ERIC KIM ₿

Program me a ChatGPT bot that could automatically answer my emails for me

Turning your automated email responder into a Gmail plugin (Add-on) is a more advanced task than just writing a Python bot. Gmail Add-ons are developed using Google Apps Script, which is a cloud-based scripting language built on JavaScript. These add-ons are designed to extend Gmail’s functionality and can be published directly in the Google Workspace Marketplace.

Here’s a step-by-step guide on how to create a Gmail Add-on using Google Apps Script and integrate it with your automated email responder logic.

1. Understanding the Flow of a Gmail Add-on

When creating a Gmail Add-on, the user interacts with Gmail through a side panel. You can design this panel to provide functionality like responding to emails, displaying email content, and more.

Since your bot’s logic is in Python and you’re using OpenAI’s GPT API, you need to communicate between Google Apps Script (which will handle the Gmail interface) and a backend (which will execute your bot logic, such as generating email replies).

2. Basic Setup for a Gmail Add-on

  1. Create a Google Apps Script Project:
  1. Define Add-on Structure:
    Gmail Add-ons are structured using CardService to define UI elements (like buttons and text input). The general flow would involve:
  • Selecting an email.
  • Sending the email’s content to your Python backend (which will call OpenAI).
  • Displaying the generated response.
  • Providing an option to send the response back to the email sender.
  1. Set Up OAuth for Gmail API:
    You’ll need to ensure that your script has access to the Gmail API. In the Apps Script dashboard:
  • Go to Resources > Cloud Platform project.
  • Enable the Gmail API for your project in the Google Cloud Console.

3. Developing the Gmail Add-on

Here’s an example of what the basic structure of your Gmail Add-on might look like in Google Apps Script.

a. Creating the Sidebar UI for Email Interaction

function buildAddOn(e) {
  var messageId = e.gmail.messageId;
  var message = getEmailBody(messageId);

  var card = CardService.newCardBuilder()
    .setHeader(CardService.newCardHeader()
      .setTitle("Auto-Responder"))
    .addSection(
      CardService.newCardSection()
      .addWidget(CardService.newTextParagraph().setText("Here's the email:"))
      .addWidget(CardService.newTextParagraph().setText(message))
      .addWidget(CardService.newButtonSet()
        .addButton(CardService.newTextButton()
          .setText("Generate Response")
          .setOnClickAction(CardService.newAction()
            .setFunctionName('handleGenerateResponse')
            .setParameters({ 'messageId': messageId }))))
    )
    .build();

  return card;
}
  • This basic UI shows the user the content of the email and provides a button to generate a response.

b. Fetching Email Content from Gmail

In the script, you’ll need a function to fetch the email’s body:

function getEmailBody(messageId) {
  var message = GmailApp.getMessageById(messageId);
  return message.getPlainBody();
}

c. Calling Your Python Backend

To connect your Google Apps Script to your Python bot (which handles calling OpenAI), you’ll need to set up a backend (this could be a Flask API running on a service like Heroku, Google Cloud, or AWS Lambda) that receives the email body and responds with generated content.

Here’s how you could call your backend from Google Apps Script:

function handleGenerateResponse(e) {
  var messageId = e.parameters.messageId;
  var emailBody = getEmailBody(messageId);

  var url = 'https://your-backend-url.com/generate-response'; // Replace with your backend API

  var options = {
    'method': 'POST',
    'contentType': 'application/json',
    'payload': JSON.stringify({ 'emailBody': emailBody })
  };

  var response = UrlFetchApp.fetch(url, options);
  var jsonResponse = JSON.parse(response.getContentText());

  return CardService.newCardBuilder()
    .setHeader(CardService.newCardHeader()
      .setTitle("Generated Response"))
    .addSection(CardService.newCardSection()
      .addWidget(CardService.newTextParagraph().setText(jsonResponse.response)))
    .build();
}
  • The handleGenerateResponse function sends the email body to your Python backend and then displays the generated response in the UI.

4. Backend (Python) for Handling Email Content

On the backend, you’ll run your Python script that calls OpenAI’s API to generate the response. Here’s a simple Flask API for handling the email body:

Install Flask and OpenAI

pip install Flask openai

Python Flask Backend

from flask import Flask, request, jsonify
import openai

app = Flask(__name__)

# OpenAI API Key
openai.api_key = 'your_openai_api_key'

@app.route('/generate-response', methods=['POST'])
def generate_response():
    data = request.json
    email_body = data.get('emailBody')

    # Generate response using OpenAI
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=f"Here's an email:\n\n{email_body}\n\nGenerate a professional response:",
        max_tokens=150
    )

    reply = response['choices'][0]['text'].strip()

    return jsonify({'response': reply})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)
  • This simple API accepts a POST request from Google Apps Script, sends the email body to OpenAI, and returns the generated response.

