lambda_function.py

import tweepy
import openai
import os
from dotenv import load_dotenv

load_dotenv()

ACCESS_TOKEN= os.getenv('ACCESS_TOKEN')
ACCESS_TOKEN_SECRET= os.getenv('ACCESS_TOKEN_SECRET')
API_KEY = os.getenv('API_KEY')
API_KEY_SECRET = os.getenv('API_KEY_SECRET')
OPENAI_API_KEY= os.getenv('OPENAI_API_KEY')

# Set up the tweepy API client
auth = tweepy.OAuthHandler(API_KEY, API_KEY_SECRET)
auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)
twitter = tweepy.API(auth)

# Set up the openai API client
openai.api_key = OPENAI_API_KEY

def lambda_handler(event, context):

    # Define a function to generate a text using GPT-3
    def generate_story(prompt):
        response = openai.Completion.create(
            engine="text-davinci-003",
            prompt=prompt,
            max_tokens=event["max_tokens"],
            n=1,
            stop=None,
            temperature=event["temperature"],
        )

        generation = response["choices"][0]["text"]
        return generation

    # Use tweepy to tweet the story generated by the function
    def tweet_story(prompt):
        story = generate_story(prompt)[:280]
        twitter.update_status(story)
        return story

    res = tweet_story(event["prefix"] + ' ' + event["prompt"] + ' ' + event["suffix"])
    print(res)
    return {"statusCode": 200, "tweet": res}