Blog — Taipy

We're Hiring! - AI Developer – R&D Team

Rym Michaut — Fri, 23 May 2025 15:50:43 GMT

Position Overview: We are looking for a motivated and technically strong AI Developer to join our Customer Success team. In this role, you will help demonstrate the full potential of Taipy, our Python-based platform for building and deploying AI-powered applications. Your responsibilities will include designing and implementing AI use cases, creating proof-of-concept applications, and contributing to the AI integration capabilities of Taipy. You will act as a bridge between technical innovation and real-world business needs. Key Responsibilities: Design and implement end-to-end AI use cases using Taipy (e.g., LLM/RAG integration, Development of AI Agents, etc) Build proof-of-concept pilots to showcase the integration of AI models with Taipy. Support pre-sales activities through demos, technical workshops, and client interactions. Contribute to content development (tutorials, notebooks, blog posts) highlighting Taipy’s AI/ML capabilities. Collaborate with the product R&D team to improve AI-related features in Taipy Core and GUI. Candidate Profile: Technical Skills: Solid experience in Python, with good knowledge of machine learning frameworks, LLMs/RAGs/Agents and their APIs. Experience: Prior experience in building or deploying AI Models is preferred, but we are open to strong junior profiles. Education: Bachelor / Master’s degree in Data Science, Computer Science or a related field. Languages: Fluent in English (written and spoken). Travel: Willingness to travel occasionally (notably to the US and Asia). Remote Work: Remote work is possible. Send your application to contact@avaiga.com

We're hiring! Python Developer – Pre-Sales Team

Rym Michaut — Fri, 23 May 2025 15:31:06 GMT

Position Overview: We are looking for a passionate and skilled Python Developer to join our Customer Success team and contribute to the growth and promotion of Taipy, one of the leading platforms for building Data/AI applications in Python. The ideal candidate will be involved in creating prototypes, engaging with end-users, supporting the Taipy community, enhancing features, and showcasing the platform through client meetings and webinars. The position is available immediately. Key Responsibilities: Develop technical content for Taipy: demos, articles, pilot projects, etc. Test the platform and interact with clients in pre-sales contexts (meetings, demos, workshops). Support the growth of the Taipy developer and contributor community. Participate in the improvement of features in both Taipy Core and Taipy GUI. Candidate Profile: Technical Skills: Strong Python programming skills. Junior profiles are welcome if programming abilities are solid. Education: Bachelor / Master’s degree in Computer Science or equivalent. Languages: Fluent in English (written and spoken). Travel: Willingness to travel occasionally, particularly to the US and Asia. Remote Work: Remote work is possible. Send your application to contact@avaiga.com

Taipy vs. Power BI: Which Data Visualization Tool is Right for You?

Alexandre Sajus — Mon, 07 Apr 2025 13:49:58 GMT

In today’s data-driven world, businesses rely on powerful visualization tools to make informed decisions. Power BI and Taipy both serve this purpose by allowing users to create dynamic dashboards and reports. While Power BI is a well-known industry standard, Taipy offers unique advantages, particularly for those seeking greater customization, interactivity, and integration with Python-based models. This article explores the key differences between the two platforms and highlights why businesses might choose Taipy over Power BI. Power BI: Advantages & Limitations Advantages of Power BI Fully Online Platform: Power BI operates entirely in the cloud, offering a controlled and managed environment. Seamless Microsoft Integration: It connects effortlessly with Azure services, Excel, PowerPoint, Teams, and more. No Technical Experience Required: Users can create dashboards with a simple drag-and-drop interface, without needing to write code. Collaboration Features: Users can comment on visual elements, set alerts for data changes, and share reports in PDF or Excel formats. Template Applications: Power BI provides ready-made templates, making it easier for businesses to start with common analytics use cases. Limitations of Power BI Pricing Complexity: While Power BI advertises a low entry cost (~$20 per user per month), real projects often require additional Azure services (e.g., data storage, virtual machines), leading to unpredictable expenses. Governance Challenges: Since multiple users can create independent dashboards, it becomes difficult to track which version is the most accurate or relevant. Limited Data Interactivity: Filters applied on one dashboard do not automatically update other dashboards, leading to redundancy and inefficiency. Restricted Customization: Layouts and visual elements are static, requiring DAX (a proprietary language) for deeper customization. Limited Support for Large Datasets: Power BI struggles with handling large-scale data visualization efficiently. Taipy: A Flexible and Customizable Alternative Common Features Between Taipy and Power BI No-Code Drag-and-Drop Interface: Users can build dashboards easily without coding. What-If Analysis: Both tools allow users to simulate different scenarios for better decision-making. Advantages of Taipy 1. Seamless Python Integration: Unlike Power BI, Taipy is deeply embedded in the Python ecosystem, making it an excellent choice for AI/ML/DL/LLM models, data management, and specialized algorithms. 2. Advanced Table Functionalities: Taipy provides table filtering, pivot, multi-index/multi-column support, and more — capabilities that Power BI lacks. 3. Complete Customization: Users can implement many visual customizations cascading filters, create specific layouts, and customize dashboards extensively. 4. Rich Multi-User Environment: Taipy supports notifications, data sharing, and real-time collaboration features for multi-user’s sake. 5. Dynamic Visualizations: Taipy allows for symbolic/textual search, full data pipeline management, and dynamic graphics (e.g., moving objects) that Power BI does not support. 6. Scalability and Performance: Taipy is optimized for large datasets, ensuring smooth performance without unexpected cost spikes. 7. Simplified Pricing: Unlike Power BI, Taipy offers transparent pricing, eliminating the risk of skyrocketing costs due to Azure service consumption. 8. Easy Learning Curve: Even non-technical users can quickly grasp Taipy, making it a viable alternative to Power BI’s user-friendly interface. Why Businesses Should Consider Taipy If your business heavily relies on Python for data analysis and AI-driven decision-making, Taipy is a superior choice over Power BI. Its ability to integrate directly with Python libraries, offer greater customization, and efficiently handle large datasets makes it a compelling solution for data-intensive applications. Additionally, Taipy’s transparent pricing model prevents unexpected costs, a common concern with Power BI’s Azure-based consumption model. Conclusion While Power BI is a strong contender for businesses deeply embedded in the Microsoft ecosystem, Taipy provides the flexibility, interactivity, and scalability needed for modern data applications. For organizations looking to break free from the limitations of static dashboards and embrace a more dynamic, Python-powered analytics platform, Taipy is the clear winner. Join the Taipy community and learn more about the tool.

🚀 Introduction to Taipy: Your Shortcut to Production-Ready Data Science Applications

Amar Tiwari — Fri, 10 Jan 2025 11:32:00 GMT

The article "Introduction to Taipy: Your Shortcut to Production-Ready Data Science Applications" provides an overview of Taipy, a Python framework designed to simplify the creation of production-ready data science applications. It addresses common challenges data scientists face when transitioning from experimental analysis to deployment, such as building interactive dashboards and automating data workflows. The article highlights Taipy's capabilities in enabling users to develop interactive dashboards for data visualization and design dynamic workflows for automated data processing. This is particularly interesting for data scientists seeking efficient tools to streamline their workflow and enhance the scalability of their applications. Read it on Medium

Taipy and IFP - Energies Nouvelles: A Collaborative Effort Towards Sustainable Mobility

Rym Michaut — Thu, 02 Jan 2025 09:56:06 GMT

In the pursuit of sustainable mobility solutions, Taipy and IFP - Energies Nouvelles have joined forces to enhance the development and deployment of eco-friendly applications. This partnership underscores a shared commitment to environmental responsibility and technological innovation. What is Taipy Founded in 2021, Taipy is a leading provider of advanced open-source Python tools designed to streamline the creation of business applications. Taipy offers both front-end and back-end solutions, enabling developers to build production-ready web applications efficiently. With a focus on performance, customization, and scalability, Taipy empowers businesses to develop interactive, collaborative, and user-centric Data & AI applications. What is IFP - Energies Nouvelles IFP - Energies Nouvelles (IFPEN) is a prominent public research organization dedicated to the energy, transport, and environmental sectors. With a strong focus on innovation, IFPEN conducts research and development to support the energy transition and promote sustainable mobility. Their commitment to corporate social responsibility is evident through initiatives aimed at reducing environmental impact and fostering eco-friendly technologies. The Geco Air Application One of the notable outcomes of the collaboration between Taipy and IFP - Energies Nouvelles is the enhancement of the Geco Air application. Geco Air is a free mobile app available on iOS and Android platforms, designed to help users monitor and reduce the environmental impact of their daily mobility. By analyzing individual trips, the app estimates pollutant emissions and provides personalized advice to promote responsible driving behaviors and the adoption of sustainable transport modes. How Geco Air Works Geco Air operates by automatically detecting users' movements across various modes of transport, including walking, cycling, driving, and public transit. For drivers, the app takes into account specific vehicle characteristics and driving styles to calculate pollutant emissions with precision. By leveraging advanced algorithms developed by IFP - Energies Nouvelles researchers, Geco Air offers real-time feedback and customized recommendations to help users adopt more eco-friendly mobility habits. Technical Description of Geco Air Geco Air utilizes sophisticated algorithms to detect and analyze user mobility patterns automatically. By inputting the vehicle's license plate number, the app retrieves specific technical characteristics, such as fuel type, engine capacity, and emission control technologies, to accurately model pollutant emissions. The app employs the smartphone's inertial sensors and native APIs to identify physical activities like walking, running, and cycling. For vehicular movements, it uses cellular network changes and GPS data to detect trip initiation and cessation, ensuring minimal battery consumption by activating GPS only when necessary. At the end of each trip, data such as speed and altitude are transmitted to IFPEN servers, where mathematical models estimate engine parameters and pollutant emissions. This approach allows Geco air to provide users with precise, personalized feedback on their mobility's environmental impact. The Role of Taipy Designer in Geco Air's Development Taipy Designer played a pivotal role in the development of Geco Air by providing IFP Energies Nouvelles engineers with a robust platform to create interactive dashboards and data visualization tools. This facilitated the rapid testing and demonstration of research algorithms, streamlining the validation and implementation processes. The intuitive interface of Taipy Designer enabled engineers to efficiently set up "what-if" analyses, enhancing the app's capability to deliver personalized and actionable insights to users. Conclusion The partnership between Taipy and IFP - Energies Nouvelles exemplifies how technological collaboration can drive the development of innovative solutions for sustainable mobility. By integrating Taipy Designer into their workflow, IFP - Energies Nouvelles has enhanced the functionality and user experience of the Geco Air application, empowering individuals to make informed decisions that contribute to environmental preservation. This joint effort not only showcases the strengths of both organizations but also paves the way for the development of future applications aimed at promoting eco-responsible behaviors in daily transportation.

🚀 Taipy: The 10X Python Framework That’s Making Streamlit Obsolete in 2024

Arman Hossen — Fri, 20 Dec 2024 11:42:00 GMT

Ever wondered how tech giants build those sleek, data-driven web applications? Now you can create them too, with just Python knowledge and Taipy’s magic. Welcome to the future of web development, where complexity meets simplicity. 🌟 Why Taipy is Breaking the Internet Forget everything you know about web development. Taipy is changing the game by letting you build sophisticated web applications without touching HTML, CSS, or JavaScript. It’s like having a full-stack development team in your pocket! 🏃‍♂️ From Zero to Hero in Minutes Read it on Medium

Mastering Python Dashboards: Tutorial to Build a Stock Analyzer with Taipy in 36 lines of code

Rym Michaut — Thu, 19 Dec 2024 11:40:00 GMT

In the world of data science, creating dashboards often requires juggling multiple languages and frameworks. But what if you could build sleek, interactive dashboards entirely in Python? Enter Taipy, a Python library that redefines dashboard development. In this article, we’ll build a Stock Analysis Dashboard that processes data dynamically and visualizes it beautifully. Let’s dive in! Read it on Medium

Avaiga & IFP-Energies Nouvelles Partner to Empower Businesses and Scientists with Cutting-Edge Python Solutions

Vincent Gosselin — Mon, 16 Dec 2024 14:16:19 GMT

December 2024 – Avaiga and IFP Energies Nouvelles (IFP-EN) are excited to announce a collaboration that is transforming how businesses and scientists develop applications. This partnership combines Avaiga’s expertise in low-code/no-code Python platforms with IFP-EN’s leadership in scientific innovation for transportation, energy, and environmental solutions. The partnership has already delivered impactful results, including the launch of Taipy Designer, the first no-code GUI builder built on Python. Fully integrated with the Taipy Enterprise Suite, Taipy Designer enables businesses and researchers to build interactive, scalable applications faster than ever before. Solving Key Challenges for Businesses & Scientists Business analysts often face hurdles when leveraging Python’s powerful ecosystem, and scientists need to bring complex models to life in practical, user-friendly ways. Taipy Designer bridges these gaps by offering: For Business Analysts & BI Teams: A simple, drag-and-drop interface to access Python’s capabilities without coding expertise, accelerating application delivery and improving productivity. For Scientists & Researchers: Tools to create interactive applications that visualize complex models, enabling faster experimentation and innovation cycles. Customer-First Collaboration Driving Results Adopting new tools often depends on their ability to drive tangible benefits. Taipy Designer has already demonstrated its transformative potential by empowering businesses and researchers to overcome technical barriers, enhance productivity, and innovate seamlessly. Here's what leaders have to say about its impact: About Avaiga Avaiga is committed to empowering businesses with modern, Python-based low-code platforms that go beyond traditional BI tools. Taipy’s integration capabilities with leading data platforms like Databricks, Snowflake, WatsonX, and SageMaker allow organizations to develop enterprise-grade applications quickly and efficiently. About IFP-EN IFP Energies Nouvelles (IFP-EN) is a European leader in applied research, specializing in clean transportation, energy transition, and sustainability. With a team of experts and researchers working across multiple disciplines, IFP-EN is shaping the future of energy and environmental innovation. Create your dashboard or application fully in Python. Contact us

