Unleash the Power of OpenAI GPT-3 with Streamlit Python Web App

Table of Contents

1. Introduction
2. Creating a Streamlit App
3. Implementing OpenAI GPT-3 Model
4. Setting up API Keys
5. Importing Dependencies
6. Using Streamlit Chat Component
7. Storing User Interactions
8. Generating User Input
9. Storing Output and Input
10. Displaying Chat Bot Responses
11. Conclusion

Introduction

In this tutorial, we will be using OpenAI's GPT-3 model to Create a chat bot and implement it within our Streamlit app. We will walk through the process step by step, showcasing the capabilities of the chat bot and how to integrate it into your own projects.

Creating a Streamlit App

To begin, we will create a new file called chatbot.py and import the necessary dependencies. We will be using the Streamlit library for the app interface and the OpenAI library for the chat bot functionality. Additionally, we will be utilizing the Streamlit Chat component, a useful tool for interacting with the chat bot.

Implementing OpenAI GPT-3 Model

Next, we will set up the API keys required to access the OpenAI GPT-3 model. It is recommended to create your own API key to ensure sustainability and avoid excessive charges. Once the API key is obtained, it can be stored in a secure location, such as an environment variable, for easy access.

Setting up API Keys

To use the OpenAI API, we need to import the openai library and configure it with our API key. We can use the streamlit.secrets function to safely and securely store the API key and access it whenever needed.

Importing Dependencies

We will import the necessary libraries for our application, including openai for using the GPT-3 model and streamlit for creating the app interface. Additionally, we will import the Message class from the streamlit_chat module, which will allow us to Interact with the chat bot.

Using Streamlit Chat Component

We will utilize the Streamlit Chat component, developed by Yash Power, to create a user-friendly interface for interacting with the chat bot. This component provides a seamless way to input text Prompts and receive responses from the chat bot within our Streamlit app.

Storing User Interactions

In order to store the user's interactions with the chat bot, we will use the SessionState functionality provided by Streamlit. By utilizing the SessionState object, we can store the user's input and the chat bot's output for each interaction, allowing for a continuous conversation experience.

Generating User Input

To capture the user's input and generate a response from the chat bot, we will create a function called user_input. This function will utilize the streamlit library to get text input from the user. The input will then be passed to the GPT-3 model for generating a response.

Storing Output and Input

Once the user's input is received and processed by the chat bot, we will store both the input and the generated output using the SessionState object. This allows us to preserve the conversation history and display it within the app.

Displaying Chat Bot Responses

Finally, we will display the chat bot's responses within the app using the streamlit library. By looping over the stored inputs and outputs in the SessionState object, we can retrieve the conversation history and display it in a user-friendly format.

Conclusion

In this tutorial, we have learned how to create a chat bot using OpenAI's GPT-3 model and implement it within a Streamlit app. We explored the steps required to set up the API keys, import the necessary dependencies, utilize the Streamlit Chat component, store user interactions, generate user input, and display the chat bot's responses.