Fast API Endpoint for Automatic Tweet Posting in Twitter

Test this app for free
97
import os
import tweepy
from fastapi import FastAPI
from pydantic import BaseModel
import uvicorn

app = FastAPI()

class Message(BaseModel):
    text: str

@app.post("/post_message")
def post_message(message: Message):
    # Authenticate to Twitter
    auth = tweepy.Client(
        bearer_token=os.environ['TWITTER_BEARER_TOKEN'],
        access_token=os.environ['TWITTER_ACCESS_TOKEN'],
        access_token_secret=os.environ['TWITTER_ACCESS_TOKEN_SECRET'],
        consumer_key=os.environ['TWITTER_API_KEY'],
        consumer_secret=os.environ['TWITTER_API_SECRET_KEY']
    )

    # Create a tweet
    auth.create_tweet(text=message.text)
Get full code

Created: | Last Updated:

This app is a fast API endpoint that will automatically post a tweet provided via the API to a company's Twitter account. Can be used in your product to promote certain things happening, for example a product community member does something and you tweet automatically about it. Ideal for automatic tweets posting for company updates. Steps: 1. Sign up for a developer account on https://developer.twitter.com/ (you have to log in with the company's Twitter handle) 2. There's a free option (even though they are pushing the $100 per month basic option you don't need that to get started and for small volumes) 3. Navigate to projects and apps 4. From the consumer keys section copy the API key and Secret into Env Secrets tab in Lazy (make sure variable names are correct) 5. Generate an Access Token and Secret and copy them into Env Secrets tab in Lazy (make sure variable names are correct) THESE MUST HAVE READ AND WRITE ACCESS OTHERWISE IT WON'T WORK 6. Generate a bearer token and add to env secrets 7. Use the Fast API docs page to test app and try to post using the API endpoint or make a sample request directly. 8. Check your Twitter for the post appearing and voila!

Introduction to the Fast API Endpoint for Automatic Tweet Posting Template

Welcome to the Fast API Endpoint for Automatic Tweet Posting template! This template allows you to create an application that can automatically post tweets to a Twitter account. It's perfect for promoting events, sharing updates, or engaging with your audience without manual intervention. In this article, we'll guide you through the process of setting up and using this template on the Lazy platform.

Getting Started

To begin using this template, click on "Start with this Template" on the Lazy platform. This will pre-populate the code in the Lazy Builder interface, so you won't need to copy or paste any code manually.

Initial Setup: Adding Environment Secrets

Before you can start using the application, you'll need to set up some environment secrets. These are necessary for the app to authenticate with Twitter and post messages on your behalf. Here's how to obtain and set up these secrets:

  1. Sign up for a developer account at Twitter Developer Platform using your company's Twitter handle.
  2. Choose the free option during the sign-up process.
  3. Navigate to the "Projects and Apps" section and create a new app.
  4. From the "Keys and Tokens" section of your app, copy the API Key and API Secret Key.
  5. Generate an Access Token and Access Token Secret, ensuring they have both read and write access.
  6. Generate a Bearer Token for your app.
  7. Go to the Environment Secrets tab in the Lazy Builder and add the following secrets with the corresponding values you obtained:
    • TWITTER_BEARER_TOKEN
    • TWITTER_ACCESS_TOKEN
    • TWITTER_ACCESS_TOKEN_SECRET
    • TWITTER_API_KEY
    • TWITTER_API_SECRET_KEY

Test: Deploying the App

Once you have set up the environment secrets, you can deploy the app by pressing the "Test" button. This will launch the Lazy CLI and begin the deployment process. If the app requires any user input, you will be prompted to provide it through the CLI.

Using the App

After deployment, Lazy will provide you with a dedicated server link to interact with your new API. You can use this link to send POST requests to the "/post_message" endpoint with a JSON payload containing the message text you want to tweet.

Here's a sample request you might send to the API:

POST /post_message HTTP/1.1<br> Host: [Your Server Link]<br> Content-Type: application/json<br> <br> {   "text": "Hello, world! This is an automated tweet from Lazy." } And a sample response indicating success might look like this:

HTTP/1.1 200 OK<br> Content-Type: application/json<br> <br> {   "message": "Tweet successfully posted!" }

Integrating the App

If you want to integrate this app into your product or service, you can use the server link provided by Lazy to set up HTTP POST requests from your application. Ensure that the requests are authenticated and formatted correctly according to the Twitter API and FastAPI documentation.

Remember, this app is designed to work within the Lazy platform, so all the heavy lifting of deployment and environment management is handled for you. Just follow the steps above to get started with automatic tweet posting!

If you need further assistance or have any questions, feel free to reach out to Lazy's customer support for help.

Technologies

X (Twitter) X (Twitter)
FastAPI Templates and Webhooks FastAPI Templates and Webhooks

Similar templates

FastAPI endpoint for Text Classification using OpenAI GPT 4

This API will classify incoming text items into categories using the Open AI's GPT 4 model. If the model is unsure about the category of a text item, it will respond with an empty string. The categories are parameters that the API endpoint accepts. The GPT 4 model will classify the items on its own with a prompt like this: "Classify the following item {item} into one of these categories {categories}". There is no maximum number of categories a text item can belong to in the multiple categories classification. The API will use the llm_prompt ability to ask the LLM to classify the item and respond with the category. The API will take the LLM's response as is and will not handle situations where the model identifies multiple categories for a text item in the single category classification. If the model is unsure about the category of a text item in the multiple categories classification, it will respond with an empty string for that item. The API will use Python's concurrent.futures module to parallelize the classification of text items. The API will handle timeouts and exceptions by leaving the items unclassified. The API will parse the LLM's response for the multiple categories classification and match it to the list of categories provided in the API parameters. The API will convert the LLM's response and the categories to lowercase before matching them. The API will split the LLM's response on both ':' and ',' to remove the "Category" word from the response. The temperature of the GPT model is set to a minimal value to make the output more deterministic. The API will return all matching categories for a text item in the multiple categories classification. The API will strip any leading or trailing whitespace from the categories in the LLM's response before matching them to the list of categories provided in the API parameters. The API will accept lists as answers from the LLM. If the LLM responds with a string that's formatted like a list, the API will parse it and match it to the list of categories provided in the API parameters.

Icon 1 Icon 1
251

We found some blogs you might like...