Script using the ChatGPT API to summarize text

chatgpt-summarize.py

# pip install openai

import openai
import os

# export API key environment variable before
# running this script (see https://platform.openai.com/account/api-keys)
# E.g.,
#
# export OPENAI_API_KEY=copy_paste_key_from_your_account

openai.api_key  = os.getenv('OPENAI_API_KEY')


def run_chatgpt(prompt, model="gpt-3.5-turbo"):
    messages = [{"role": "user", "content": prompt}]
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=0,
    )
    return response.choices[0].message["content"]


text_to_summarize = """
Following the original transformer architecture, 
large language model research started to bifurcate in two directions: 
encoder-style transformers for predictive modeling tasks such as 
text classification and decoder-style transformers for generative modeling 
tasks such as translation, summarization, and other forms of text creation.
The BERT paper above introduces the original concept of 
masked-language modeling, and next-sentence prediction 
remains an influential decoder-style architecture. 
If you are interested in this research branch, I recommend 
following up with RoBERTa, which simplified the pretraining 
objectives by removing the next-sentence prediction tasks.
"""

prompt = f"""
Summarize the text below, delimited by backticks, 
in at most 3 sentences. 

Text: `{text_to_summarize}`
"""

response = run_chatgpt(prompt)
print(response)