💓Thank you so much for watching guys! I would highly appreciate it if you subscribe (turn on notifcation bell), like, and comment what else you want to see! 📅 Book a 1-On-1 Consulting Call WIth Me: calendly.com/worldzofai/ai-consulting-call-1 🔥 Become a Patron (Prviate Discord): patreon.com/WorldofAi 🧠 Follow me on Twitter: twitter.com/intheworldofai Love y'all and have an amazing day fellas.☕ To help and Support me, Buy a Coffee or Donate to Support the Channel: ko-fi.com/worldofai - Thank you so much guys! Love yall
[Must Watch]: PrivateGPT 2.0 - Private & Fully Local Chat with Docs, PDF, TXT, HTML, PPTX, DOCX, and More!: ruclips.net/video/NemHxc2FGVU/видео.html DeepSeek LLM: Most POWERFUL Base Model & Better Than Llama 2!: ruclips.net/video/yMWaHnMduzQ/видео.htmlsi=s_9eIfdMGfixTdLL Drag-A-UI: Easiest Way To UI's with AI! (Installation Tutorial): ruclips.net/video/mioS_XmvpM0/видео.htmlsi=6VcpYLjjBFIKFXmw
This feels like another streamlit / Gradio. Just spent got my head around those. Obviously datapipelines, etc are a bit more "enterprise" than streamlit depending on how you implement it, but this also seems like more of a lift. Does it have an code interpreter type capabilities which would be preferencial to dipping in and out of chatgpt for streamlit code when throwing ideas at a wall
We have some capabilities, such as a VSCode extension (Taipy Studio), that provides completions when writing components in Taipy. We are working on giving LLM-based completions like Copilot to write Taipy code.
maybe without all the session state pain you have to go through with streamlit when you have to work with dynamic data or multiple users. but yeh, that was my initial throught
(Taipy Engineer here) We actually used Streamlit in the past. Our gripe with it was how the backend event loop was managed. Streamlit re-runs your code at every user interaction to check what's changed (unless you cache specific variables, which is hard to do well). When your app has significant data or a significant model to work with or multiple pages or users, this approach fails and starts freezing constantly. We wanted a product that compromises between the easy learning curve of Streamlit while retaining production-ready capabilities: we use callbacks for user interactions to avoid unnecessary computations, and the front and backend are running on separate threads. We can run on Jupyter notebooks if that helps. We also focus on providing pre-built components to allow the end-user to play around with data pipelines quickly. These components enable the user to visualize the data pipeline in a DAG, input their data, run pipelines, and visualize results.
I actually used Streamlit in the past. The gripe with it was how the backend event loop was managed. Basically, Streamlit re-runs your code at every user interaction to check what's changed (unless you cache specific variables, which is hard to do well). When your app has significant data or a significant model to work with or multiple pages or users, this approach fails, and the app starts freezing constantly. Taipy is a product that does a compromise between the easy learning curve of Streamlit while retaining production-ready capabilities: it uses callbacks for user interactions to avoid unnecessary computations, front and back-end are running on separate threads. We also run on Jupyter notebooks if that helps. More than that, it also focuses on providing pre-built components to allow the end-user to play around with data pipelines quickly. These components allow users to visualize the data pipeline in a DAG, input their data, run pipelines, parallelize them, and visualize results with a whole management of scenarios and data
💓Thank you so much for watching guys! I would highly appreciate it if you subscribe (turn on notifcation bell), like, and comment what else you want to see!
📅 Book a 1-On-1 Consulting Call WIth Me: calendly.com/worldzofai/ai-consulting-call-1
🔥 Become a Patron (Prviate Discord): patreon.com/WorldofAi
🧠 Follow me on Twitter: twitter.com/intheworldofai
Love y'all and have an amazing day fellas.☕ To help and Support me, Buy a Coffee or Donate to Support the Channel: ko-fi.com/worldofai - Thank you so much guys! Love yall
[Must Watch]:
PrivateGPT 2.0 - Private & Fully Local Chat with Docs, PDF, TXT, HTML, PPTX, DOCX, and More!: ruclips.net/video/NemHxc2FGVU/видео.html
DeepSeek LLM: Most POWERFUL Base Model & Better Than Llama 2!: ruclips.net/video/yMWaHnMduzQ/видео.htmlsi=s_9eIfdMGfixTdLL
Drag-A-UI: Easiest Way To UI's with AI! (Installation Tutorial): ruclips.net/video/mioS_XmvpM0/видео.htmlsi=6VcpYLjjBFIKFXmw
Great video man! Keep it up
TaskWeaver: Create LLM-Based Autonomous AI Agents - AutoGen 2.0!? (Installation Tutorial): ruclips.net/video/JS7p3_c9s18/видео.html
Fantastic video, you earned a new subscriber! Please make more videos on Taipy
Introducing Google's NEW Gemini AI Model! Better Than GPT-4! - ruclips.net/video/65WNKnYGMyY/видео.html
Thank you for this awesome video and app, We really do like it!
This feels like another streamlit / Gradio. Just spent got my head around those. Obviously datapipelines, etc are a bit more "enterprise" than streamlit depending on how you implement it, but this also seems like more of a lift. Does it have an code interpreter type capabilities which would be preferencial to dipping in and out of chatgpt for streamlit code when throwing ideas at a wall
We have some capabilities, such as a VSCode extension (Taipy Studio), that provides completions when writing components in Taipy. We are working on giving LLM-based completions like Copilot to write Taipy code.
OpenCopilot: FREE Opensource AI Copilot Executing APIs (Installation Tutorial): ruclips.net/video/HiXh3pVp9Is/видео.html
this is the same as streamlit?
maybe without all the session state pain you have to go through with streamlit when you have to work with dynamic data or multiple users. but yeh, that was my initial throught
(Taipy Engineer here) We actually used Streamlit in the past. Our gripe with it was how the backend event loop was managed. Streamlit re-runs your code at every user interaction to check what's changed (unless you cache specific variables, which is hard to do well). When your app has significant data or a significant model to work with or multiple pages or users, this approach fails and starts freezing constantly.
We wanted a product that compromises between the easy learning curve of Streamlit while retaining production-ready capabilities: we use callbacks for user interactions to avoid unnecessary computations, and the front and backend are running on separate threads. We can run on Jupyter notebooks if that helps.
We also focus on providing pre-built components to allow the end-user to play around with data pipelines quickly. These components enable the user to visualize the data pipeline in a DAG, input their data, run pipelines, and visualize results.
I actually used Streamlit in the past. The gripe with it was how the backend event loop was managed. Basically, Streamlit re-runs your code at every user interaction to check what's changed (unless you cache specific variables, which is hard to do well). When your app has significant data or a significant model to work with or multiple pages or users, this approach fails, and the app starts freezing constantly. Taipy is a product that does a compromise between the easy learning curve of Streamlit while retaining production-ready capabilities: it uses callbacks for user interactions to avoid unnecessary computations, front and back-end are running on separate threads. We also run on Jupyter notebooks if that helps. More than that, it also focuses on providing pre-built components to allow the end-user to play around with data pipelines quickly. These components allow users to visualize the data pipeline in a DAG, input their data, run pipelines, parallelize them, and visualize results with a whole management of scenarios and data
holy S this is Cool