Marine Gosselin

@PythonTrends Thanks for the share! 🙌🙌

Building A #ChatGPT Wizard with MistralAI Using Taipy #python #pythonprogramming #pythondev #dormosheio #tutorial #ai dormoshe.io/trending-news/…

@Elijah1477793 @Taipy_io It's growing! 🚀

Any idea why Taipy @Taipy_io never got so far in web app dev.🌐🕷️ Or rather why it's not so popular and still niche thought it's better than KivyMD (my opinion)

I love #swag and #Taipy knows it 🤩 Thanks for this wonderful bag filled with goodies 🥳 #python #fullstack #gui #devrel cc @Taipy_io @themaringoss @Rymguerbi

📛 taipy 🧠 Taipy is an open-source Python library for building web applications, swiftly transforming data and AI algorithms into production-ready apps. 🛠️ @taipy_io 💻 Python ⭐ 1585 🍴 89 🔗 github.com/Avaiga/taipy

@Coding_MadeEasy Love to see this! 🙌

How To Create Web App With Taipy Python - Full Tutorial codemagnet.in/2024/03/19/how… #Python #SoftwareDevelopment

@prod42net Thank you for sharing 💚

🌟 Dive into the world of AI and ML with these top Python libraries! From Taipy for app development to Scikit learn for ML frameworks, these tools are essential for your data science journey. #Python #AI #ML #DataScience #Tech 🌟 ift.tt/BSwirpZ

@RastgooMojdeh @PyDataParis @glemaitre58 @probabl_ai @Taipy_io @Scaleway Happy to know you had fun :)

Once again another great meetup from @PyDataParis, thanks to the organizers. Also thanks to the speakers @glemaitre58 (@probabl_ai), Florian Jacta & Alexandre Sajus (@Taipy_io) and @Scaleway for hosting the meetup

Super proud of this achievement! 🚀

7.0K ⭐️ Taipy: turns Data & AI algorithms into production-ready web apps in no time. ⚡️@Taipy_io #Avaiga/taipy&Date" target="_blank" rel="nofollow noopener">star-history.com/#Avaiga/taipy&… #starhistory #GitHub #OpenSource #python

@animesh_xd @MLHacks Congratulations!

Won track prize best use of Taipy in electrothon 6.0 nit hamirpur #mlh #devfolio @MLHacks

@OAkhaleed @Taipy_io Yayy!

My boss stumbled up in a post on Taipy by @Taipy_io. I am officially starting my Taipy Journey today.

@andrea_moro @Taipy_io Yes, we do! We are multi-user by default, you can also broadcast variables from a client to others, and more!

@Taipy_io out of curiosity, do you support state management out of the box?

@prod42net Thank you for the feature 🙂

Check out Marine's tutorial on creating an AI photo app with Python and Taipy! Learn to build a model for image recognition using TensorFlow and Taipy for the GUI. Train your model and enjoy classifying images with ease. #Python #AI #ImageRecognition #Te… ift.tt/SXKJbBx

@AsfiShaheen @AsfiShaheen thank you for this great post! Those are great comparison points; I would only correct that Taipy is building in Javascript and Python. The performance is very true ; we manage callbacks and events very differently! Nice to see a new Taipy- aficionado :)

Just two years ago Snowflake paid $800 mm to buy Streamlit. Tapiy was launched shortly after (April 2022) and now has 6.5k stars on Github vs 31k for Streamlit. I tried Taipy today and felt it was a better Streamlit. Scary times to pay big bucks for software and dISTriBuShun

I was bored building APIs until this application hit my plate. 5 lessons I learned from building a Data-Driven application. The Request: Build an application to help customers visualize their data. It has to be fast and follow our design guidelines. 1. Enterprise Data platforms are expensive; look around. The obvious solution was to look for a platform to connect to the data and build some visualizations. I looked at some enterprise solutions, and GOD, they cost an arm and a leg. I needed a plan B, so I took a deep dive into the open-source pool. (This pays off 90% of the time) I found two main options: Taipy and Streamlit.  2. Data Integration doesn’t need to be painful. One of the first real headaches was trying to get all sorts of data to play nice together. There was no unified view. The data live across different formats and Systems. That was when I discovered Taipy’s data dashboards. Now, I can bring together data from different sources: - Databases - APIs - Real-time feeds Also, Data Dashboards serve to simplify and make complex data visually attractive. 3. Staring at numbers is not enough. Users want to ask "what if" and watch the data come alive with answers. I need to develop something interactive that can provide quick responses. Again, Taipy provided more out-of-the-box features for creating interactive GUIs without sacrificing simplicity. 4. Your users deserve nothing less than the best UI. Taipy's capability to build responsive and interactive graphical GUI was a game-changer. Streamlit renders all the graphical components again when a user interacts. But, when the user interacts with a visual element, Taipy triggers callbacks based on the action or change in the GUI. This was a decision maker; check how smooth the transition is on the image.  5. Users want data, but only if it is fast. No matter how fun your spinner animation is, nobody wants to spend time looking at it. As the platform grew, so did the concern about its scalability. Taipy's scalable architecture allowed me to manage the growing demands of the platform. The open-source community came through, and I can’t sing Taipy’s praises enough for making my job much easier. Give them a star here: github.com/Avaiga/taipy And start today by typing: $ pip install taipy Big thanks to Taipy for supporting this post.