How to Create a Stunning Web UI for CrewAI with Taipy

Yeyu Huang — Mon, 09 Dec 2024 15:51:00 GMT

In our last tutorial, we learned how to create a web app with the latest version, 0.80, of the CrewAI framework. In that development, we used the Panel as the web UI library to create an interactive multi-agent workflow app. We implemented redirecting agents’ output and user input messages from the command line to the front end. Panel is a powerful library for building web apps and is highly compatible with CrewAI. However, it is not the only option. This tutorial will show you how to create a web UI with Taipy, another popular open-source library for building data apps. Read it on Medium

Egg Production Forecasting

Vincent Gosselin — Thu, 28 Nov 2024 14:20:33 GMT

Objective The purpose of production forecasting is to predict egg output accurately over time by analyzing multiple influencing factors. This helps farm managers make informed decisions about operations, resource allocation, and market strategies. Key Components 1. Factors Influencing Egg Production: Seasonality: Egg production can fluctuate with seasonal changes in daylight and temperature. Lighting programs are often used to mitigate seasonal effects. Age of Laying Hens: Younger hens (pullets) produce fewer eggs until maturity, while older hens may experience declining production. Environmental Conditions: Temperature, humidity, and ventilation significantly affect hen productivity and comfort. Feed Quality and Availability: Balanced nutrition ensures maximum production. Shortages or poor-quality feed can reduce output. Stress Factors: High stocking density, noise, or disease outbreaks can lead to decreased production rates. 2. Forecasting Models: Machine Learning Models: Regression models (e.g., Random Forest, Gradient Boosting), or neural networks can be used to incorporate multivariate inputs like feed quality, age, and weather conditions. Simulation Models: Scenario-based forecasting to estimate production under different environmental or operational conditions. 3. Inputs for Forecasting: Historical production data (e.g., daily egg counts). Data on factors such as hen age, feed quality, and environmental metrics. External data, like weather forecasts and market trends. 4. Implementation Workflow: Data Collection: Gather data from sensors, records, and external sources. Preprocessing: Clean and standardize data for model input. Model Training and Testing: Train forecasting models using historical data and evaluate accuracy. Prediction and Monitoring: Generate and compare predictions with actual production. Modules Short-Term Forecasting: Predicting daily or weekly production to align inventory with demand. Long-Term Forecasting: Planning for seasonal shifts or adjusting for the aging of hen populations. Risk Mitigation: Detecting early signs of abnormal production patterns caused by disease, stress, or environmental issues. Outcomes 1. Operational Efficiency: Improved scheduling for feed delivery, labor, and egg collection. 2. Market Responsiveness: Aligning production with market demand to minimize surpluses or shortages. 3. Cost Savings: Reducing waste and unnecessary resource expenditure by aligning operations with accurate forecasts. 4. Improved Animal Welfare: Detecting early stress indicators in hens (e.g., drops in production) and taking corrective actions. Challenges 1. Data availability and accuracy: Ensuring consistent data collection for effective modeling. 2. Environmental variability: Adapting forecasts to unforeseen events like extreme weather. 3. Integration with other farm systems: Connecting forecasting models with inventory and supply chain systems.

Sophisticated Speech-to-Text Application

David Akim — Mon, 25 Nov 2024 15:54:00 GMT

Discover this Speech-to-Text application in Taipy using AssemblyAI's Universal-2 Speech-to-Text model. The application's features are: Transcribe spoken words into written text. Detect multiple speakers in an audio file and what each speaker said. Summarize your audio data with key takeaways Download transcriptions to a text file. Read it on dev.to

Building Fraud Detection Applications with Taipy

Vincent Gosselin — Fri, 15 Nov 2024 12:56:02 GMT

Data-driven Fraud Detection consists of two different approaches: Supervised Learning: Train Machine-Learning models on historical data to detect fraudulent behavior with precision. Unsupervised Learning: Identify anomalies, outliers, and unseen fraud patterns using advanced unsupervised techniques. Different AI models for different fraud detection Supervised Learning learns from existing patterns (frauds observed in the past). It is based on machine learning models that are capable of detecting fraud with precision. Unsupervised Learning is needed to detect fraud that has not yet been detected in the past (previously unseen). Customers use Taipy to build quickly and efficiently highly interactive Fraud detection software covering Supervised and Unsupervised learning. Building a successful Fraud Detection Application It may come as a surprise that the difficulty lies not so much in developing these AI models as it is in building a whole application around them. Intuitive & Powerful User Interfaces One of the most important factors is the ability to provide Powerful Data Visuals and Interactions. Customized interactive graphical interfaces are crucial to present the data and the results to the end-users. Transparency Second, it is essential to supply clear explanations as to why certain transactions are earmarked as Fraud by the ML engine. Taipy applications offer a “white-box” environment that provides insights into the logic behind flagged transactions. Provide Users with Control Finally, end-users need control: they must be able to influence. They must be able to influence how the solutions get generated and presented to her/him, for instance: Having the capability to prioritize frauds based on different factors: Monetary, Frequency, etc. Being able to ‘tune’ the AI models to minimize false positives, for example. Catching 99% of all the actual fraud cases implies that many of the suspected fraud will be false positives, creating a huge backlog for the investigating team of fraud experts. Perform What-if analysis using Taipy Scenario Management. Unsupervised Learning: Finding outliers When it comes to Unsupervised learning, specific custom graphics need to be provided to highlight outliers and potential frauds. This can be standard graphs (showing a sudden change in customer behavior), charts comparing a customer with its peers, or geographical maps highlighting unusual patterns. Visualizing social networks can also be an extremely useful tool for detecting rings of fraudsters, etc. Why Taipy? The Taipy low-code Python platform is the ideal tool to quickly develop and deploy the next generation of fraud detection applications. Here is a non-exhaustive list of key functionalities provided by Taipy: Empowering End-Users with Advanced Decision Support SystemsWith Taipy, you’re equipped to quickly build highly interactive Fraud detection applications: High level of customization: Every customer has different requirements in terms of fraud detection. Taipy provides a level of customization that most OLAP/BI tools can’t provide. Low-Code: Develop and deploy Fraud applications quickly and efficiently. Scenario Management: allowing for what-if analysis. This is important in the context of fraud detection since end-users need to ‘test’ the AI models using different options. Collaboration: Often, in the context of fraud, end-users need to interact with each other and exchange specific information about a given alleged fraud case. This is totally covered by Taipy which is designed as a collaborative tool. Great Integration with the rich AI eco-system: Taipy is pure Python, sharing the same ecosystem as all the standard AI models & libraries. This ensures a complete integration between the Taipy application and the underlying AI layer. Fraud Alert and Case Management Concerning the processing and triggering of alerts, Fraud Detection applications need to propose different modes of operations: Flag suspicious transactions in real-time, 24/7. Priority Management Batch-process complex cases for thorough analysis. Track alerts, route cases for investigation, and monitor resolution through tailored dashboards or existing management systems. Taipy can integrate with existing use case management tools (if the customer already has one). Alternatively, a Taipy dashboard can be built to track the status of suspicious cases: progress/resolution/… Conclusion Many companies have failed to implement game-changing software using AI technology simply because they were using the wrong tools. Taipy avoids the limitations of traditional approaches while providing new functionalities essential for Fraud Detection applications: Easy customization Fast development time thanks to its low-code API A collaborative framework ideal for team members' interaction Scalable Graphics Scenario Analysis Support for Large Data Support for Structured & Unstructured Data Best practices in terms of software engineering (versioning,...) ensure a smooth software evolution over time (new data sources, software updates, etc.).

Develop, then deploy a WEBP to PNG image converter Taipy App to the web — Part 2

Thomas Reid — Mon, 11 Nov 2024 14:16:00 GMT

This is the second of two articles showing how to build and deploy a Taipy utility app that converts images from WEBP to PNG format. This part shows the step-by-step process to deploy your app to the web for FREE using the PythonAnywhere service so that anyone can see and use it. To learn how I developed the app, check out Part 1 using the link below. Read more on Medium

Develop, then deploy a WEBP to PNG image converter Taipy App to the web — Part 1

Thomas Reid — Sun, 03 Nov 2024 14:19:00 GMT

This is the first part of a two-part series where I’ll show you how to build and then deploy to the web a useful utility that converts images in WEBP format to PNG format. This part will deal with developing our app using Taipy. Part 2 will show you how to deploy the app using the PythonAnywhere service so anyone can see and use your app. All for FREE! Read it on Medium

Taipy 4.0: Redefining Data and AI Application Development

Rym Michaut — Wed, 16 Oct 2024 10:17:00 GMT

Introduction With the release of Taipy 4.0, we are taking a major leap forward in empowering developers and data scientists to build, manage, and deploy AI-powered applications with even greater efficiency. This new version introduces enhancements across scenario management, data handling, UI controls, and performance improvements, making Taipy more intuitive and powerful than ever before. Upgrading to Taipy 4.0 Taipy 4.0 brings changes to the package structure that require users to manually uninstall previous versions before upgrading. If you are currently using Taipy 3.x, follow these steps to ensure a smooth transition: This ensures that outdated dependencies are removed, preventing potential runtime issues. Key Enhancements in Taipy 4.0 Enhanced Scenario and Data Management Taipy 4.0 introduces major usability improvements to scenario and data node handling: Scenario Selector: Multiple selection support. New filtering, sorting, and search functionalities for efficient navigation. Data Node Selector: Multiple selection support. Enhanced filtering, sorting, and search options. Data Node Viewer: Users can now upload and download data from file-based data nodes. Job Selector: Added a detailed panel for better job monitoring. Powerful New UI Controls in Taipy GUI Taipy 4.0 introduces three new powerful UI controls: Metric Control: Display key numerical information, ideal for industrial KPIs. Progress Control: A compact way to visualize process completion. Chat Control: Simplifies the creation of chat-based applications. Enhanced Table Control The table component now includes: Built-in edit, add, and delete functionalities without requiring extra callbacks. New format_fn[column_name] property for custom Python-based formatting. Boolean values now support checkboxes for faster rendering of large datasets. Additionally, Taipy now supports custom page styling and allows list-of-values (LoVs) to be generated from enumeration classes. Core Enhancements in Taipy Core taipy.get_scenarios() and taipy.get_primary_scenarios() now support: Sorting by name, ID, creation date, or tag. Filtering scenarios by time range. Improved job tracking: New timestamp attributes (submitted_at, run_at, finished_at). New duration metrics (execution_duration, pending_duration, blocked_duration). New Event Consumer API: Introducing CoreEventConsumerBase, allowing developers to track and react to CRUD operations on Taipy entities. Template Enhancements New applications generated from templates now support Git repository initialization for seamless version control from the start. Conclusion Taipy 4.0 is a significant step forward in democratizing data-driven application development. Whether you are building complex AI workflows, interactive dashboards, or scenario-based applications, Taipy 4.0 offers a richer, more efficient, and seamless development experience. Ready to explore Taipy 4.0? Upgrade today and start building smarter AI applications! 📌 Join the conversation:GitHub | Documentation

HacktoberFest 2024: How to Contribute and Win Swag with Taipy?

Rym Michaut — Fri, 04 Oct 2024 09:01:50 GMT

Taipy is participating in HacktoberFest 2024! Join us! Code, Solve issues, & Win exclusive prizes🎁🎁🎁 while making an impact! Hacktoberfest 2024 What is the HacktoberFest? 🤖 HacktoberFest is an annual month-long celebration of open-source software dev. It’s the perfect opportunity for devs of all skill levels to contribute to meaningful projects, collaborate, and WIN COOL SWAGS. Why the HacktoberFest Matters? 🚀 HacktoberFest is more than just coding. This is a time when experienced & novice contributors unite to enhance each other’s work, learn, and progress together. Taipy in HacktoberFest 1. Where do I go? Everything is happening on Taipy GitHub Repository! ➡️ Go to the “issues” tab each labeled with "hacktoberfest" 🔍 Select one, solve it, wait for its merge, & win your prize! 2. Which issue to choose? The issues are categorized by experience level, find yours! Easy - beginner-friendly skills tasks. These are tagged 100 points 💎 and you can find them **HERE.** Intermediate - mid-level skills tasks. These are tagged 200 points 💎💎 and you can find them **HERE.** Advanced - highly advanced skills tasks. These are tagged 300 points 💎💎💎 and you can find them **HERE.** 3. What is the reward? As you solve issues and get your Pull Requests (PR) merged, you earn points that unlock exclusive swag! Additionally, we are running a unique giveaway! 🎁🎁🎁 ➕ Give Taipy a star on our GitHub repo, and be among 10 stargazers to win a swag package. 4. How long will it last? Starts: October 1, 2024 Ends: October 31, 2024 Detailed Process to Contribute Here’s how you can participate: Step 1: Choose an Issue Visit our GitHub repository: Taipy GitHub Repository Look for issues tagged with "hacktoberfest" and choose the issue that best matches your skills and interests. Step 2: Fork the Repository Once you’ve selected an issue, start by forking the Taipy repository to your GitHub account. This creates a copy of the repository that you can work on without affecting the original codebase. Step 3: Clone Your Fork Locally After forking, clone the repository to your local machine by running the following command in your terminal: git clone Step 4: Create a New Branch Always create a new branch for your work. It’s good practice to name your branch based on the issue you're working on. For example: git checkout -b bug/#-comment Step 5: Work on the Issue Start coding and solving the issue! Follow the guidelines mentioned in the issue, and make sure to test your changes locally. If you have any questions, feel free to ask for help in the issue’s comment section or reach out to our community. Step 6: Commit & Push Your Changes Once you’re satisfied with your work, commit your changes and push the branch to your fork: git add . git commit -m "description of the fix" git push origin your-branch-name Step 7: Submit a Pull Request (PR) Go back to the original Taipy repository. You’ll see an option to submit a pull request. Make sure your PR description clearly references the issue you worked on and provides a brief explanation of what you fixed. Join our Discord server if you have any questions or issues Important Guidelines Choosing Issues Wisely Stick to issues labeled "hacktoberfest" and labeled with the number of points as well to ensure they count towards the event. New issues can be created, but 🚨🚨🚨 they must first be reviewed and approved by the Taipy team. If a newly created issue is not approved, it will be tagged as spam 🚨🚨🚨, it will not be labeled “hacktoberfest”, and any PR referencing it will be rejected. Many contributors can be assigned to one issue. A contributor can contribute to several issues. Points cannot be cumulated. Tagging & Validation For any new issue created by contributors, it must be tagged with "hacktoberfest" by the Taipy team and assigned one of the three difficulty levels (Simple, Medium, or Difficult). So ask the team before solving, otherwise it won't be counted as a Hacktoberfest PR. Do not submit a PR on any issue until the Taipy team has validated it. Individual or Team Participation You can participate individually or as part of a team. However, if you choose to work with a team, please note that only one team member can submit a Pull Request (PR) using their GitHub account. Additionally, only one swag package will be shared among the entire team. Following these guidelines will ensure that your contributions are valuable and recognized! Get Started! Head over to our GitHub repository, choose an issue, and start contributing today! Don’t forget to ⭐star our project⭐ for a chance to win even more rewards! Together, let’s make HacktoberFest 2024 a success for open source and Taipy!