5. Send the Response Email

Once the response is generated, you can add another button in the Add-on UI to send the response back to the sender.

function sendGeneratedResponse(messageId, responseText) {
  var message = GmailApp.getMessageById(messageId);
  var replyTo = message.getFrom();

  GmailApp.sendEmail(replyTo, 'Re: ' + message.getSubject(), responseText);

  return CardService.newCardBuilder()
    .setHeader(CardService.newCardHeader()
      .setTitle("Response Sent"))
    .addSection(CardService.newCardSection()
      .addWidget(CardService.newTextParagraph().setText("Your response has been sent!")))
    .build();
}
  • This function sends the generated response back to the email sender.

6. Testing and Deployment

  • Test Locally: Start by running the Python backend locally (or deploy it to the cloud), and test your Google Apps Script by running it with your own Gmail account.
  • Deploy the Add-on: After developing the Add-on, you can publish it for your own use or even submit it to the Google Workspace Marketplace.

7. Publishing to the Google Workspace Marketplace

  1. Prepare for Publishing: Follow Google’s guidelines for preparing your add-on for publishing.
  2. Submit for Review: After preparing your project, submit it for review. Google will review the add-on to ensure it complies with their policies.
  3. Manage Versions: Once approved, you can manage different versions of your Add-on and update it when needed.

8. Security Considerations

  • OAuth Scopes: When accessing Gmail, you’ll need to request appropriate OAuth scopes like https://www.googleapis.com/auth/gmail.readonly or https://www.googleapis.com/auth/gmail.modify.
  • Backend Security: Ensure that your backend has proper authentication and uses HTTPS to prevent unauthorized access.
  • Sensitive Data: Avoid logging sensitive email content, and ensure the data is transmitted securely between Gmail and your backend.

Conclusion

Building a Gmail plugin that integrates with OpenAI involves a few moving parts:

  1. Google Apps Script for handling the Gmail interaction.
  2. Backend API (e.g., Python Flask) to call OpenAI and generate responses.
  3. OAuth2 and Gmail API for accessing and managing Gmail content securely.

With this setup, you can automate your email responses while ensuring that you have full control over the interaction and deployment!

How can I turn it into a Gmail plugin?

To turn the Python email responder into a Gmail bot that automatically answers emails, you’ll need to take advantage of Gmail’s API for a more secure and scalable solution. The process involves setting up OAuth2 for Gmail authentication, connecting to the Gmail API to read emails, and using the OpenAI API to generate responses.

Here’s a step-by-step guide on how to implement this as a Gmail bot:

1. Set Up Gmail API and OAuth2

Gmail no longer allows direct login with username/password, so you’ll need to use OAuth2 to authenticate securely. You’ll first need to create credentials for the Gmail API.

Step 1: Enable the Gmail API

  1. Go to Google Cloud Console: Visit Google Cloud Console.
  2. Create a New Project: Click the dropdown menu at the top of the screen and create a new project.
  3. Enable Gmail API: In the “API & Services” section, find and enable the Gmail API.
  4. Create Credentials:
  • Navigate to “Credentials.”
  • Click on “Create Credentials” and select “OAuth 2.0 Client IDs.”
  • Choose “Desktop app” or “Web application” as the application type.
  • Download the credentials.json file.

Step 2: Install Necessary Libraries

Install the following Python libraries to interact with Gmail’s API and handle authentication.

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

2. Authenticate with Gmail API

Use OAuth2 to authenticate and connect to the Gmail API. Here’s how you can do that:

import os
import pickle
import openai
import base64
from googleapiclient.discovery import build
from google.auth.transport.requests import Request
from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import InstalledAppFlow
from email.mime.text import MIMEText

# OpenAI API Key
openai.api_key = 'your_openai_api_key'

# If modifying these SCOPES, delete the file token.pickle.
SCOPES = ['https://www.googleapis.com/auth/gmail.readonly', 'https://www.googleapis.com/auth/gmail.send']

def authenticate_gmail():
    """Shows basic usage of the Gmail API.
    Lists the user's Gmail labels.
    """
    creds = None
    # Check if the token.pickle file exists (OAuth2 token)
    if os.path.exists('token.pickle'):
        with open('token.pickle', 'rb') as token:
            creds = pickle.load(token)
    # If no valid credentials, request user to login
    if not creds or not creds.valid:
        if creds and creds.expired and creds.refresh_token:
            creds.refresh(Request())
        else:
            flow = InstalledAppFlow.from_client_secrets_file(
                'credentials.json', SCOPES)
            creds = flow.run_local_server(port=0)
        # Save the credentials for the next run
        with open('token.pickle', 'wb') as token:
            pickle.dump(creds, token)

