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...
pip install -r requirements.txt 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__...
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...
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 ...
publish(request_data['topic'], request_data['msg']) return jsonify({'code': publish_result[0]}) Run Flask application When the Flask application is started, the MQTT client will connect to the server and subscribe to the topic /flask/mqtt. if __name__ == '__main__': app.run(...
The next step is to install the necessary dependencies to host ChatGPT locally. Add the following code to a new file called app.py: from flask import Flask, request, jsonify from transformers import GPT2LMHeadModel, GPT2Tokenizer app = Flask(__name__) tokenizer = GPT2Tokenizer.from_pretrai...
fromflaskimportFlask, jsonify, requestfromwerkzeug.middleware.proxy_fiximportProxyFix# Initialize Flask appapp = Flask(__name__)# Middleware to handle proxy headers when behind a reverse proxyapp.wsgi_app = ProxyFix(app.wsgi_app, x_proto=1, x_host=1)# Sample data for demonstration purposesusers...
return jsonify({'token' : token.decode('UTF-8')}) return make_response('could not verify', 401, {'WWW.Authentication': 'Basic realm: "login required"'}) Copy Still, within the virtual environment, create another route in theapp.pyfile to get or retrieve all registered users. ...
We use the SerpAPIWrapper to add this feature. @app.route('/search', methods=['POST']) def search_with_assistant(): data = request.get_json() if not data: return jsonify({"error": "No data provided"}), 400 messages = data.get("message") llm = ChatAnthropic() # Get the last...
Below is a simplified example of how it sends a trade command to NinjaTrader: from flask import Flask, request, jsonify from werkzeug.serving import make_server import threading import time import logging import uuid import psutil import pystray from PIL import Image import os import winreg app ...