Taipy is Now a Validated Databricks Technology Partner!

Vincent Gosselin — Fri, 13 Sep 2024 11:20:52 GMT

Great News! Taipy has just been validated as a Databricks Technology Partner. This recognition reflects the significant value that Taipy brings to Databricks customers. Why This Matters Taipy provides all the features needed to transform Databricks scripts (Python, notebooks) into fully interactive applications for LOB end-users (business users). The Challenge with Databricks While Databricks enables data scientists to develop complex data logic and AI algorithms using Python, it can be a struggle to transform these scripts into a full application for end-users. Common Requirements & Challenges: Developing a Robust Front-End GUI Customization options Great response times Ability to visualize large datasets Multi-User Collaboration Powerful What-If Analysis Enable your LOB end-users to "play" with the algorithms by launching various executions using different parameters. This “What-If” Analysis(aka Taipy’s Scenario Management) is essential to gain user acceptance. Many Databricks customers face challenges during this phase, leading to promising pilots not achieving the status of fully deployed applications validated by happy end-users. How Taipy Helps Overcome These Challenges Enter Taipy—a Python-based platform specifically designed to remove all the friction to bring Databrick code into full scale B2B applications. . As a truly low-code solution, Taipy ensures a swift learning curve, making it the perfect companion for your Databricks environment. Key Benefits of Using Taipy: Seamless Integration with Databricks Fast Development of Front-End & Back-End Empower End-Users with Scenario Management Reduce Time to Market for AI & Data Projects Success Stories By combining Databricks with Taipy, our customers have transformed the way they scale their Databricks scripts into full-fledged applications, dramatically reducing friction and accelerating their success. Here’s a quote from Stephane Leray, Manager at Group Les Mousquetaires (GLM) Datalab: “Implementing the CFM project with Taipy was a game-changer. We got our front-end and back-end up and running incredibly fast. It’s kind of amazing. And when you pair Taipy with Databricks, it is like two pieces of a puzzle that just fit perfectly together. Now, we are already using Taipy as our standard platform for our new AI projects.” Read the full success story Trusted by Industry Leaders Taipy has also been validated by large companies like: McDonald’s Textile Apparel Limited TSMC ... And more Learn More Integration Guide: The description of the integration with Databricks is available at Integrating with Databricks. Overview Video: Watch a short that provides a brief introduction. Ready to Elevate Your Databricks Projects? Don't let the challenges of application development hold back your Databricks projects. Leverage Taipy to create interactive, scalable applications that delight your end-users. Get in Touch with Our Team

How to Construct a Multipage Data Science Web App in Python with Taipy

Alan Jones — Thu, 05 Sep 2024 13:23:00 GMT

Taipy is a framework for building Data Science and AI web apps in Python. As such, it is a competitor to the likes of Streamlit or Dash, but it is distinct from both of those products. Taipy separates the user interface from the rest of the program logic and uses callbacks to add functionality to user controls. In this sense, it is closer to Dash than Streamlit where the user interface controls are often incorporated into the main Python code. Both Dash and Taipy are based on the Flask microframework, so it shouldn’t be a surprise that there are similarities but where in a Dash app, it can feel like you construct your user interface in HTML (but written with Python functions), Taipy has an additional layer of abstraction that lets the user define user controls that are closer to Streamlit than Dash. So, is Taipy the best of both worlds? I wrote an introduction in A Data Dashboard in Pure Python with Taipy that explores how to construct a web app in Taipy, so you can judge for yourselves how easy it is to use. Read it on Medium

Transforming Excel Data into an Interactive Dashboard Using Python: A Personal Journey

Kevin Meneses González — Tue, 13 Aug 2024 13:29:00 GMT

As a data analyst, I’ve always been passionate about turning raw data into actionable insights. However, my journey into data visualization wasn’t always smooth. I remember a time when my team needed a dynamic dashboard to monitor sales across multiple regions. We had all the data in Excel, but creating an interactive dashboard seemed daunting. That was until I discovered Python and its powerful libraries that could turn this challenge into a breeze. Let me take you through my journey and how you can do the same. Read it on Medium

Taipy Designer, the Python alternative to Power BI, Qlik & Tableau

Anmol Baranwal — Fri, 02 Aug 2024 09:28:14 GMT

Data scientists, if you've been frustrated by the clunky process of exporting data to external tools for visualization, Taipy Designer is the solution you've been waiting for. This powerful Python-based tool lets you build dynamic, interactive dashboards directly on top of your Python code, seamlessly integrating AI capabilities. Why Taipy Designer? It's a drag-and-drop GUI builder, offering simplicity and efficiency for Python developers. With customizable styles and instant data visuals, it’s a game-changer in the Python data science ecosystem. Explore the future of data visualization with Taipy Designer—streamline your workflow and boost productivity! Read it on Dev.to

Optimizing the Supply Chain with Taipy

Vincent Gosselin — Mon, 29 Jul 2024 13:08:38 GMT

The 2 applications presented here can be accessed through Taipy’s website at: - 2-echelon supply chain - 3-echelon supply chain Before we delve into practical examples, let's introduce some key concepts behind supply chain network optimization. The concept of Echelon Supply Chain models differ widely in their complexity. One such level is the concept of Echelon or the number of layers required to model the actual supply chain. If we take the example of a country-wide pharmaceutical wholesaler, it may only be interested in optimizing the location of its own distribution centers. In such cases, only two levels or echelons need to be represented: - The Distribution Centers - The Customer locations In the case of a manufacturer, an additional echelon might be needed: Plants. Thus, a 3-echelon supply chain would include: - Plants - Distribution Centers - Customer Locations Similarly, a nationwide wholesaler might model a 3-echelon supply chain if they have multiple distribution levels: - Regional Distribution Centers - Local Distribution Centers - Customer Locations While it's possible to have even more echelons, in most real-life situations, 2-echelon or 3-echelon supply chains cover the majority of use cases. Modeling various objectives When optimizing supply chains, it is crucial to model one or several objectives. Transportation costs are a primary objective. In a 2-echelon supply chain, the transportation costs relate to: - The distance traveled between warehouses & customer locations, - The mode of transport contract: FTL, LTL, etc. - Specifics of transportation contracts, etc. Advanced models also account for warehouse fixed and variable costs. Additionally, costs often include the carbon footprint generated. Supply Chain Network Constraints For certain use cases, it is paramount to model important constraints such as: In certain scenarios, it is essential to model key constraints, such as: - Maximum number of warehouses - Capacity at different echelons like warehouse or plant capacity - Plant capabilities: some plants can only produce certain products while others can produce a full range - Pre-defined locations - Maximum distance/time to delivery: ensuring a maximum distance/time between the serving warehouse and each customer - Etc. What is Supply Chain Optimization? With core concepts defined, supply chain software typically employs an optimization model to generate “optimal” solutions for various scenarios. Applications can be categorized into three different maturity stages: Stage 1: - Provides visuals to display input data - Allows end-users to make manual decisions, such as selecting warehouse locations Stage 2: - Similar to Stage 1 but adds the capability for users to create and compare different scenarios/solutions Stage 3: - Fully functional Decision Support System - Includes a smart optimization engine that generates optimal solutions - Allows end-users to modify input parameters, automatically generate optimal solutions, and compare and select the best option This later stage incorporates all the functions of a Decision Support System. Examples of level-3 Supply Chain Solutions Optimal Solution to a 2-echelon supply chain Optimal Solution to a 3-echelon supply chain Comparing Scenarios Being able to easily compare scenarios is an integral part of Taipy. Here’s an example of a 2-scenario comparison: When more than two scenarios need to be evaluated, here’s an example of a multi-scenario comparison: Conclusion This article demonstrates how to build powerful Decision-Support Systems using Taipy, all in Python. The combination of powerful graphics and scenario management makes Taipy a unique tool in its category.

[Taipy Tech Talk #4]: Manage Configurations in Your Python App Like a Pro

Rym Michaut — Fri, 05 Jul 2024 10:28:00 GMT

In this fourth episode of the Taipy Tech Talk series, we tackle a common yet often underestimated challenge in app development: configuration management. Whether you're managing scenarios, pipelines, or user interfaces, keeping your application’s logic clean and maintainable is essential. That’s why this session focuses on how to use Taipy’s configuration system to build scalable, modular, and easy-to-update applications — with Python, and Python only. 👥 Meet the Speakers This episode features three of our in-house experts who not only know Taipy inside out — they helped build it: Alexandre Sajus – Customer Success Engineer at Taipy Alexandre helps clients succeed with Taipy by providing expert guidance and ensuring smooth, scalable deployments of their apps. Florian Jacta – Community Success Engineer at Taipy Florian supports and grows the Taipy community, helping developers unlock the full potential of the framework while shaping its evolution through feedback. Jean Robin Medori – Chief Product Officer at Taipy Jean Robin co-heads the R&D team and plays a central role in designing the features and philosophy behind Taipy’s architecture. 🧠 What You’ll Learn This is not just another Python tutorial — it’s a deep dive into best practices for organizing complex applications with configuration files. You'll discover how to: Use Taipy’s configuration-based approach to define scenarios, tasks, data nodes, and pipelines Separate logic from structure, making your codebase cleaner and easier to maintain Dynamically manage changes across your app without touching the core code Scale your application effortlessly with reusable config blocks Gain more control over versioning and team collaboration by decoupling parameters from logic This session is a must-watch for teams who want to future-proof their code and build enterprise-ready applications. ▶️ Watch the Replay Ready to level up your app architecture? 👉 Watch the full episode here In less than an hour, you’ll gain valuable insights from the creators of Taipy on how to keep your application modular, readable, and ready to evolve — no matter how complex it gets. ⚙️ Why Configuration Matters As your project grows, hardcoded logic becomes a bottleneck. Taipy’s configuration system is designed to keep your applications lean and agile. Whether you're building a dashboard, an optimization workflow, or a multi-user app, mastering configurations is key to clean code and rapid iteration. This Tech Talk gives you everything you need to get started — and sets you up for long-term success. Don’t forget to subscribe to our YouTube channel and stay tuned for more episodes of Taipy Tech Talks, where we showcase hands-on tutorials, expert use cases, and practical advice to help you build better data apps, faster.

[Taipy Tech Talk #4]: Manage Configurations in Your Python App Like a Pro

Rym Michaut — Fri, 05 Jul 2024 10:25:00 GMT

Taipy Among the Top Web Development Companies in Europe in 2024

Rym Michaut — Mon, 17 Jun 2024 14:32:24 GMT

A Holistic Approach to AI Application Development At Taipy, our mission is clear: to revolutionize how companies create and implement data and AI solutions. Research from Gartner highlights a critical disconnect in the industry, with 85% of AI projects failing to deliver their intended business outcomes. This failure often arises from overly ambitious projects that lack a clear integration with business needs or a well-defined problem. Organizations frequently operate in silos, with data scientists, IT professionals, DevOps teams, and end-users using disparate technology stacks. This fragmentation hinders seamless collaboration and integration. Taipy addresses these challenges by providing a coherent strategy that bridges gaps across various stages of application development. Our platform integrates scenario management and what-if analysis into a user-friendly interface, fostering better engagement between end-users and AI technologies. This direct interaction with AI algorithms through a graphical interface enhances the usability and practical utility of developed applications, increasing project success rates. Tracing the Origins of Taipy’s Effectiveness The effectiveness of Taipy is the result of decades of industry experience. Our team, with 20 to 30 years in the field, has distilled best practices from working with major corporations in AI and data. Our extensive expertise ensures a robust strategy that overcomes the scalability challenges often found in Python-based pilot projects. By developing our own libraries across various use cases, Taipy is optimized for handling large datasets with greater stability than other libraries. “Our core philosophy blends simplicity with functionality, offering a platform that is both accessible and powerful enough to meet enterprise needs. This equilibrium ensures that while Taipy remains user-friendly, it does not compromise performance, customization, or scalability. It embodies the optimal balance of ease of use and robust performance in production environments.” Vincent Gosselin, co-founder and CEO of Taipy. Balancing Speed and Sophistication to Enhance Productivity In today’s fast-paced tech environment, the importance of rapid development cannot be overstated. Specialists no longer have the luxury of spending extended periods on tasks such as developing graphical interfaces or pipelines. Taipy addresses this by providing multiple options for creating graphical interfaces, including a low-code approach using an extended Markdown and a pure Python API. This facilitates quick development while allowing for the creation of sophisticated, interactive, and multi-page graphical interfaces capable of handling large data. At the Cutting Edge of Technological Innovations We are excited to announce our upcoming no-code builder, ‘Taipy Designer.’ This pioneering Python software allows for drag-and-drop interface creation directly onto clients’ Python programs, eliminating the need for coding. The commitment to ease of use extends to the backend as well, where our graphical editor facilitates swift pipeline construction and algorithm execution within the Python development environment. Users can visualize trade scenarios, monitor executions, and compare results seamlessly, all with just a few lines of code. Taipy Designer will be a comprehensive solution for front-end and back-end development, prioritizing rapid development without sacrificing functionality. Real-World Applications One of our standout applications is with McDonald’s, which uses Taipy’s product for sales forecasting across thousands of stores. Our powerful tool predicts store-level sales metrics for upcoming weeks down to a 30-minute granularity. Leveraging factors like weather, historical data, and traffic conditions, it provides accurate forecasts and facilitates crucial what-if analyses. Leading retailers in Western Europe rely on Taipy’s software for comprehensive cash flow prediction at the company level. This decision support system, crucial for CFOs, forecasts cash flow over the next three months, managing vast sums of money and ensuring financial stability through informed decision-making. Taipy’s applications extend to NFC utilization, electricity procurement, and solar energy impact analysis for retailers and industries. Our versatile platform benefits companies across various sectors, demonstrating its effectiveness in addressing multifaceted challenges. We are honored by this recognition and remain dedicated to pushing the boundaries of what’s possible in AI and data application development. Thank you to CIOReview Europe for this esteemed award, and congratulations to the entire Taipy team for their hard work and innovation. To learn more about our solutions and how we can help your business succeed, book a call!