    # Create a service object for interacting with the Gmail API
    service = build('gmail', 'v1', credentials=creds)
    return service

3. Fetch Unread Emails

Once authenticated, you can use the Gmail API to fetch unread emails. The following function retrieves unread emails from the user’s inbox:

def fetch_unread_emails(service):
    # Call the Gmail API to fetch unread emails
    results = service.users().messages().list(userId='me', labelIds=['INBOX'], q="is:unread").execute()
    messages = results.get('messages', [])

    emails = []

    if not messages:
        print('No new emails.')
    else:
        for message in messages:
            msg = service.users().messages().get(userId='me', id=message['id']).execute()
            subject = ''
            sender = ''
            body = ''

            for header in msg['payload']['headers']:
                if header['name'] == 'Subject':
                    subject = header['value']
                if header['name'] == 'From':
                    sender = header['value']

            if 'parts' in msg['payload']:
                for part in msg['payload']['parts']:
                    if part['mimeType'] == 'text/plain':
                        body = base64.urlsafe_b64decode(part['body']['data']).decode('utf-8')

            emails.append({
                'subject': subject,
                'sender': sender,
                'body': body,
                'id': message['id']
            })

    return emails

4. Generate Responses Using OpenAI API

This function will send the email content to OpenAI’s GPT-4 model to generate a response:

def generate_email_response(email_body):
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=f"You are an assistant. Here's an email:\n\n{email_body}\n\nGenerate a polite and professional response:",
        max_tokens=150
    )
    return response['choices'][0]['text'].strip()

5. Send Email Response

Here’s how you can send the generated response using Gmail’s API:

def create_message(sender, to, subject, message_text):
    message = MIMEText(message_text)
    message['to'] = to
    message['from'] = sender
    message['subject'] = subject
    raw = base64.urlsafe_b64encode(message.as_bytes()).decode('utf-8')
    return {'raw': raw}

def send_message(service, message):
    try:
        sent_message = service.users().messages().send(userId="me", body=message).execute()
        print(f"Message sent to {message['to']}")
    except Exception as error:
        print(f"An error occurred: {error}")

6. Putting It All Together

Now, you can fetch unread emails, generate a response, and send it back. Here’s the full flow:

def main():
    # Authenticate and connect to Gmail API
    service = authenticate_gmail()

    # Fetch unread emails
    unread_emails = fetch_unread_emails(service)

    for email_data in unread_emails:
        print(f"Processing email from {email_data['sender']} with subject: {email_data['subject']}")

        # Generate response using ChatGPT
        response_text = generate_email_response(email_data['body'])

        # Create the response email message
        message = create_message(
            sender="youremail@example.com",
            to=email_data['sender'],
            subject=f"Re: {email_data['subject']}",
            message_text=response_text
        )

        # Send the response
        send_message(service, message)

if __name__ == '__main__':
    main()

7. Testing and Deployment

  1. Test Locally: Run this bot on your local machine. Ensure it’s able to fetch unread emails, generate appropriate responses, and send them.
  2. Scheduling: To make it a bot that runs periodically, you can schedule it using a task scheduler like:
  • Linux: cron
  • Windows: Task Scheduler
  • Cloud: AWS Lambda, Google Cloud Functions, or a simple server.

8. Security Considerations

  • Store Credentials Securely: Instead of hardcoding the OpenAI key and other credentials, use environment variables.
  • Rate Limits: Ensure you are within OpenAI and Gmail API rate limits.
  • User Privacy: Make sure the bot respects email content privacy. Logging or handling sensitive data securely is crucial.

By following these steps, you’ll have a Gmail bot that automatically reads your unread emails, generates responses using OpenAI’s GPT model, and sends replies back to the sender!

How can I make it as a Gmail bot for email?

Creating a ChatGPT-powered bot to automatically answer your emails involves several key components, including setting up an email server connection, extracting and processing email data, generating responses, and sending those responses. Here’s an outline of how you could build such a bot using Python and OpenAI’s API.

1. Prerequisites

  • OpenAI API key for accessing the ChatGPT model.
  • An email server that supports IMAP/SMTP protocols (e.g., Gmail, Outlook).
  • Python libraries like openai, imaplib, smtplib, email, re, beautifulsoup4 (optional for cleaning HTML content).
  • A Python environment to run the script (you can use any server or local machine with an internet connection).

