How to Use Jupyter with ChatGPT

Jupyter is an open-source web application that allows users to create and share documents that contain live code, equations, visualizations, and narrative text. It is widely used in data science and machine learning, among other fields. ChatGPT, on the other hand, is a conversational AI model that can generate human-like responses to natural language inputs. In this blog post, we will discuss how to use Jupyter with ChatGPT to create interactive chatbots that can answer questions and carry out tasks. You can also do this for free on Saturn Cloud and get more computing power as well as connect securely to your data in your cloud.

Step 1: Install Jupyter and ChatGPT

The first step is to install Jupyter and ChatGPT on your local machine. Jupyter can be installed using Anaconda, a popular data science platform that includes Jupyter and other useful tools. To install Anaconda, go to the Anaconda website and download the appropriate version for your operating system. Once installed, you can launch Jupyter by opening the Anaconda Navigator and clicking on the Jupyter icon.

ChatGPT, on the other hand, can be installed using the pip package manager, which is included with Python. Open a terminal window and type the following command:

pip install transformers

This will install the transformers package, which includes the ChatGPT model.

Step 2: Create a Jupyter notebook

Once you have installed Jupyter and ChatGPT, you can create a new Jupyter notebook by clicking on the “New” button in the Jupyter dashboard and selecting “Python 3” from the dropdown menu. This will open a new notebook in your web browser.

Step 3: Import the necessary libraries

To use ChatGPT in your Jupyter notebook, you need to import the transformers library and the ChatGPT model. You can do this by adding the following code to a code cell in your notebook:

from transformers import pipeline, set_seed

generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B')
set_seed(42)

This code creates a new ChatGPT pipeline using the EleutherAI/gpt-neo-2.7B model, which is one of the largest and most powerful GPT models available. The set_seed() function sets the random seed for the model, which ensures that it generates the same output for the same input every time.

Step 4: Define the chatbot function

Next, you need to define a function that takes a user input and generates a response using the ChatGPT model. You can do this by adding the following code to a new code cell in your notebook:

def chatbot(user_input):
    response = generator(user_input, max_length=1000, do_sample=True, temperature=0.7)
    return response[0]['generated_text']

This code defines a new function called chatbot() that takes a user input as an argument and generates a response using the ChatGPT model. The max_length parameter specifies the maximum length of the generated text, while the temperature parameter controls the randomness of the generated text.

Step 5: Test the chatbot function

To test the chatbot function, you can call it with a sample input and see what response it generates. You can do this by adding the following code to a new code cell in your notebook:

chatbot("Hello, how are you?")

This code calls the chatbot() function with the input “Hello, how are you?” and generates a response using the ChatGPT model. The output should be a generated text that responds to the user input in a natural language.

Step 6: Create an interactive chatbot

To create an interactive chatbot, you can use the Jupyter interactive widgets library, which allows you to create interactive widgets that can be used to control the behavior of your code. You can do this by adding the following code to a new code cell in your notebook:

from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets

def chatbot_interactive(user_input):
    response = generator(user_input, max_length=1000, do_sample=True, temperature=0.7)
    return response[0]['generated_text']

interact(chatbot_interactive, user_input=widgets.Text(value='Hello, how are you?', description='Input:'))

This code creates a new function called chatbot_interactive() that takes a user input as an argument and generates a response using the ChatGPT model. The interact() function creates a new interactive widget that allows the user to input text and see the chatbot’s response in real-time.

In this blog post, we have discussed how to use Jupyter with ChatGPT to create interactive chatbots that can answer questions and carry out tasks. We have covered the steps involved in installing Jupyter and ChatGPT, creating a Jupyter notebook, importing the necessary libraries, defining the chatbot function, testing the chatbot function, and creating an interactive chatbot using Jupyter widgets. With these tools and techniques, you can create powerful and engaging chatbots that can interact with users in a natural language.