Ultimate Guide to TaiPy GUI

Ishmam Fardin — Mon, 17 Jun 2024 13:11:43 GMT

Discover the power of Taipy, an open-source Python library designed for seamless, end-to-end application development. With features such as what-if analyses, smart pipeline execution, built-in scheduling, and deployment tools, Taipy stands out as a versatile solution for developers. This article shares an experience from a data science hackathon at the Georgia Institute of Technology, where the author's team explored Taipy to create an interactive sports analytics application. Through challenges and triumphs, they navigated Taipy’s capabilities and complexities, offering valuable insights into its configuration, multi-page functionality, state management, and more. Whether you're new to Taipy or looking to deepen your understanding, this guide provides practical tips and examples to help you harness its full potential. Read it on Medium

How to Host Your Taipy App on PythonAnywhere

Eric Narro — Mon, 17 Jun 2024 13:06:44 GMT

Deploying Python applications can often be a complex task, especially when compared to the relative ease of deploying NodeJS or PHP applications. However, PythonAnywhere has emerged as a popular platform that simplifies this process, making it particularly accessible for hosting Python web-based applications. While Python frameworks such as Django or Flask are commonly used, deploying Streamlit applications on PythonAnywhere poses a challenge as they are not supported by the platform. This article explores how Taipy, a library designed to create deployable Python applications, can be effectively hosted on PythonAnywhere. By leveraging Flask, which is well-supported on PythonAnywhere, we can deploy Taipy applications with ease. This guide will walk you through the process of converting a Taipy app to run as a Flask app, setting up a PythonAnywhere environment, and successfully running your application, ensuring you can showcase and share your Python projects effortlessly. Read it on Medium!

Taipy: A Python Framework for Data & AI App Development

LINK LAYER WIRELESS FOUNDATION — Mon, 17 Jun 2024 12:43:00 GMT

In the fast-paced world of data and artificial intelligence, developers are constantly seeking tools that can help them bring their ideas to life quickly and efficiently. Enter Taipy, a pure Python open-source framework that promises to streamline the process of building data and AI applications. Taipy's mission is simple: to allow developers to concentrate on their core data and AI algorithms without getting bogged down in the complexities of building user interfaces and web applications. By abstracting away these tedious tasks, Taipy frees up developers to focus on what they do best – innovating with data and AI. Read it on LinkedIn!

Building a Chatbot with Taipy in 10 simple steps

Rym Michaut — Mon, 10 Jun 2024 19:07:28 GMT

Creating a chatbot using Taipy involves integrating OpenAI's GPT-3.5 API to generate intelligent responses. Here's a structured guide to help you build your own chatbot. Step 1: Install Requirements Create a `requirements.txt` file with the following content to set up the necessary software packages that the chatbot will depend on. This file lists the specific versions of Taipy and OpenAI packages required. Install the requirements using: Step 2: Imports Create a `main.py` file with the necessary imports: taipy.gui is used for creating the graphical user interface, and openai is for interacting with the OpenAI API. Step 3: Initialize Variables Initialize the conversation context and variables in `main.py`: context maintains the conversation history, conversation stores the dialog, and current_user_message holds the user's input. Step 4: Generate Responses Initialize the OpenAI client and create a function to get responses. This function sends user messages to the OpenAI API and receives the AI's response. client initializes the OpenAI API with your API key. The request function sends a message to the API and returns the AI's response. Step 5: Handle User Messages Create a function to process user messages and update the conversation. This function updates the conversation context with the user's message and the AI's response. It appends the user's message to the context, calls the request function to get the AI's response, and updates the conversation history. Step 6: Design the User Interface Define the interface using a Markdown string to define the layout and appearance of the chatbot interface. This defines a simple interface with a table to display the conversation and an input box for the user's messages. Step 7: Run the Application Run the Taipy application. This code starts the Taipy GUI with the defined page layout, enabling dark mode and setting the window title. Step 8: Add Styling Customize the chat interface by creating a `main.css` file: Apply these styles dynamically: Update the table: Step 9: Additional Features Enhance the chatbot with additional features like notifications, a clear conversation button, and conversation history. Notifications: Use the notify function to inform users of important events. Clear Conversation: Add a button to clear the conversation history. Conversation History: Store and display previous conversations for context. The full implementation is in the GitHub repository. Step 10: Secure API Key Store your API key securely using an environment variable: Deploy your application securely, ensuring the API key is not exposed in the code. By following these steps, you can create a functional and stylish chatbot using Taipy and OpenAI's GPT-3.5. For a detailed guide, visit the official tutorial.

Taipy in the Semicon Industry

Vincent Gosselin — Thu, 06 Jun 2024 08:47:49 GMT

In this article, we present some examples of Taipy Applications developed for the semiconductor industry. These applications range from rich Data Visualization to more complex Decision-Support Systems. Most of these applications are B2B applications and can be divided into 3 different categories: 1. Company-Level Applications These applications are definitely level 3 since sophisticated AI/Mathematical models work hand-in-hand with What-If Analysis. Supply-Chain Optimization In the semiconductor industry, it is essential to regularly establish a plan for the upcoming months. Such a plan needs to take into account the various production sites (Fabs, Assembly, Test), transportation, inventory, raw materials, semi-finished, and finished products. These models can be fairly large, and precise modeling of the capacity and yield is essential. These new projects are replacing older-generation supply chain software that is too slow and heavy to be sustainable. These applications excel at finding the best supply chain setup to minimize various objectives: costs (transportation, storage, etc.), carbon footprint, fixed costs, customer service levels, etc. Many scenarios are typically executed, analyzed, and compared. Such a Decision Support System is essential and very often leads to millions of dollars in savings. Capacity Planner (aka "CP") CP combines decisions on the acquisition of new critical/expensive equipment (i.e., Photolitho tools) with the impact on the overall production plan. The purchase of new critical equipment, its arrival at the plant, its set-up, its tests & qualification of the equipment need to be synced up with the production plan. CP generates optimal equipment acquisition plans for the mid to long-term, taking into account various objectives (costs, throughput, cycle time, etc). CP can be used for a single production site or across multiple sites. What-if Analyses are extremely important to validate various demand / financial / capacity scenarios. Such a model fits into a larger Equipment Life Cycle Management Application where the phasing-out and qualification process are managed across a large set of tools / equipment. It is important to note that these applications make extensive use of Taipy’s Scenario Management. In effect, many different scenarios need to be built, evaluated, and compared. These scenarios are easily modelled in Taipy using different assumptions in terms of Plant capacity, Demand Forecast, Objectives (Cost-based, Profit-Based, …). CP's objectives are to optimize the timing of acquisition and preparation for these expensive tools so that the plants achieve the company’s production objectives. This precise planning has demonstrated a 10% to 15% reduction in preparation time and a smoother integration with the Fab production environment. 2. Plant-Level Applications OnLine Scheduler (aka “OLS”): Production Scheduling This is a factory-level application particularly suited for highly automated plants. It optimizes the throughput or cycle time for critical steps such as Photolithography or Diffusion. OLS uses advanced AI models to decide upon the sequence and timing of the lots on the different equipment. Taipy provides the overall graphical interface, scenario management as well as the core optimization engine. This corresponds to an AI-enabled Decision Support System (Level 3 in the above diagram). OLS optimizes cycle time and throughput across one or several sectors in the plant. Cycle time is typically reduced by 10% on average, with even higher results obtained for urgent lots. Throughput is also increased for bottleneck equipment, ranging between 5% and 10%. Plant Level Analytics Fabs need modern analytics that combines lots of equipment/production data with smart prediction models: Monitoring / Predicting Equipment failure, Predictive Maintenance, Anomaly detection: AI-based pattern recognition is used to identify and classify defects, helping engineers understand their root causes and take corrective actions. Yield Prediction across a production route, etc. Taipy provides a great platform to visualize, monitor and predict different production indicators. These applications range from Level 1 to Level 3. Taipy support for large time series is essential for these types of analytical applications.

[Taipy Tech Talk #3]: Build a GPT-4 Chat Web App in Python (in Minutes!)

Rym Michaut — Tue, 04 Jun 2024 10:21:00 GMT

In the third episode of our Taipy Tech Talk series, we dive into the fascinating world of AI-powered applications. This time, our team walks you through how to build a fully functional GPT-4 chatbot app — entirely in Python, using the Taipy framework. Whether you're an AI enthusiast, a Python developer, or someone curious about integrating large language models (LLMs) into real-world apps, this live-coding session is packed with practical insights. 👥 Meet the Speakers Hosted by two of our in-house experts: Alexandre Sajus – Customer Success Engineer at Taipy Alexandre helps clients succeed with Taipy by providing expert guidance and ensuring smooth, scalable deployments of their apps. Florian Jacta – Community Success Engineer at Taipy Florian supports and grows the Taipy community, helping developers unlock the full potential of the framework while shaping its evolution through feedback. 🧠 What You’ll Learn In under an hour, you’ll see how to build a real GPT-4 chat application with a Python-native interface, using only the tools you already know. Here’s what’s covered: How to connect Taipy to the OpenAI GPT-4 API How to design a simple and responsive chat interface with Python How to manage user inputs, outputs, and app logic using Taipy’s callback system How to make your app interactive and production-ready — no front-end coding required! From backend integration to frontend behavior, this webinar gives you everything you need to create a powerful, sleek chatbot. 🎥 Watch the Replay Ready to build your own AI chatbot in Python? 👉 Watch the full episode here You’ll walk away with a working prototype and a clear understanding of how to build on it for your own use case — whether it’s for customer support, internal tooling, or just to explore what LLMs can do. 💡 Why It Matters Generative AI is transforming the way we build and interact with applications. With Taipy, you can bring those innovations to life faster — without worrying about front-end frameworks, deployment headaches, or tech stack complexity. This session is perfect for those looking to integrate GPT models into real apps while staying entirely within the Python ecosystem.

[Taipy Tech Talk #2]: Create a Multipage, Multi-Data Source, Multi-User Dashboard

Rym Michaut — Thu, 23 May 2024 10:18:00 GMT

We’re back with the second episode of our Taipy Tech Talk series — and this one is packed with insights for teams looking to build powerful dashboards that scale! In this session, we demonstrate how to create an advanced data application that handles multiple pages, several data sources, and multi-user scenarios — all built entirely in Python using Taipy. Whether you’re working in analytics, operations research, or optimization, this episode shows just how far you can go with Taipy’s flexibility and power. 🎙️ Meet the Speakers For this episode, we’re joined by a strong trio of experts: Alexandre Sajus – Customer Success Engineer at Taipy Alexandre helps Taipy users build and scale real-world applications, bringing deep technical expertise and practical implementation advice. Florian Jacta – Community Success Engineer at Taipy Florian works closely with our growing community of developers, providing support and ensuring their feedback shapes the future of Taipy. Irv Lustig – Optimization Principal at Princeton Consultants A long-time leader in the field of mathematical optimization, Irv shares how Taipy is used by real-world clients — including in complex decision-making environments. 📚 What You’ll Learn This Tech Talk dives into building a complete app that mimics the needs of real enterprise users. Here’s what you’ll take away: How to create a multi-page navigation flow with different views (dashboards, reports, forms…) How to connect to and manage multiple data sources inside your app How to implement multi-user logic, where different users see different elements or interact with the app simultaneously How Taipy handles advanced user flows and data visualization, without the need for JavaScript or external front-end tools You’ll also hear from Irv about how his team at Princeton Consultants uses Taipy to build smart applications that support optimization workflows for clients in logistics, finance, and more. ▶️ Watch the Replay Missed the live event? We’ve got you covered — the full recording is available on YouTube: 👉 Watch the episode here This is a must-watch if you’re interested in building enterprise-grade dashboards that are dynamic, responsive, and Python-native. 🎯 Want to Build Your Own App? This webinar gives you everything you need to start building with Taipy — plus a clear vision of what’s possible. Whether you're a developer, data scientist, or decision optimization expert, this session is packed with practical ideas you can apply today. More Tech Talks are on the way — each one designed to teach, inspire, and empower the Python community to build better apps, faster.