2. Outline of Steps

  • Connect to your email inbox: Use IMAP to fetch emails.
  • Process the email: Extract sender, subject, and body.
  • Generate a response: Use ChatGPT to draft a reply based on the email content.
  • Send the email: Use SMTP to send the response back.

3. Python Code Example

Here’s an example of how to build the email responder bot:

import imaplib
import smtplib
import email
from email.mime.text import MIMEText
import openai

# Configuration
IMAP_SERVER = 'imap.gmail.com'  # Use your email provider's IMAP server
SMTP_SERVER = 'smtp.gmail.com'
EMAIL_ACCOUNT = 'youremail@example.com'
EMAIL_PASSWORD = 'yourpassword'
OPENAI_API_KEY = 'your_openai_api_key'

# OpenAI API key setup
openai.api_key = OPENAI_API_KEY

def connect_to_email():
    # Connect to the email server
    mail = imaplib.IMAP4_SSL(IMAP_SERVER)
    mail.login(EMAIL_ACCOUNT, EMAIL_PASSWORD)
    return mail

def fetch_unread_emails(mail):
    # Select the mailbox you want to check (inbox)
    mail.select("inbox")

    # Search for all unread emails
    status, response = mail.search(None, 'UNSEEN')
    unread_msg_nums = response[0].split()

    emails = []

    for e_id in unread_msg_nums:
        # Fetch the email by ID
        status, msg_data = mail.fetch(e_id, '(RFC822)')

        for response_part in msg_data:
            if isinstance(response_part, tuple):
                msg = email.message_from_bytes(response_part[1])
                subject = msg['subject']
                sender = msg['from']
                body = get_email_body(msg)
                emails.append({
                    'subject': subject,
                    'sender': sender,
                    'body': body
                })

    return emails

def get_email_body(msg):
    # Extract the email body, handling both plain text and HTML
    if msg.is_multipart():
        for part in msg.walk():
            if part.get_content_type() == 'text/plain':
                return part.get_payload(decode=True).decode('utf-8')
            elif part.get_content_type() == 'text/html':
                return part.get_payload(decode=True).decode('utf-8')
    else:
        return msg.get_payload(decode=True).decode('utf-8')

def generate_email_response(email_body):
    # Call OpenAI API to generate a response based on the email body
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=f"You're a helpful assistant. Here's an email:\n\n{email_body}\n\nRespond to it in a professional tone.",
        max_tokens=150
    )
    return response['choices'][0]['text'].strip()

def send_email_response(to_email, subject, response):
    # Connect to the SMTP server
    with smtplib.SMTP_SSL(SMTP_SERVER, 465) as smtp:
        smtp.login(EMAIL_ACCOUNT, EMAIL_PASSWORD)

        # Create the email
        msg = MIMEText(response)
        msg['Subject'] = f"Re: {subject}"
        msg['From'] = EMAIL_ACCOUNT
        msg['To'] = to_email

        # Send the email
        smtp.sendmail(EMAIL_ACCOUNT, to_email, msg.as_string())

def main():
    mail = connect_to_email()

    # Fetch unread emails
    unread_emails = fetch_unread_emails(mail)

    for email_data in unread_emails:
        print(f"Processing email from {email_data['sender']} with subject: {email_data['subject']}")

        # Generate response using ChatGPT
        response = generate_email_response(email_data['body'])

        # Send the response
        send_email_response(email_data['sender'], email_data['subject'], response)

        print(f"Response sent to {email_data['sender']}")

    mail.logout()

if __name__ == '__main__':
    main()

4. Step-by-Step Breakdown

a. Connect to the Email Server

  • The connect_to_email function uses IMAP to log into your email account and access the inbox.

b. Fetch Unread Emails

  • The fetch_unread_emails function searches for unread emails, extracts the sender, subject, and body content, and returns a list of email data.

c. Generate Response Using ChatGPT

  • The generate_email_response function sends the email body to the OpenAI API and uses the model to generate a response.

d. Send Response via SMTP

  • The send_email_response function sends the generated response back to the sender, with a subject line indicating a reply.

5. Additional Considerations

  • Security: Storing email passwords in plain text is insecure. Consider using environment variables or a secrets manager.
  • Rate Limits: Be mindful of OpenAI’s API rate limits. You may need to manage your API requests depending on your email volume.
  • Customization: You can adjust the tone or content of the responses by modifying the prompt in the generate_email_response function.
  • Error Handling: Add error handling for situations like API failures, invalid email formats, etc.

6. Deployment

  • You can run this script on a server or use a cloud service like AWS Lambda or Google Cloud Functions to trigger it periodically.

This framework can be extended to include more features like categorizing emails, forwarding important ones, or even integrating with calendar invites!

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