Build Your Own GPT-3 Powered Application with Streamlit: A Step-by-Step Tutorial

What are we cooking today?

Ready to whip up some AI magic? This GPT-3 tutorial will guide you in crafting your own web application powered by the impressive GPT-3 from OpenAI. Using Python, Streamlit, and GitHub, you will learn the essentials of launching a GPT-3 powered application. This tutorial is perfect for those with a basic understanding of Python.

Getting Started with GPT-3

We'll be harnessing the power of GPT-3, OpenAI's 175 Billion parameter text transformer. This tutorial aims to provide a solid foundation for getting a GPT-3 powered web application up and running.

Step 1: Obtaining Your OpenAI API Key

To get started, you need to obtain your OpenAI API key. Follow these steps:

• Sign up on the OpenAI website.
• Once your account is created, go to your profile.
• Click on the "API Keys" tab, or simply use this link to access OpenAI API Keys.

Step 2: Clone the GPT-3 Boilerplate from GitHub

In this tutorial, we will be using a GPT-3 boilerplate that simplifies the process of building your own GPT-3 powered application. Here's how:

• Copy this repository: GPT-3-Boilerplate onto your computer.
• Add it to your own repositories.

Step 3: Running the Project Locally

Next, you will need to set up the project locally:

1. Navigate to your project folder.
2. Create a virtual environment of your choice following the guide from the Python 'venv' documentation.
3. Activate the virtual environment by running the command: ./venv/Scripts/activate
4. Install the necessary dependencies with: pip install -r requirements.txt
5. Run the application: streamlit run gpt_app.py
6. Your application will open in a browser window. Input your API key to proceed.

About the Boilerplate Application

The boilerplate application is a simple Poem generator. It generates a poem based on user-provided input words using the text-davinci-002 model from OpenAI, specified in the model.py file.

You can modify the model to explore other variations and play with different model parameters:

• Change the model to suit your needs.
• Adjust the temperature parameter to introduce randomness in poem generation. For example, setting temperature to 0.9 results in a more diverse output.
• Feel free to edit the prompt to reflect any specific style or theme you desire.

Step 4: Deploying the Application on Streamlit

Finally, it's time to deploy your application:

1. Go to Streamlit Sharing and register your account.
2. Click on New app.
3. Choose your repository, branch name, and the main file path (in our case, gpt_app.py).
4. Click Deploy!

Conclusion

This concludes our tutorial on building and deploying a GPT-3 powered web application. By following these steps, you can create your own unique applications powered by one of the most advanced AI models available. Start experimenting and see what magic you can create!