Python is the best language in the world. Today, you can build AI and data applications using Python alone! No HTML or JavaScript necessary. Take a look at Taipy, an open-source Python library to build end-to-end production applications. Star their repository: github.com/Avaiga/taipy I recorded a quick video showing how to build a simple chat interface to talk to OpenAI GPT-3.5 using Taipy alone. Something important to keep in mind: Taipy's goal is not to replace web developers but to provide an alternative to those who need to build applications without web experience. If you are a data scientist or someone dealing with data, Taipy will simplify your life considerably. Thanks to the team behind Taipy for collaborating with me on this post. Here is a link to the full demo. Download this code and run it. It takes 30 seconds: github.com/Avaiga/demo-ll….

New #Startup github.com/Avaiga/taipy - Build powerful #data and #AI apps in pure #Python

Here's how you can convert your Python application into a website in 2 minutes. Read more ↓

I built my first AI project on a weekend. Here are the 7 steps I followed: I want to create a personal assistant. So I sat down and wrote what I needed: - Something that can write text like humans. It sounds like a job for an LLM. - A UI that allows the user to ask questions and the chatbot to respond. - A simple way to publish this application. You can’t train an LLM on a weekend, so borrow one; I used OpenAI, but you can use your own fine-tuned LLMs. Now, for the fun parts, the UI, the code, and the hosting, I used Taipy. Taipy is an open-source Python library designed for easy development of data-driven web applications. It covers both the front-end and back-end, allowing users to develop the whole back-end of an application, model dataflows, and pipelines. It was perfect for my weekend idea (and probably for many of your ideas, too). With Taipy You can build the whole back-end and the front-end without knowing much about HTML, CSS, and JS. You have access to Taipy Cloud; designed to simplify web application development and deployment. Everything starts here: ‘$ pip install taipy’ From then 7 simple steps: 1. Add your imports 2. Write the request and the send_message functions. The “request” function takes the user message as input and returns the response from the LLM. The “send_message ” function adds the user's message to the context, sends it to the API, and then displays the conversation. 3. Now, the only missing piece was the UI. Taipy has a way to define pages by using Markdown strings. It cannot be easier. I used a table to display the conversation and input so the user could type their message. When the user presses enter, the UI calls the send_message() function. 4. I added some styling, and … I have my own Personal Assistant. 5. From here, I just connected to Taipy Cloud, clicked on "Add Machine," filled in the fields, and added a new Application. All this took me less than 5 minutes. 6. There is only one configuration pending. Adding my environment variable to hold the OpenAI’s key. (Keep your keys out of source code) 7. Now for the final step: zip all files and upload, and click "Deploy app." Wait for the deployment to complete, and share the link with the people you want to impress. Taipy Platform Also: - It helps you manage data flows and optimizes task performance and pipelines. - It provides a Rest API. - Includes a cache system that enables it to skip repetitive tasks. - Supports Authentication/Authorization Why did I choose Taipy over Streamlit? - It is designed for both prototyping and production. - It can scale. - It provides much better performance. - It supports large data. - A lot of UI components. - It is truly multi-user and provides for different user profiles. Open-source AI is taking over the world, and Taipy is part of it. Give them a star: github.com/Avaiga/taipy Disclaimer: This post was sponsored by Taipy.

@yrise_ Love the energy! Yesss

@themaringoss Let's democratize Datascience! Great!

𝗣𝘆𝘁𝗵𝗼𝗻 🐍 𝗹𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗗𝗮𝘁𝗮𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗩/𝗥𝗲𝘀𝘂𝗺𝗲