com.openai.unity VS streamlit

I am trying to block the OpenAI website through Defender web filtering for Windows and macOS. It seems to block fine on Edge, but not for Chrome when trying to block the subdomains like chat.openai.com. If we try openai.com then this gets blocked fine on both Chrome and Edge.

NeuralPit platform allows team members to collaborate on projects and seamlessly upload and analyze data in various formats, such as PDF, DOCX, Excel, CSV, video, audio, website URLs, YouTube links, and images. Allow teams to unearth insights, brainstorm ideas, and visualize and interact with data anytime, anywhere. NeuralPit provides small and medium businesses, as well as professionals, with access to features similar to those of Microsoft Copilot and OpenAI, but at significantly lower costs, particularly for teams.

If you have been keeping up to date with the Spring ecosystem, you may have heard about the Spring AI Project, It is currently in its pre-release state but it provides an innovative abstraction toolkit fostering AI integration across applications. The experimental Spring AI project was introduced during the SpringOne conference and allows the creation of AI applications by using common concepts of Spring. Currently, the project integrates Azure OpenAI and OpenAI as AI backends. Use cases like content generation, code generation, semantic search, and summarization are supported by the project.

The official Todoist website can be found at https://todoist.com, and information about OpenAI is available at https://openai.com. The names Todoist and OpenAI as well as related names, marks, emblems, and images are registered trademarks of their respective owners.

A Generative Pre-Trained Transformer (GPT) is a type of Large Language Model (LLM) and they are the hot topic in the technology world this year and many companies are scrambling to add this technology to their products. Creating and training these large models can be a very complex, time consuming and expensive. You may think that you cannot use this technology since it is so complex and expensive but companies like OpenAI have done a ton of work to create useful models and setup platforms exposing APIs to use them. If you have ever used an API where you send some data in, it does some magic behind the scenes and you get some data to use in a response, then you can integrate this cutting edge technology into your application. Let’s take a look at how we can setup a Full stack web app which lets us ask questions sent to OpenAI and stream the response.

OpenAI

Streamlit (https://streamlit.io/)

Does it need to be live (i.e when database or underlying spreadsheet updates does it need to be reflected in real time on the dashboard) or are you ok with static display.

Live updating data is a pain I've messed around using javascript to force refresh html iframes on a timer. But I was never really satisfied with this. I've heard you can do things with websockets but that is starting to get too complicated for me (I'm not a programmer).

For static stuff one of the data scientists in my org pointed me to Streamlit (https://streamlit.io/) it's a python package I found very easy to use. Can easily combine SQL with CSV imports and display them all on one dashboard. Can use forms toggle butotns etc to control the display.

Note that there are many tools that make this easier/simpler to prototype, including chainlit, streamlit, etc… The backend API we built is amenable to interacting with them as well.

2.-Go to https://streamlit.io, log in, and create a new app from your GitHub repository.

Developed using Langchain and Streamlit technologies for enhanced performance.

Hello,

Have you ever seen the https://streamlit.io/ ? I think this is what you are looking for.

While building dashboards in Streamlit, I found myself really missing Buefy's (Bulma) modern web components.

Specially due to the inability to add new values to Streamlit's multiselect [1], some missing controls like a polished image carousel [2] or a highly customizable data table.

Long story short, we put together streamfy (Streamlit + Buefy) as an MIT licensed project in GitHub to bring Buefy to Streamlit.

Demo: https://streamfy.streamlit.app

All the form components are implemented, missing half of other non-form UX components. There is plenty of room for PRs, testing, feedback, documentation, example, etc.

Please send issues and contributions to GitHub project [3] and general feedback to X / Twitter [4]

Thanks!

[1] https://github.com/streamlit/streamlit/issues/5348

Here's your savior, let's welcome Streamlit.

Looks cool. How do you see this differing from streamlit? https://streamlit.io/

Imagine you have an AI-powered personal alerting chat assistant that interacts using up-to-date data. Whether it's a big move in the stock market that affects your investments, any significant change on your shared SharePoint documents, or discounts on Amazon you were waiting for, the application is designed to keep you informed and alert you about any significant changes based on the criteria you set in advance using your natural language. In this post, we will learn how to build a full-stack event-driven weather alert chat application in Python using pretty cool tools: Streamlit, NATS, and OpenAI. The app can collect real-time weather information, understand your criteria for alerts using AI, and deliver these alerts to the user interface.