Build Interactive Data Apps of Scikit-learn Models Using Taipy

Avi Chawla — Thu, 23 May 2024 08:31:28 GMT

Data scientists and machine learning engineers mainly use Jupyter to explore data and build models. However, building an interactive app is better for those who don’t care about our code and are interested in results. While we have previously discussed about Taipy, an open-source full-stack data application builder using Python alone (I also like to call it a supercharged version of Streamlit), we are yet to do a practical demo of building a data application. So today, let me walk you through building a simple data pipeline: It will train various classification models implemented in sklearn. It will plot their decision region plots to let the user visually assess their performance. By the end of this newsletter, we would have built the following app: Let’s begin! Building blocks of Taipy apps A typical data pipeline has a series of steps, such as: Load data → select columns → clean data → fit model → predict → find training score → find test score. But if you look closely, is it necessary to go step-by-step? In other words, an optimal execution graph can look something like this: Unless explicitly implemented, traditional data application builders (and even Python scripts) can not take advantage of such parallelization. As a result, they resort to a step-by-step execution, which is not optimal. However, data apps built with Taipy can leverage such optimizations. There are four core components of Taipy that facilitate this: Data node → A placeholder for data like text, numbers, class objects, CSV files, and more. Task → A function to perform a specific task in the pipeline — cleaning data, for instance. Thus, it accepts data node(s) as its input and outputs another data node, which can be an input to another task. Pipeline → A sequence of tasks to be executed (with parallelism and caching). Scenario → A Scenario configuration defines a given pipeline. An instance/execution of such configuration is called a Scenario. Typically, a new Scenario is needed (created and executed) when we execute the pipeline with modified/different inputs. Next, let’s understand how we can utilize them in Taipy to build efficient data apps. Prerequisites To get started, install Taipy: Next, install Taipy Studio, a VS Code extension. It provides a graphical interface to build and visualize data pipelines. These are the steps: VS Code →Extensions → Search Taipy Studio → Install. Sklearn Model app using Taipy You can download the code for this project here: Taipy-Sklearn demo. It would be better if you download the above code, open it in VS code, and read this newsletter side-by-side. We will be creating four Python files in this project: main.py: This will be the project’s base file. config.py: This will define the connections between tasks, which data nodes they accept, and what data nodes they output. algos.py: This will implement the tasks utilized in our data pipeline. So here’s where we will train the models and create their decision region plots. interface.py: This will implement the interface for the user to interact with our data app. We discussed creating interfaces in Taipy in an earlier issue, so we won’t go through it again. Note that if you are not comfortable with Taipy's GUI Markdown syntax, Taipy also provides a pure Python interface. First, let’s look at the algos.py file as that one is the easiest to begin with. It has two functions: fit: Based on the parameter value model_name, it creates a model object. plot: This method accepts the model trained above and returns a decision region plot. Next, consider the config.py file. It imports the two functions defined in the algos.py file above and the Config class of Taipy library. Let’s look at the configure() method in detail. Recall that the fit() method defined above has three parameters → X, y, and model_name. Thus, we define those as data nodes in the first three lines: The output of fit() method is a model object, which is also a data node. Thus, we define that too in line 9. Moving on, we define a task in the pipeline to fit a model. 1st argument: Name of the task → “fit” 2nd argument: Function this task is supposed to execute → fit. 3rd argument: The input data nodes accepted by the task (We defined them in earlier lines of the configure method) 4th argument: The data node outputted by the task. Similarly, we define a task to plot the decision regions as well: 1st argument: Name of the task → “plot” 2nd argument: Function this task is supposed to execute → plot. 3rd argument: The input data nodes accepted by the task (We defined them in earlier lines of the configure method) 4th argument: The data node outputted by the task, a figure object. This defines our pipeline. Finally, we bind the tasks together in a scenario, export the configuration to a TOML file (for visualization), and return the scenario: We will see shortly that once we run the pipeline, it will create a config.toml file, which can be visualized using Taipy Studio (the extension we installed earlier): The diagram makes it quite easy to visualize how different tasks and data nodes contribute to the overall pipeline. Next, let’s look at the main.py file, which is the base file of this project. We import the following here: The taipy library. The configure() method we defined in the config.py file. The GUI markdown object (interface) defined in interface.py. The Core and GUI components of Taipy. Core is used to run pipelines. GUI is used to define a web interface. The method to create moons dataset from sklearn. Under the main block (if __name__ == "__main__"), we first instantiate an object of the Core class imported above, and a standard scenario configuration defined in the configure method: Next, for every model: We create a scenario (line 25). Specify values for the data nodes (lines 27-29) that are needed to start the scenario, and submit it (line 31). Finally, under the same main block, we create our GUI by passing the Markdown object created in interface.py. Done! Executing this as follows launches the data app I showed you above: Wasn’t that simple? To recap, here’s what we did: We implemented all the tasks in algos.py. In config.py, we defined the overflow workflow of our data application and how different tasks and data nodes interacted with one another. In interface.py, we defined our graphical interface. In main.py, we launched all independent scenarios by specifying data node values and executing the application. The whole application was entirely Pythonic and did not take more than 160-180 lines of code to implement. You can find the code for this project here: Taipy-Sklearn demo. A departing note Taipy is genuinely one of the best application builders I have ever used. It’s hard for me to switch to any alternatives now. The latency difference is quite noticeable in practical apps when I use Taipy as compared to other apps, as depicted below: Taipy has witnessed exponential growth over the last couple of months or so, which shows that more and more programmers are adopting it and finding it useful. It has also trended on GitHub multiple times. They are releasing new updates almost every week now, so do give them a star on GitHub to support their work: Taipy GitHub. I love Taipy’s mission of supporting data scientists and machine learning engineers in building full-stack apps themselves while Taipy takes care of all backend optimizations. They are solving a big problem with existing tools, and I’m eager to see how they continue! Get started with: 🙌 A big thanks to the Taipy team, who very kindly partnered with me on this newsletter issue. 👉 Over to you: What problems will you use Taipy for? Thanks for reading!

Accelerating Agile Optimization Solution Development with Taipy

Irv Lustig — Thu, 16 May 2024 13:18:00 GMT

Our partner, Princeton Consultant, had a project to optimize a complex scheduling process for a client of theirs. The scheduling involved tasks of varying durations and required coordination among teams with different roles, posing a significant challenge for limited personnel. Princeton developed an optimization model application to generate efficient schedules for ongoing and future tasks. Read the article on their website!

Revolutionize Your Web Development with Drag-and-Drop Python Integration

Vincent Gosselin — Mon, 22 Apr 2024 12:36:10 GMT

Empowering Developers and Non-Coders Alike to Build Interactive Web Applications Effortlessly For those just starting out with Python programming, the vast number of available libraries can seem staggering. It appears that there's a Python framework for just about any need you might have. However, one area seems to be notably overlooked: no-code studios for developing web front-ends. While several low-code tools exist for creating web interfaces—like Streamlit for pilots, Taipy for actual projects, and Gradio for quick interfaces for ML models—none fully automates this process. To bridge this gap, we are launching Taipy Designer this week. Taipy Designer interface Seamless Integration: From Python Code to Web Interface with Taipy Designer Taipy Designer offers a studio environment in which users can design entire web pages by dragging and dropping various graphical widgets onto a canvas. Suppose you've developed a Python program containing various elements, such as a Pandas dataframe named ‘df’, a boolean ‘bSampling’, and a date ‘date’, among other things. When you open Taipy Designer with your script, it automatically makes all these Python variables accessible. For instance, a ‘switch’ widget can be connected directly to your boolean variable bSampling, a ‘date selector’ can be connected to date, etc. You can also link any Python container, such as a dictionary, a Numpy array or a Pandas dataframe, to graphical components like tables, charts, or maps. Broad Accessibility with Taipy Designer Providing a full range of widgets for charts would make Taipy Designer too cumbersome. Instead, it offers basic widgets for line, bar, and pie charts while also providing easy access to all charts from libraries like Matplotlib, Plotly, and E-charts through "Generic" widgets. Taipy Designer can be used to implement multi-page, highly interactive web applications. Taipy Designer targets not only any Python developer but also non-professional developers such as scientists from other fields (Physics, Chemical/Electrical/Mechanical /Industrial Engineering, Biomedical, Airspace, Industrial Engineering, Environment, etc). Taipy Designer also benefits from the robust Taipy environment with powerful backend capabilities, pipelines, and scenario management. Check Taipy Designer's documentation.

Building A ChatGPT Wizard with MistralAI Using Taipy

Vincent Gosselin — Fri, 19 Apr 2024 14:53:05 GMT

TL; DR Let's learn how to build a simple chatbot using the Taipy GUI library and the Mistral-7B-Instruct-v0.1-GGUF language model from the transformers library. The walkthrough loading the language model generating responses to user prompts updating & clearing conversation history application styling By the end of this article, we will have a basic understanding of how to build a chatbot using these tools. 1. Loading the Mistral-7B-Instruct-v0.1-GGUF model Mistral 7B is a super-smart language model with 7 billion parameters! It beats the best 13B model, Llama 2, in all tests and even outperforms the powerful 34B model, Llama 1, in reasoning, math, and code generation. How? Mistral 7B uses smart tricks like grouped-query attention (GQA) for quick thinking and sliding window attention (SWA) to handle all sorts of text lengths without slowing down. Source: Mistral.AI Docs And there's more! Mistral AI Team fine-tuned Mistral 7B for specific tasks with Mistral 7B – Instruct. It outshines Llama 2 13B in chat and rocks both human and automated tests. The best part? Mistral 7B – was released under the Apache 2.0 license. Download GGUF files using ctransformers Install ctransformers With no GPU acceleration or install with ctransformers with CUDA GPU acceleration or install with ctransformers with AMD ROCm GPU acceleration (Linux only) or install with ctransformers with Metal GPU acceleration for macOS systems only Load the model All set? Let's run the code below to download and send a prompt to the model. Make sure to free up space on your computer and connect to a good internet connection. The model will continue the statement as follows, 2. Installing Taipy Taipy is an open-source Python library that makes it simple to create data-driven web applications. It oversees the visible part (Frontend) and the behind-the-scenes (Backend) operations. Its goal is to speed up the process of developing applications, from the early design stages to having a fully functional product ready for use. ⭐⭐⭐ Star us on GitHub ⭐⭐⭐ Source: Taipy Docs Requirement: Python 3.8 or later on Linux, Windows, and Mac. Open up a terminal and run the following command to install Taipy with all its dependencies. We're set! Let's say hello to Taipy! Save the code as a Python file: e.g., hi_taipy.py. Run the code and wait for the client link http://127.0.0.1:5000 to display and pop up in your browser. You can change the port if you want to run multiple servers at the same time with Gui(...).run(port=xxxx). 3. Create a chat interface with Taipy Now we are familiar with Taipy, let's get our hands dirty and build our chat interface. Step 1: Import the AutoModelForCausalLM class from the ctransformers library This will generate text using pre-trained language models. Step 2: Import the Taipy GUI library Used to build the chatbot user interface Step 3: Load the Mistral-7B-Instruct-v0.1-GGUF model Step 4: Initialize the prompt and response variables as empty strings. Step 5: Define the chat function Called when the user clicks the "Chat" button in the user interface. This function takes the current state of the GUI as input, generates text using the pre-trained language model based on the user's prompt, and updates the response variable in the state. Step 6: Define the user interface with Taipy GUI Time to define the user interface for our chatbot using the Taipy GUI library. The user interface consists of an input field where the user can enter a prompt, a "Chat" button that triggers the chat function, and a display area where the chatbot's response is shown. Step 7: Run! Now, let's run the Taipy GUI application using the run method. Full Code Results Here it is, a simple chat interface! Let's level up our application to become a chatbot, as we imagine. 4. Set the Mistral AI Chatbot Step 1: In this step, we initialize the prompt and response and the conversation Step 2: Update the chat function Step 3: Add clear_conversation function to clear the conversation history. The function sets the state.conversation object to a new dictionary with a single key-value pair, where the key is Conversation, and the value is an empty list. This effectively clears the conversation history, as the state.conversation object is now an empty dictionary with a single key-value pair containing an empty list. The updated state.conversation object will be reflected in the chatbot UI, showing an empty conversation history. Step 4: Let's define the layout of the user interface for the Chatbot. Let's add a logo by downloading and saving it in the same directory as the script. Then attach clear_conversation to the New chat button. 5. Styling the chatbot Now, let's style our chat UI by floating the response to the left and the prompt to the right-hand side with some CSS. Step 1: Create a CSS file with the same title as the python file and save it in the same directory. Step 2: Create the style_conv function: The style_conv function is a callback function used to apply styles to the conversation history table in the Taipy GUI. It takes three arguments: state, idx, and row. The state argument is a dictionary containing the GUI's current state, including the conversation history. The idx argument is the index of the current row in the table, and the row argument is the index of the current column in the table. The function checks the value of the idx argument to determine which style to apply to the current row. If idx is None, the function returns None, indicating no style should be applied. If idx is an even number, the function returns the string user_mssg, corresponding to the CSS class for the user's prompts. If idx is an odd number, the function returns the string mistral_mssg, corresponding to the CSS class for the chatbot's responses. Here is the code for the style_conv function: To use the style_conv function in the Taipy GUI, we need to pass it as the value of the style attribute in the table element. For example: Step 3: Add a sidebar: Redefine the page to add the sidebar. Conclusion To conclude with, this article demonstrated how to build a simple chatbot using the Taipy GUI library and the Mistral-7B-Instruct-v0.1-GGUF language model from the ctransformers library. The code provided shows how to load the language model, generate responses to user prompts, update the conversation history, and clear the conversation history. The chatbot's UI, built using the Taipy GUI library, provides a user-friendly interface for interacting with the chatbot. Overall, this article provides a useful starting point for building more sophisticated chatbots using these Taipy. Resources: - HuggingFace: Mistral-7B-Instruct-v0.1-GGUF - Taipy: Taipy Docs

Taipy: The best Python GUI tool. Could it be a Streamlit killer?

