Using GPT-3 to Read Your Gmail with Python

GPT-3 is a powerful language processing model developed by OpenAI that can generate human-like text. In this post, we'll show you how to use GPT-3 and the Python programming language to connect to your Gmail account and read your email messages.

Prerequisites

Before we get started, you'll need to do the following:

• Set up a GPT-3 model and obtain the necessary credentials to access it
• Set up a Google account and authorize access to your Gmail account

Setting Up the GPT-3 Model

To use GPT-3 in your Python code, you'll need to install the openai library:

pip install openai

Next, we'll set up the GPT-3 model and specify a prompt to use when generating text. For this example, we'll use the "davinci" engine and prompt the model to generate text based on the subject of an email:

import openai # Set up the GPT-3 model model_engine = "davinci" prompt = "What is the subject of the email?" model = openai.OpenAI(model_engine)

Setting Up the Gmail API Client

To access your Gmail account, we'll use the Google API client library. First, install the library:

pip install google-auth google-auth-oauthlib google-auth-httplib2 google-api-python-client

Next, set up the Gmail API client and authenticate with your Google account:

import google.auth import google.auth.transport.requests import google.auth.transport.urllib3 import googleapiclient.discovery import googleapiclient.errors # Set up the Gmail API client scopes = ['https://www.googleapis.com/auth/gmail.readonly'] credentials = google.oauth2.credentials.Credentials.from_authorized_user_info(info=info, scopes=scopes) service = googleapiclient.discovery.build('gmail', 'v1', credentials=credentials)

Reading Your Email Messages

Now that we have the GPT-3 model and the Gmail API client set up, we can use them to read your email messages. First, we'll send a request to the Gmail API to retrieve a list of your email messages:

# Send a request to the Gmail API to retrieve a list of email messages

result = service.users().messages().list(userId='me', maxResults=10).execute()

messages = result.get('messages', [])

 [])

This will retrieve a list of the most recent email messages in your Gmail account. You can adjust the maxResults parameter to control the number of messages that are retrieved.

Next, we'll iterate through each message and use the GPT-3 model to generate text based on the subject of the message:

import base64 import email # Process each email message for message in messages: # Fetch the message from the Gmail API msg = service.users().messages().get(userId='me', id=message['id']).execute()

# Extract the subject from the message msg_str = base64.urlsafe_b64decode(msg['payload']['body']['data'].encode('UTF-8')) mime_msg = email.message_from_bytes(msg_str) subject = mime_msg['subject'] # Use the GPT-3 model to generate text based on the subject response = model.completion(engine=model_engine, prompt=prompt, max_tokens=1024, n=1,stop=None,temperature=0.5) text = response['choices'][0]['text'] print(text)

This code sets up the GPT-3 model and the Gmail API client, retrieves a list of the most recent email messages in your Gmail account, and then processes each message in turn. For each message, it extracts the subject, uses the GPT-3 model to generate text based on the subject, and prints the generated text to the console.

Keep in mind that this is just a simple example and you will likely need to modify the code to meet your specific needs. For example, you may want to customize the prompt that is passed to the GPT-3 model or handle more advanced tasks such as filtering the messages by sender or date.