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How to Create a Bridge Between OpenAI ChatGPT and your applications

With this post, you will accomplish the following: Set up a Flask app on an Ubuntu server hosted on Azure to create a bridge between ChatGPT-4 and your applications. The Flask app listens on port 8080 and exposes an API endpoint to generate text based on the provided input. Secure the Flask app with an SSL certificate using Let's Encrypt and Certbot, ensuring that the API is accessible over HTTPS. Configure Nginx as a reverse proxy to forward requests to the Flask app, enabling the app to be accessed using your custom domain name. Create a Web Source Module in Oracle APEX to consume the Flask app's API. This involves configuring the API endpoint, HTTP method, authentication, and required parameters in APEX. The Web Source Module can be used in various components within your APEX application, such as Interactive Reports, Classic Reports, or Interactive Grids. Throughout the post, I will provide detailed instructions, code snippets, and troubleshooting steps to guide you in setti...
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Instalación JFLEX y CUP

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Instalación JFLEX y CUP 1-     Descargar NetBeans y JDK: El primer paso es descargar Netbeans, pueden hacerlo descargando este jdk que ya incluye Netbeans con JDK 7, http://www.oracle.com/technetwork/java/javase/downloads/jdk-7-netbeans-download-432126.html 2-      Instalar NetBeans Si descargamos NetBeans desde el link anterior, solo basta hacer doble clic sobre él y seguir los pasos de instalación. 3-      Descargar JFLEX y CUP Descargamos JFLEX y CUP desde los siguientes links, http://jflex.de/ http://www2.cs.tum.edu/projects/cup/ 4-     Agregar JFLEX y CUP al proyecto              Ahora creamos nuestro proyecto a trabajar desde Netbeans con JFLEX y CUP y copiamos           los .jar de nuestra descarga.                JFlex lo encontramo...
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Machine Learning - Decision Trees and Random Forests for Classification and Regression - Python Example using Real Data

 I've written the following code as an example of an application for Decision Trees and Random Forests for Classification and Regression using Python code and libraries with a downloadable CSV with real data about US housing prices and characteristics. The Use Case You want to predict the price of a property using training data for the machine learning supervised algorithms of Random Forests and Classification and Regression. The principle is very simple, you have a set of historic data, this can be separated into training data and test data for our algorithms, and determine the accuracy of each algorithm to see which one is better for this sample data. CSV File The file and be downloaded from my repository here . https://github.com/Markuspg1/machine-learning1 Loading CSV File Function Here's a python function that will help you upload any csv file going forward. Using the following code you can test this function, we use it now to upload the CSV into our df variable. Use the f...
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