Thomas Reid — Fri, 12 Apr 2024 11:39:00 GMT

This article is about a relatively new web framework that came into being in the last couple of years. It is called Taipy and, according to its website, it is … an open-source Python library for building production-ready front-end & back-end in no time. No knowledge of web development is required! I would say that the target audience for Taipy is data scientists and data engineers who perhaps don’t have a lot of experience developing front-end applications but are usually fluent in Python. Taipy makes it fairly easy to develop front-ends using Python so that’s a win-win all around. Read the whole article on Medium

Taipy 3.1: Elevating AI & Data Workflows

Vincent Gosselin — Sun, 24 Mar 2024 08:44:00 GMT

We are thrilled to announce the release of Taipy 3.1, a significant update that brings many new features and enhancements to our comprehensive platform. This latest version underscores our commitment to providing robust, scalable, and intuitive functionalities that cater to the community and the enterprise needs. With Taipy 3.1, users can expect many improvements designed to enhance data visualization, integrate tightly with Python visualization libraries, and enable more efficient computing capabilities. Empowering the Community Edition with New Features Third-Party Component Integration This enhancement allows users to seamlessly integrate a broad range of Python libraries into their Taipy application without waiting for specific integrations. By enabling the visualization of any html of Python objects into Taipy’s generic part object, users can now effortlessly incorporate tools with many Python visualization libraries like Folium, Bokeh, Altair, Matplotlib, etc. To go in depth with this feature, the part block is used to group visual elements into a single element. This allows to show or hide them in one action and be placed as a unique element in a cell. More technical info here Third-party component integration Native Plotly Integration Further enriching the data visualization capabilities of Taipy, version 3.1 integrates Plotly Python natively. Until Taipy V3, integrating additional charts from Plotly has some complexity. Now, any Plotly chart can be embedded within a Taipy application in a single line of code while retaining the performance and interactivity of Taipy. If we dig more in details, the control has a new property called figure that expects an instance of plotly.graph_objects.Figure. This class is provided by the Plotly Open Source Graphig Libary for Pyhon, so you can create all sorts of graphs in Python. More technical info here ‍ Native Plotly Charts integration Elevating Enterprise Solutions with Advanced Features Distributed Computing Recognizing the growing needs of enterprises for scalable data processing solutions, Taipy 3.1 introduces Distributed Computing capabilities. This feature enables the distribution of computational tasks across multiple machines, significantly improving the performance of large-scale data projects. Companies can now leverage distributed computing to perform complex computation tasks more efficiently, reducing turnaround times and enhancing productivity. If we go deeper in the technical aspect, a new job execution mode named cluster mode is available. It enables to run the jobs on a cluster of dedicated machines in a remote, distributed and scalable environment. More technical info here ‍ Distributed Computing Telemetry To assist enterprises in maintaining the health and performance of their applications, Taipy 3.1 incorporates Telemetry features. This addition provides administrators and developers with valuable insights into application performance metrics and health indicators. By facilitating the monitoring and analysis of these metrics, Taipy aims to help enterprises proactively identify and address potential issues, ensuring smoother operations and optimal performance. More technical info here Telemetry ‍ And beside all this, Taipy and all its dependencies now support Python 3.12. ‍ Conclusion Taipy 3.1 represents a major step forward in our mission to provide data scientists with a powerful, flexible and easy-to-use framework. Whether you are part of the open-source community or seeking enterprise-level solutions, this release let you enhance your data projects. We are excited to see how you will leverage these new capabilities to build great pilots as well as fantastic production-ready applications. For those interested in exploring these new features further, we encourage you to join our growing community of developers and data scientists and meet some of the on Discord. Join us on Discord Together, we can continue to innovate and make Taipy the standard framework for your Python application development needs. Star on GitHub!

Taipy or How to Remove Major Hurdles with Your AI/Data Projects

Vincent Gosselin — Thu, 21 Mar 2024 14:34:00 GMT

Over the years, I have been involved in implementing many “smart software” projects that demonstrated high benefits to major organizations. At the heart of these different software projects were algorithms based on Mathematical Programming, Simulation, and Heuristics, as well as AI models based on ML and generative AI. Most of these projects led to substantial ROI for these organizations; some have even shaped their company’s future. Despite all the hype around AI and Data, many organizations (outside of the software industry) struggle to implement a successful AI strategy. Most CIOs/CDOs involved have mostly produced “standard” data projects (data lakes/warehouses/data management/Dashboarding), some implemented several AI pilots, and very few have generated deployed projects showing substantial ROI for their company. One could consider the distribution of companies in terms of AI penetration as a highly left-skewed fat-tail distribution. The purpose of this article is not to list all the obstacles preventing the wider penetration of AI projects inside companies. For this purpose, I would recommend these two enlightening articles: Why businesses fail at Machine Learning How AI can help leaders make better decisions under pressure Instead, we focus on two gaping holes in the current software implementation approach. Gaping hole 1: A very siloed Environment Visualizing the various groups involved in a typical AI project is interesting. Of course, there are valid reasons for having these different roles, let alone the need for specialization. However, it is worth noting that: On a real project, the gap between the data scientists and end-users is substantial. Each silo uses different technology stacks. It is not uncommon for data scientists to develop mainly in Python, while IT developers use JavaScript, Java, Scala, etc. There has never been a wider variety of programming skills between and within each siloes. Gaping hole 2: Getting acceptance from the end-users / business-users As highlighted in a previous article, end-users seem to have disappeared from the AI landscape. It is all about data, technologies, algorithms, testing, deployment, etc. As if all AI projects will necessarily replace completely human experts. I am convinced that the future of AI in the industry lies in the hybrid collaboration between business users and AI software. However, end-users are an integral part of AI software development. Not getting them fully involved during the development process puts you at risk of not having your software used when the system goes live. Our strategy is to ensure that these two steps get implemented: A smooth end-user Interaction with the algorithm(s) And an easy tracking of business-user satisfaction How to fill Gap 1? Some obvious directions are: To standardize as much as possible on a single programming language. Provide an easy-to-learn/use programming experience to cater to all programming levels. Python is the ideal candidate for this. It is at the heart of the AI stack and ideal for integrating with other environments. Many Python libraries are available and provide an easy learning curve (including low code); unfortunately, they often suffer from performance issues and lack of customization. Let’s consider, for instance, the development of graphical Interfaces: One has the choice of using full-code libraries like Plotly Dash (or even development in Java Script) or easy-to-develop libraries like Streamlit or Gradio. However, these libraries do not scale performance-wise and will set you into a strict framework forbidding most customization. A Python developer shouldn’t have to arbitrage so much between programming productivity and performance/customization. We spent a lot of time on the design/implementation of our product, Taipy, to go one step further by guaranteeing ease of development while providing a huge leap in performance and customization. Here are two examples of performance issues (amongst many others) solved with Taipy: How to fill Gap 2? Addressing the two salient points mentioned above is crucial: A smooth end-user Interaction with the back-end algorithm(s) And an easy tracking of the business-user satisfaction Addressing Point 1: the end-user needs to interact with the algorithm/back-end. For this purpose, it is essential to: Provide variables/parameters that the end-user can control through the GUI. Allow the end-user to execute backend algorithms using these different parameter values, leading to different results. Provide the possibility to compare these different runs and track KPI performance over time. In Taipy, we have introduced the ‘scenario’ concept that addresses all of the above requirements. A scenario consists of the execution of the algorithm/pipeline where Taipy stores all the data elements (data sources, data outputs) Taipy’s scenario registry enables the end-user to: keep track of all of its runs, revisit a past scenario, understand its results, scan its input data, etc. ‍ Addressing Point 2: easy tracking of the business-user satisfaction Another great benefit of Taipy’s Scenario function is that it reduces the gap between the end-user and the data scientists. The Taipy scenario registry is a gold mine for data scientists since they can access all end-user’s runs. In addition, the end-user can tag any of these scenarios and share them with the data scientists for examination. This scenario feature can dramatically increase the software's acceptance by the end-user. Unfortunately, in practice, testing AI algorithms is generally limited to a few test cases and the usage of drift detection. More is needed to guarantee a high acceptance of the software. And Taipy’s scenarios will help a lot here. Here are some examples of Taipy AI applications enabling the business user to explore previously generated scenarios. Conclusion To conclude with, Taipy has proven instrumental in the success of AI projects for leading corporations, offering an efficient and user-friendly Python framework. With the launch of Taipy Designer, we continue to democratize AI development, focusing on accessibility for Data Analysts and ensuring the seamless integration of AI into business processes.

Augmenting the Markdown language for Python Graphical Interfaces

Vincent Gosselin — Tue, 05 Mar 2024 15:06:00 GMT

If you ever tried to go global, you have probably faced a reality check. A whole new set of issues starts to appear when you start to operate a workload over multiple locations across the globe: Your distributed systems may stop working: when agents need to coordinate over a network, they often necessitate low latency. Hashicorp's Consul requires the average RTT for all traffic between its agents to never exceed 50ms. You need to secure traffic between your components, distributed over the world: what technology do you choose? You need to maintain that now, too! Building consistent UI React Themes are the backbone of consistent user interfaces, laying the groundwork for styling and design. Understanding react themes React themes are essentially JavaScript objects, housing design attributes like colors, typography, and spacing. These attributes, when centrally defined and used, guarantee a consistent design language throughout your application. Themes are utilized via the ThemeProvider component specify a wrapper component that provides the theme object to all child components within your application. Overhead: for small projects or prototypes, setting up a theme can seem like unnecessary overhead. It's important to evaluate the project requirements before deciding to implement a theme. Learning Curve: understanding how themes work and how to use them effectively requires a bit of learning. But once mastered, it can be a powerful tool in your React toolbox. Scope Management: ensuring that the theme's scope is managed correctly is crucial. A poorly scoped theme can lead to inconsistencies and unpredictability in your design. Step 1: Agreeing on a target vision for a multi-region engine Major architectural changes like this have a long-lasting impact: these decisions can be carried over during 10 years. We needed a future-proof architecture that would hold its ground for at least two or three years to come and support at least 25 locations, actually up to 100 locations. Our goals: efficiency, agility, resiliency (aka better, faster, stronger) React themes are essentially JavaScript objects, housing design attributes like colors, typography, and spacing. These attributes, when centrally defined and used, guarantee a consistent design language throughout your application. React themes are essentially JavaScript objects, housing design attributes like colors, typography, and spacing. These attributes, when centrally defined and used, guarantee a consistent design language throughout your application. Multi-region or not, whenever you want to deploy an application on our platform, it all begins with a POST API call against our API with the desired deployment definition. A deployment definition describes how your app should be deployed and roughly looks like this: If you ever tried to go global, you have probably faced a reality check. A whole new set of issues starts to appear when you start to operate a workload over multiple locations across the globe.

[Taipy Tech Talk #1] Build a Full Python App from Scratch – Replay Available Now!

Rym Michaut — Tue, 05 Mar 2024 11:08:00 GMT

We’re excited to launch the first episode of our brand-new webinar series: Taipy Tech Talks! In this inaugural session, we take you behind the scenes of building a complete, production-ready data application — entirely in Python — using the Taipy framework. This hands-on, developer-first demo is designed for data scientists, Python developers, and technical leads who want to go beyond notebooks and prototypes to deliver real-world, interactive applications. 🎙️ Meet the Speakers This Tech Talk brings together voices from both Taipy’s core team and the user community: Alexandre Sajus – Customer Success Engineer at Taipy Florian Jacta – Community Success Engineer at Taipy Zaccheus Sia – Application Developer at Knowledge Touch, a Taipy user and contributor Together, they walk you through every step of building a real app — while sharing tips and stories from their experiences helping users bring their ideas to life with Taipy. 📚 What You’ll Learn In this live coding session, you’ll watch the team: Set up a new project from scratch using Taipy Build a data pipeline and orchestrate multiple scenarios Design an interactive user interface with charts and buttons using only Python Implement callbacks and logic to make your app dynamic Visualize KPIs and outputs in real time Run the complete app locally and prepare it for production This webinar is perfect whether you’re discovering Taipy for the first time or looking to see it in action in a real use case. 🎥 Watch the Replay Missed the live session? No worries — you can catch the full webinar on our YouTube channel. 👉 Watch now and see how fast you can go from a Python script to a beautiful, interactive data app! Whether you're building dashboards, simulation tools, or full-fledged decision-making applications, this session will show you the path forward with Taipy. If you want to learn more about Taipy book a call with our technical team

How To Create an AI Photo App with Python

Marine Gosselin — Mon, 04 Mar 2024 09:14:00 GMT

What is a Neural Network Builder Let’s start! The first phase is to create a Neural Network for image classification. We will use a Neural Network Builder from TensorFlow and the CIFAR-10 dataset. Tensorflow is an essential for Artificial Intelligence library to develop and train our neural network. The dataset comprises +50,000 images and is crucial for training image recognition models. We will be able to put our model to the test with Taipy, and build an application. ‍ Our model will be trained on the ten categories present in the CIFAR dataset: airplane ✈️ automobile 🚗 bird 🦜 cat 🐈 deer 🦌 dog 🐶 frog 🐸 horse 🐴 ship ⚓ Our model will be able to classify images into these ten categories. Creating a Neural Network Builder Prerequisites Python- The Python programming language should be available on your computer virtualenv - A tool for creating isolated virtual Python environments I will be using virtualenv for this project; however, you can use your preference, like venv, or Conda, and adapt your commands. IMPORTANT : Depending on your setup, you might need to use the command python or python3 when running commands in the terminal Setup Ok, time to build! Run these commands to set your project up: Now let's install the real deal, two Python libraries: TensorFlow and numpy- a library for mathematical operations on arrays. Use the following command: pip install tensorflow numpy ‍ Download CIFAR dataset But first, data! Now, let’s download the CIFAR-10 dataset from here. ‍ The one we need is the CIFAR-10 Python version. When you download and unzip the files, you should see a folder named cifar-10-batches-py. Copy that folder and all the files inside the project we just created: neural-network-builder/venv. ‍ Python script: generate-model.py Create a file called generate-model.py. This will be our Python script for the model training and exporting. Add the code below to the generate-model.py. ‍‍ Run the generate-model file Moment of truth! Run your file. This file will build, train, and save our model. Depending on your computer, this process can take more or less time. Let’s focus on the parameter called epochs in our script, which is set to epochs=50. ‍ ‍ An epoch is an important hyperparameter, representing one complete cycle through all training datasets. Each sample will update the model’s parameters. This cycle is not about the time it takes but the number of times it runs through the data. This key parameter influences the training process. Indeed, having more epochs affects the model’s learning rate. The higher the number, the longer it will take to train the model. Underfitting could happen if there are insufficient epochs for the model to identify the underlying patterns in the data. However, an excessive number of epochs might cause the model to overfit the training set, resulting in subpar generalization on fresh, untried data. To run the script, use this command here (you might need to use Python or python3): ‍ Now, a little patience! Wait for your model to be trained and saved in the model folder. When creating our GUI front end, we will copy that model folder. Now, let’s add a GUI to play with our model! Build our Taipy interface Setup Navigate back to the main project folder ml-photo-app and then run these scripts to set up our interface: ‍ Now, let’s install the Python libraries we will be using: Taipy TensorFlow Numpy Pillow ( PIL) - Python imaging library Run this command: Frontend folder Let’s get our fresh new model and copy it from our neural-network-builder project into our frontend folder. Create two files in our root folder inside of frontend: index.py index.css. These are the files we will run for our GUI. Set your CSS preferences: index.css Add this code to the index.css file we just made: ‍ Create the index.py Python script To create an application with Taipy, you can use Markdown, the Python API, or HTML. In this tutorial, we will use the HTML method, but feel free to use whichever option! And lastly, add this code to the index.py script: ‍ Our application will run on the 8000 port here, but feel free to change it. Run the application ‍ With the use_realoder set to *True”, if you make any changes to your GUI code, you don’t have to rerun everything; just refresh your application page. To run our app, use this command here: How to use the application? Upload any .png, preferably with an image part of these ten categories! airplane ✈️ automobile 🚗 bird 🦜 cat 🐈 deer 🦌 dog 🐶 frog 🐸 horse 🐴 ship ⚓ Have fun seeing how your application classifies images!

