developing AI and natural language processing (NLP) applications. Their models can be accessed through an official API/SDK, allowing developers to create innovative applications with scalability and efficiency.

What is Streamlit?

Streamlit is an open-source Python library that simplifies the creation of custom web applications. Its intuitive interface empowers developers to build prototypes rapidly, making it an ideal choice for projects that require quick development cycles.

Project Overview: Building a Sport Identifier App

In this tutorial, we aim to create a straightforward application that processes a sport description and returns the name of the sport. Utilizing both AI21's powerful models and Streamlit’s user-friendly interface, we can accomplish this task with minimal coding.

Step 1: Set Up Your Project

1. Create a new directory for your project and navigate into it.
2. Set up a virtual environment to manage your project dependencies.

Step 2: Install Required Dependencies

In your command line interface, install the necessary libraries for your project:

pip install streamlit python-dotenv requests

Step 3: Configure Your API Key

Create a .env file in your project directory to store your AI21 API key securely. This key will be crucial for accessing AI models.

Step 4: Create the Main Application File

Next, create a main.py file. In this file, begin by importing the required libraries:

import streamlit as st
from dotenv import load_dotenv
import os
import requests

load_dotenv()

Step 5: Define the Functionality

Create a function that accepts a sports description and returns the name of the corresponding sport. This function will utilize AI21's models to generate predictions:

def identify_sport(description):
    api_key = os.getenv('AI21_API_KEY')
    # Set up the request to AI21 APIs here
    return sport_name

Step 6: Create the User Interface

With Streamlit, we can design an interface for users to input their sport descriptions. Here is a basic example:

st.title('Sport Identifier')
user_input = st.text_input('Describe the sport:')
if st.button('Identify Sport'):
    result = identify_sport(user_input)
    st.write(f'The identified sport is: {result}')

Step 7: Run Your Application

Launch your application with the following command:

streamlit run main.py

Conclusion

Building applications using AI21 and Streamlit is both straightforward and engaging. With only basic programming knowledge, you can create interactive applications that harness the power of AI technologies. Be sure to explore the extensive documentation available for both platforms to discover more functionalities.

If you're interested in diving deeper into generative AI models or exploring different AI technologies, such as OpenAI Whisper, visit our AI tutorials page for more resources.

We also invite you to participate in our upcoming AI21 Hackathon to test the skills you have acquired through this tutorial. Join a vibrant community of builders, innovators, and creators who are shaping the future with AI!