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'...
fromflaskimportFlask,request,jsonifyfromPILimportImageimportpytesseract app=Flask(__name__)@app.route('/ocr',methods=['POST'])defocr_process():ifrequest.method=='POST':image_file=request.files['image']image_data=Image.open(image_file)# Perform OCR using PyTesseracttext=pytesseract.image_to_str...
If setting the database field to DECIMAL returns a Python Decimal object, then it's possible that the Decimal object is not being jsonified (the Python json module does not necessarily jsonify objects in a way that you expect). Look at the database response before you jsonify it and see....
Next, create a new folder in the root directory and name it services. Inside this folder, create a new file, products.py, and add the code below to set up a Flask server. import requests import os from flask import Flask, jsonify app = Flask(__name__) port = int(os.environ.get(...
Create a new Python file namedchatgpt_plugin.pyand open it in your favorite code editor. Add the following code to thechatgpt_plugin.pyfile: fromflaskimportFlask, request, jsonifyimportopenai app = Flask(__name__) openai.api_key ='YOUR_OPENAI_API_KEY'@app.route('/chat', methods=['POS...
$python3--versionPython3.8.2 1. 2. Use Pip to install the Flask-MQTT library. 复制 pip3installflask-mqtt 1. Use Flask-MQTT We will adopt the Free public MQTT broker provided by EMQ, which is created on the basis of MQTT cloud service - EMQX Cloud. ...
from flask import Flask, request, jsonify from ocr import process_image _VERSION = 1 # API version In this, I’m adding “process_image(),” one of the OCR Engine functions in JSON response. JSON is used to collect data that is entering in and out of the API. We make use of the...
jsonify function are the same as to the dict constructor. When this request is passed into the dumps( ) function from the jsonify wrapper they are converted by the one best suited out of the above 4 mentioned ways. Now, once the data is retrieved, the data is in form of a python ...
Currently, I am developing a Teams Bot in Python (Flask). Due to certain circumstances, I am proceeding with bot development without using BotFramework. I have been able to return an AdaptiveCard based on user messages. However, when I press the button
(documents, service_context=service_context) index.save_to_disk('index.json') return jsonify({'status': 'success', 'response': 'Data loaded successfully'}) load() @app.route("/chatgpt", methods=['GET']) def chatbot(): index = GPTSimpleVectorIndex.load_from_disk('index.json...