Turns Data And AI Algorithms Into Production-Ready Web Applications In No Time

April — Sun, 03 Mar 2024 09:30:00 GMT

Taipy is an open-source Python library for easy, end-to-end application development, featuring what-if analyses, smart pipeline execution, built-in scheduling, and deployment tools. Taipy is designed for data scientists and machine learning engineers to build full-stack apps. Read it on Medium!

Bringing the end-user into the AI picture

Vincent Gosselin — Thu, 29 Feb 2024 09:39:00 GMT

There is a ton of information these days on every single phase involved in building AI algorithms, and this is great! This covers loading/preparing data, feature engineering, training, testing, hyper-parameterization, validation, explainability, MLOps, and deployment. Overlooking End-Users in AI Applications At the same time, I am puzzled to see how little is mentioned about the “end-user”: the end-user being a business person with no AI background interacting with the software. Even if AI has led to many “automated” AI applications (for instance, autonomous vehicles, trading bots, etc), most companies need end-users to “collaborate”/interact with an AI engine. Vincent Gosselin Let’s take two examples: QSR Store Sales Forecast A two-month Cash Flow Prediction for a large Franchised brand. ‍ In Example 1, a QSR store manager connects to the new forecasting software. Through an ergonomic GUI, she/he can generate next week’s sales forecast (created by the AI engine). Then, she/he just discovered 5 minutes ago that a competitor across the road is running a new promotion today. She/He may then opt to lower the generated forecast by 10% during peak hours. Here, the end-user needs to modify the output of the forecast engine. In Example 2, the company treasurer wants to run the Cash Flow Prediction for the next two months. However, he wants to play with different inflation values and evaluate the impact on the forecast. Here, the end-user wants to control an input parameter (the inflation rate) to the AI Engine. There are countless other examples where end-users need to modify an AI engine's input or output. This is an integral part of the Decision Process. ‍ Taipy's Capabilities to enhance end-user interaction with AI To address these situations, we defined (as part of the Taipy open source team) the concept of “scenario” and “data nodes”. A scenario is nothing more than the execution of your algorithm (pipeline) given a set of input information (input data nodes). We have also implemented three essential capabilities: 1. Data Nodes Ability to model pipelines as a sequence of Python tasks as well as Data Nodes (anything that can be an input or an output of a Python task). A data node can connect to any data format (SQL, NoSQL, CSV, JSON, etc) or a parameter (a Python object, i.e., A date entered by the end-user through the graphical interface). 2. Scenarios Ability to record each pipeline execution (inside a registry). We call such execution a ‘scenario’. 3. Scenario comparison Ability to retrieve past/registered scenarios, compare them, track them, etc. We decided to provide two options for defining your pipeline in Taipy: Programmatically or using a Visual Code Graph Editor. ‍ Let's take an example 1. Create a pipeline Let’s take a straightforward pipeline case with: - A single task: “predict”, calling the inference of an AI engine - 2 input Data Nodes: ‘historical_temperature” and “date_to_forecast”. ‍ To create this pipeline, with Taipy, we have two options: Option 1: Programmatical Configuration We can dive into Python code. This script creates a scenario_cfg object: ‍ Option 2: Graphical Editor Configuration Or, we can use Taipy Studio, the Pipeline/DAG Graphical Editor that enhances pipelines creation. (It's a VS Code extension) The scenario_cfg object is then created by loading the previous diagram and saved as a TOML file. Discover Taipy Studio ‍ 2. Execute different scenarios Scenarios are just instances of the previous pipeline configuration. Here: 1. We create a scenario (an instance of the pipeline configuration above) 2. We initialize its input data nodes 3. We execute it (tp.submit()) Note that behind the screen, the execution of a given scenario is registered, i.e., an automatic storage of information related to each data node used at the time of execution. ‍ Benefits This relatively “simple” scenario management process defined in this article allows for: 1. A rich set of user functionalities such as: Easy Retrieval of all scenarios over a given period and their associated input/output data nodes allows easy data lineage. Comparing two or more scenarios based on some KPIs: the value of a given data node. Tracking over time a given KPI Re-executing a past scenario with new values (can change the value of a given data node) 2. Full pipeline Versioning: Essential for quality Project management Overall pipeline versioning is badly needed when new data nodes/sources are introduced or a new version of a given Python code (avoiding incompatibilities with previously run scenarios). 3. Narrowing the gap between Data Scientists/Developers & End-users By providing access to the entire repository of end-user scenarios, data scientists and Python devs can better understand how end-users use the software. ‍ And to go further To help this process, we found it helpful to provide specific graphical objects to explore past scenarios visually, display their input and output data nodes, modify them, re-execute scenarios, etc. For this purpose, we extended Taipy’s graphical library to provide a new set of graphical components for Scenario visualization. Here’s an example of such a scenario ‘navigator’. Conclusion This is our interpretation of scenario management. We hope such an article will trigger more interest and discussion on this crucial topic and lead to better AI software and, ultimately, better decisions.

Memento - Turning your group's nostalgic memories into thrilling, exciting stories!

Arsal Abrar — Wed, 07 Feb 2024 10:23:00 GMT

Inspiration strikes when nostalgia meets innovation. This year's theme of Nostalgia inspired them to create Mememto—a gateway to relive cherished memories through captivating narratives. By inputting images and brief descriptions, users unlock a world where the past comes alive, courtesy of Taipy and Cohere. Join them on a journey of rediscovery, where each click unveils a new chapter of the past. What's next for Memento? The journey has just begun. Read more on Devpost!

New Open Source Python Library Named Taipy Offers Streamlined Data Science Applications

OSDC — Mon, 05 Feb 2024 09:59:00 GMT

A groundbreaking article by OSDC introduces Taipy, as an open-source Python-based framework tailored for data scientists, promising to streamline web application development without the need for HTML, CSS, or JavaScript expertise. Taipy empowers data scientists to create user-friendly applications seamlessly integrated with sophisticated data analyses and machine learning models. Its user-centric design and comprehensive feature set, including dynamic UI generation and robust data management tools, aim to simplify the development process, allowing data scientists to focus on their core competencies. Leveraging Python's versatility, Taipy enables the creation of engaging web applications directly from the Python environment, fostering a more direct connection between data teams and users. With features like scenario configuration without code and Visual Studio Code extension, Taipy presents a significant development in web application creation, potentially reshaping future application development paradigms. Read it on Medium!

Convert your Python application into a website in 2 minutes

Pratham — Fri, 02 Feb 2024 10:21:00 GMT

There's no easy way than this to build a data, AI-based web applications.I came across Taipy. It is an open-source Python library for building production-ready front-end & back-end in no time. Read it on Rattibha!

How to run your first local LLMs

Eric Narro — Thu, 01 Feb 2024 10:16:00 GMT

Within the past twelve months (as I am writing this in 2024), large language models (or LLMs) have transformed the professional environment and how we carry tasks out. You certainly are familiar with Chat GPT and their Chat version. You may be a Chat GPT Plus user (OpenAI’s paid plan). Or may have used other similar providers, such as Anthropic, or Google Bard. And you may even be a developer who has used some LLMs via an API (like OpenAI’s API and Python client — I wrote about it before — or using LangChain). All these options are great, they provide ready-to-go access to LLMs and to infrastructure that is able to handle them. Read it on Medium!

Meet Taipy: An Open-Source Python Library Designed for Data Scientists and Machine Learning Engineers for Easy and End-to-End Application Development

Niharika Singh — Wed, 31 Jan 2024 10:18:00 GMT

Biharika introduces Taipy as a Python-based framework designed to simplify full-stack application development for data scientists and machine learning engineers. It eliminates the need to learn additional languages or complex frameworks, offering user-friendly interface generation, pre-built data pipeline components, robust scenario and data management features, and version control tools. By streamlining development and enabling focus on data and AI algorithms, Taipy enhances efficiency and impact across diverse business applications, making it a practical and efficient solution for professionals in the field. Read it on Marketpost!

Why Taipy Must ALWAYS Be Your Go-to Data Application Builder Tool

Avi Chawla — Mon, 22 Jan 2024 10:05:00 GMT

Data scientists are fond of using Jupyter to explore data and build models. However, an interactive app is better for those who don’t care about our code and are interested in results, or when we are building user-facing apps. Read it on Dailydoseofds!

Unlocking the Power of Taipy: Create Full-Stack Web Apps Effortlessly

Leo Liu — Sat, 20 Jan 2024 10:28:00 GMT

The article introduces Taipy, our Python-based framework for full-stack web application development, highlighting its user-centric approach and robust features as a compelling alternative to frameworks like Streamlit. Taipy is presented as suitable for both beginners and experienced developers, excelling in tasks such as data visualization and manipulation. Presented key Features of Taipy: Intuitive Design Pandas Integration Customizable UI Components Data Visualization Tools Direct File Downloads Taipy's Simplicity Demonstrated:The article provides a 'Hello World' example showcasing the simplicity of Taipy's code for a basic GUI application. Interactive Features:Taipy's ability to enhance user interaction is demonstrated with an example of an interactive slider that dynamically updates a value. Styling and Layout Customization:Developers can beautify applications using various styling options, illustrated in an example where CSS styling is applied to elevate the user interface. Structured Layouts:Taipy allows the organization of applications into distinct sections for a more structured layout. An example divides an application into sections for a slider and a chart, showcasing flexibility in layout design. Read it on Medium!

These Five Free Websites Will Change Your Way of Doing Web Development From Beginner to Master

Abu Bakar — Fri, 19 Jan 2024 10:26:00 GMT

The article introduces five game-changing apps that aim to revolutionize the way web developers approach their work. These apps offer enhanced collaboration, comprehensive documentation, AI-powered assistance, visual prototyping, and workflow automation to empower web developers and elevate their skills. Read it on Medium!

We raised $5 Million to our open-source project

Vincent Gosselin — Mon, 15 Jan 2024 09:51:00 GMT

We are excited to share with you this wonderful news: we completed our $5M Seed Funding round last month! The Taipy Story A few years back, Albert and I, after years of leading AI projects for large organizations, decided that it was time to transition to full Python development and stop using traditional Java, JS, .Net stacks, etc. We had a pretty clear idea of what features we were looking for, but to our surprise, we couldn’t find them amongst the flurry of existing Python packages. Our mission was straightforward yet ambitious: to provide the missing bricks that prevent so many AI/Data pilots from making it into a successful deployed project. In particular, we wanted to bring the end-user back into the “AI/Data” picture. I am still amazed today to see how little is mentioned about the end-user: from data scientists to data engineers, is it all about data flows, exposing algorithms, etc.? No mention of how a human being will interact with AI/Data models… We wanted to change all that! So we decided to build Taipy… Taipy combines: A powerful Interactive front-end Application builder is yet very simple to learn/use. “Scenarios”, that is the possibility for the end-user (and the data scientist as well) to interact with data and algorithms easily. In 2022, we first launched Taipy as an open-source project (check out our GitHub Page), followed by the Enterprise version later that year. We want to express our heartfelt gratitude to our incredible community. Our GitHub project is trending and saw a phenomenal rise in popularity, growing from 100 to over 3,000 stars in a few weeks! I would also like to thank our early corporate adopters and partners, who are so important in validating and testing a new technology. Specials thanks to McDonald’s, KnowledgeTouch, Groupe Les Mousquetaires, Textile Apparel Limited, Total Energies, IFP-EN, etc. Taipy’s future This significant investment allows us to continue our full-time commitment to improving Taipy with a fantastic release coming up soon (Q1 2024). This funding is also a crucial step towards realizing our vision, positioning Taipy as the leading platform for Python AI/Data projects. 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝘁𝗼 𝘁𝗵𝗲 ‘near’ 𝗳𝘂𝘁𝘂𝗿𝗲: Distributed Computing: Execute on remote clusters for parallel scenario/task execution. A brand new GUI Designer: you can build your GUI page with no coding! Integration with major Platforms like Databricks, Dataiku, etc. On the communication side: We’re now present on several community platforms: Discord, LinkedIn, X, and YouTube. We sponsored many events: conferences, hackathons, meetups, webinars… We also plan to start regular Taipy Tech-Talks soon. Thank you for believing in us! This journey wouldn't have been possible without the dedication of Taipy's contributors and the unwavering support of our investors, advisors, and co-workers who share our vision. ‍ Together, we are transforming the landscape of AI and Data projects. Let's continue this incredible journey together, creating solutions that empower businesses and developers alike. Vincent Gosselin & Albert AntoineCo-founders of Taipy ‍ Haven't checked out Taipy yet? Feel free to visit our GitHub page. Feel free to ⭐ the Taipy repository

Build your AI / Data full-stack applications

Toolify AI — Wed, 10 Jan 2024 13:08:00 GMT

Discover Taipy, an open-source AI web and app builder in Python. Explore its versatile features, including generative AI for rapid development and intuitive data management through Taipy. Demo: Excel Data Analysis: Experience Taipy in action with a hands-on demo. Learn how to extract and analyze data from an Excel file using Taipy's intuitive features, showcasing its efficiency in handling and visualizing data. Taipy emerges as a game-changer in the web development landscape, offering a seamless blend of AI innovation, user-friendly features, and powerful data management. Elevate your application development journey with Taipy! 🚀 Read more on toolify.ai!

