Use Postman to call the /publish API: Send the message Hello from Flask to the /flask/mqtt topic. We can see the message sent from Flask in MQTTX. Complete code from flask import Flask, request, jsonify from flask_mqtt import Mqtt app = Flask(__name__) app.config['MQTT_BROKER_URL'...
You then import theSQLAlchemyclass from the Flask-SQLAlchemy extension, which gives you access to all the functions and classes from SQLAlchemy, in addition to helpers, and functionality that integrates Flask with SQLAlchemy. You’ll use it to create a database object that...
In this code block, you import theFlaskclass and therender_template()function from theflaskpackage. You use theFlaskclass to create your Flask application instance namedapp. Then you define aview function(which is a Python function that returns an HTTP response) calledhello()using theapp.route(...
In order to start working with the REST API through Python, you will need to connect a library to send HTTP requests. The choice of the library depends on the version of Python. If you use Python 2, we recommend using unirest because of its simplicity, speed, and ability to work with ...
Create simple Flask application First, we need to emulate a slow 3rd party API: # slow_api/api.pyimportosimportasynciofromaiohttpimportwebasyncdefhandle(request):delay=float(request.query.get('delay')or1)awaitasyncio.sleep(delay)returnweb.Response(text='slow api response')app=web.Application()...
I'm trying to make multiple requests async and get response back, I'm using concurrent.futures to do this, but inside my function using current_app which from flask and I always got this error: RuntimeError: Working outside of application context. I don't know how to resolve thi...
How to see whether a website uses an API Let’s create a little sandbox to demonstrate how it works. We can use Flask to make a simple internal API which will send only one string of text data. The ‘Access-Control-Allow-Origin’ header is added so we can simply open the HTML file...
To containerize our Python Flask application, we use the first stage with all the development dependencies to prepare our execution environment. We copy it onto a fresh Ubuntu base image to run it, configuring the model server’s gRPC connection. Frontend – Ubuntu-based NGINX container and Svel...
For further reading, check out API reference documentation.Conclusion In this article, we learned how to use Flask and Bigquery APIs to extract data from BigQuery datasets based on user query parameters. I am currently using these frameworks as part of the podcast dataset that I gave a sneak-...
api.py - Where you register models to be exposed via a REST-ful API app.py - Your "Flask" application, configuration, and database. auth.py - The authentication system used to protect access to the admin. main.py - this is the secret sauce models.py - Database models for use with ...