Taipy: Build UIs easily with only Python!

Abish Pius — Wed, 03 Jan 2024 13:13:00 GMT

Dive into Taipy with this beginner-friendly guide. Learn to install, configure, and create your first application with ease. Read it on Medium!

2023 Wrapped: Best Python Libraries of 2023🌯

Manoj Das — Sun, 31 Dec 2023 13:23:00 GMT

As 2023 comes to an end, let’s take a look back at the notable libraries of the year. As we step into the promising realms of 2023, it becomes abundantly clear that this year holds exceptional promise for Python enthusiasts and developers alike. With its unparalleled versatility, robust libraries, and widespread adoption across various industries, Python stands poised at the forefront of technological innovation. From cutting-edge advancements in artificial intelligence and machine learning to its pivotal role in web development and data science, Python continues to shape the digital landscape, making 2023 a truly remarkable year for the language that has become synonymous with efficiency and adaptability. Read it on Medium!

Big Data Charting Strategies in Python

Vincent Gosselin — Thu, 21 Dec 2023 09:53:00 GMT

This article is a follow-up to the article Python Charting: Big Data without crashing. This article can be read independently from this previous article. ‍ As Data Scientists, we are challenged very often with large data sets that need to be displayed graphically. I am not even talking about Big Data here: a mid-size dataset greater than tens of thousands of points may represent a challenge for most Python libraries, let alone data sets with millions of data points. One common approach is to use various heatmap techniques (such as datashader, etc). However, this only covers a small set of situations, usually involving maps. The bread & butter of data scientists remain time series and scatter plots. Taipy (A Python library to fast-track your front-end and back-end development) addresses this problem with “Decimators”. These provide some clever’ implementation of data sampling in order to minimize the volume of data transiting between the application and the GUI client, without losing information.If one considers a table (dataframe, array, etc) with just 20 columns and 500K rows, generating a graphical view for such a table will require over 1 minute on a 10MB/s network! Taipy manages to reduce ‘smartly’ the number of points that define the shape of the plot/curve. ‍ Taipy Decimators for Time Series The basic “algorithm” The process of ‘reducing’ a dataset by eliminating ‘non-significant’ points is generally referred to as ‘decimation’ or ‘downsampling’ (or even ‘subsampling’). We apply here the MinMaxDecimator to the curve for the Apple stock price over time. This is not a very large dataset but the principle described here works for any size of dataset including those that will crash your Python environment (due to lack of memory most likely). ‍ ‍ Note that the Taipy code that displays the two charts is condensed in the lines ‍ ‍ Taipy provides two methods to generate graphics: One is based on an ‘augmented’ markdown syntax. This is our choice here. You get the markdown facility extended with graphical syntax receiving Python objects. Here a dataframe, df_AAPL is an input to the ‘chart’ markdown object. More can be found in Taipy’s documentation. The second is a pure Python interface. No markdown. ‍ You get the following results: A few comments on these two charts: One can notice on the right-hand side graph that the shape of the curve is preserved even though the number of points is reduced (limited to 300 points). The MinMaxDecimator removes the points that least modify the shape of the curve and keeps the 300 most meaningful points. Note that 300 is just a parameter. Second, if you zoom in on the second chart, the graphical object will ‘repopulate’ the selected area with 300 points again. This allows the ‘optimal’ display of the data, whatever zoom level is applied by the end-user. The results after zooming: the diagram on the right still displays 300 points representing the selected area. ‍ ‍ Beside the MinMax decimator, there are two other types of decimators worth exploring: the RDP and the LTTB decimator. ‍ Taipy Decimators for clouds of points (2D or 3D) The previous decimators are well suited for 2D line curves but are not satisfactory for scatter plots: for instance, in the situation where you want to visualize clusters. Here, we will be using the ScatterDecimator. Here’s a Python script displaying two ‘scatter’ charts: One without the decimator And the second with the decimator. The first part of the script is about creating a data set for classification (using sklearn’s make_classification() generator). The second part is similar to the previous script, with Taipy’s markdown generating two different graphs. ‍ ‍Here are the resulting charts: ‍ Again, the right-hand chart displays many fewer points, allowing for better visuals (whereas in the first chart, the shape of the clusters gets obfuscated). The interesting thing is that in the second graph, the outer envelop of each cluster is respected (the frontier points of each cluster are preserved). ‍ Performance Some substantial differences in performance between the various decimators are also worth noting. Min-Max: 1 (reference value) LTTB: 10 x RDP (Ramer–Douglas–Peucker using the 'epsilon' threshold): 20 x RDP (Ramer–Douglas–Peucker(using a fixed number of points): 800 x ‍ ‍ Conclusion Taipy offers the possibility to apply or not a decimation algorithm. A developer can choose which algorithm to use and with what parameters. If this algorithm is to impact the application's performance, it is always possible to fine-tune these parameters. The benefits of it are: A massive decrease in the load of the network and the memory used (on the client side) More meaningful visuals get displayed A much better / smoother user experience ‍

Python Charting: Taming Big Data Without Crashing

Alexandre Sajus — Tue, 12 Dec 2023 12:22:00 GMT

Our focus this year with the R&D team was to minimize the volume of data transiting between the application and the GUI client, without losing on the information. When we talked about Data we are referring to large Tabular data with plenty of columns and even more rows. If we consider a table (dataframe, array, etc) with just 20 columns and 500K rows, generating a graphical view for such table will require over 1 minute on a 10MB/s network! ‍ 10 Mb/s x 8 (bits) x 8 (bytes) * 20 (columns) * 500,000 / 10,000,000 (network throughput) = 64 sec ‍ If we manage to reduce "smartly" the number of points that define the shape of the plot/curve, we will get overcome: The network bandwidth (see above) The memory used on the client side A more meaningful visual (client side) since the plot will involve a much lower number of data points. A much better user experience: when the end-user clicks on a point to perform some action, the graphical component has to seek which points amongst the whole dataset have been selected. With a reduced number of points, performance will improve drastically. ‍ This work is completely disconnected from data transformation, such as compression algorithms. It is known that HTTP/1.1 already supports data compression (see the Internet Engineering Task Force). ‍ First level: dealing with curves (2D) The basic “algorithm” The first effort was to transform data (with hundreds of thousands of points or more) on the application side to a reduced form that can be plotted as a curve visually equivalent to the full data set. Such reduced form should not exceed the maximum resolution of the screen/frame (usually on the horizontal axis). The process of "reducing" a dataset by eliminating "non significant" points is generally referred to as "decimation" or "downsampling" (or even "subsampling"). ‍ Important: This implementation must allow the end-user to select (graphically) a different subset of the displayed curve with an automatic re-run of the "decimator algorithm". This will allow the optimal" display of the data whatever zoom level is applied by the end-user. We evaluated three different algorithms: The Min-Max algorithm The LTTB algorithm The Ramer-Douglas-Peuker algorithm ‍ Min-Max algorithm Let NOP be the desired number of points to be represented on the client side. The Min-Max algorithm divides one of the dimensions into as many segments as the number of points (NOP). For each segment only the 2 extreme points (on the second axis) of the original dataset are kept. Only the extreme values are kept. Performance is excellent. ‍ LTTB Algorithm This algorithm is also referred to as “Largest Triangle Three Buckets”. This algorithm forms triangles between adjacent points to decide the relative importance of these points. This is a technique used often in cartography. Several LTTB implementations are available in Python. Performance of this downsampling algorithm is also excellent. ‍ The Ramer-Douglas-Peucker Algorithm This algorithm uses a completely different approach. It seeks “important” points in the initial/large datasets. These important points are points that if removed, do modify drastically the shape of the curve. Another way to put it is: this algorithm removes all the points that least modify the shape of the curve. More information can be found in the seminal paper: “The Douglas-Peucker Algorithm for Line Simplification“. M. Visvalingam & J. D. Whyatt, 1990. Then an improved version of the algorithm: “Line generalization by repeated elimination of points“. M. Visvalingam & J. Whyatt, 1993. Performance is not as good as the previous ones, but the quality of the results (as demonstrated below) is much better. ‍ Second level: dealing with a cloud of points (3D or more) The previous algorithms are well suited for 2D curves but are not satisfactory to higher dimension visuals: if several data points of the original dataset get projected on the same space, they would simply be removed if using the previous algorithms. Here, the density of the points needs to be represented. We developed a specific algorithm using the following approach: We divide the representation space (client side) into a mesh space. We minimize the number of points projected in the same cell (of the mesh) We ensure that cells with a single point are preserved. ‍ Results Presenting here quantitative results, in terms of time or memory saved, when applying adaptive optimization algorithms is not very useful. Indeed, the achieved performance improvements are closely related to three independent factors: the computational power of the application server and its available memory, the network bandwidth connecting the server to the client, and the client-side itself, where the type of browser also comes into play. On the other hand, a commented visualization of the results obtained can be interesting. Here is a typical curve in front of which a data analyst should make decisions: ‍ ‍ This curve is the result of sampling an anonymous dataset and contains more than 15,000 points. Its display is performed by a thin line connecting markers at each point representing a sample. We chose this dataset because it contains several types of progressions that will interest us in judging the results: an area where the data progresses slowly, more abrupt variations, and local disturbances. All points are represented in the above curve, and we will apply our algorithms to these data to judge the visual quality of decimation—how satisfactory the result is and at what cost. For each of the selected algorithms, we decimated the initial dataset by setting the number of output points to: 500, to visually compare the shapes of the curves and check the approximation's satisfaction. This number of curve definition points seems consistent with the intended use, occupying between half and one-third of the common horizontal screen resolutions. 100, to visually account for how the algorithm behaves in different regions of the original curve. The degradation is considerable and unacceptable in the context of an application. ‍ Min-Max Algorithm Here is the result of executing this algorithm on the initial dataset: ‍ We can observe that the overall appearance of the curve is well preserved, as we hoped. An obvious drawback is visible on the low-quality curve: the uniform segmentation of the axis causes a kind of waste of output points at the beginning of the curve, where little variation is visible. ‍ LTTB Algorithm If we execute this algorithm on our dataset, we obtain the following curves: ‍ We can observe (on the low-quality curve) the same problem on the left part of the curve. This is not surprising since the surface of the evaluated triangles will increase with the abscisse spacing, retaining potentially non-significant points. On the other hand, we can see greater precision in the part where values increase rapidly (first peak): both local peaks are well preserved whereas the Min-Max had eliminated one of the two. ‍ Ramer–Douglas–Peucker Algorithm The third algorithm gives the following curves: ‍ This time, it is clear that the left part of the curve (with its low variability) is approximated by one or two segments, which seems optimal. This allows "saving" additional points to better define the rest of the produced curve. The performance of this algorithm is significantly worse than for the two algorithms described above but potentially more 'precise' concerning the shape of the curve. Unlike the two previous algorithms, this algorithm in its original form does not start from the target number of points to obtain as output. It uses a threshold value ('epsilon') beyond which we consider that a segment deviates angularly too much from the previous one, causing the selection of its destination point as 'significant.' A modified form of this algorithm is needed if one wants to impose a target number of points (like the other algorithms). A pre-processing phase is needed, which is more costly. Performance As we have already mentioned, providing absolute performance values would not be very useful, as the execution conditions of our algorithms can vary drastically. However, relative performance data can be useful. Here they are: Min-Max: 1 (reference value) LTTB: 10 x Ramer–Douglas–Peucker (using the 'epsilon' threshold): 20 x Ramer–Douglas–Peucker (using a fixed number of points): 800 x ‍ Conclusion Our Community Edition offers the possibility to apply or not a decimation algorithm. A developer can choose which algorithm to use and with what parameters. If this algorithm is to impact the application's performance too much, it is always possible to fine-tune these parameters. In the case where the end-user zooms in or out or scrolls the curve, the server must calculate the new data entirely represented. It is important then that the developer understands the consequences of his choices in terms of: - manipulated data volume - the parameters of each decimation algorithm.

Integration of a Hamilton ARC analytical sensor into a Wi-Fi network using ESP-32

Jaime Eduardo Navarrete Rodriguez — Sun, 03 Dec 2023 13:27:00 GMT

This article explores ARC family sensors, particularly the Hamilton ARC analytical sensor, for industrial network communication using the Modbus RTU protocol. It emphasizes the sensors' ability to act as servers, eliminating the need for transmitters, and details the use of VP8 cables for sensor connections. The author discusses Hamilton Process Analytics' wireless solutions, including a Bluetooth adapter, and introduces their motivation to develop a Wi-Fi/Modbus RTU communication gateway using the ESP32 for a Hamilton ARC pH analytical sensor. The article includes a code snippet for the ESP32 gateway, highlighting its low hardware cost but the demand for programming expertise. In conclusion, the ESP32 is suggested as a promising gateway option for smaller-scale or budget-conscious applications in the Industrial Internet of Things (IIoT) landscape. Read it on LinkedIn!

Taipy: Create Production Ready Apps with AI FOR FREE!

Vincent Gosselin — Sat, 02 Dec 2023 12:17:00 GMT

In this video, we delve into the magic of Taipy, the all-in-one solution for both front-end and back-end web development. No need for extensive coding knowledge; Taipy simplifies the process, from prototypes to full-scale applications! Whatch it on YouTube!

Build, Visualize and Launch Big Data DAGs

Rym Michaut — Thu, 30 Nov 2023 13:56:00 GMT

Using PySpark with Taipy Taipy is a powerful workflow orchestration tool with an easy-to-use framework to apply to your existing data applications with little effort. Taipy is built on a solid foundation of concepts — Scenarios, Tasks and Data Nodes — which are robust in allowing developers to easily model their pipelines, even when using 3rd party packages without explicit support. ‍ If you’re already familiar with PySpark and Taipy, you can skip ahead to “2. The Taipy configuration (config.py)”. That section dives right into the nitty-gritty of defining a function for a Taipy task to run a PySpark application. Otherwise, read on! ‍ This article will employ a simple example to demonstrate how we can integrate PySpark with Taipy to couple your big data processing needs with smart job execution. A Simple Example: palmerpenguins Let’s use the palmerpenguins dataset as an example: ‍ ‍ This dataset only contains 344 records — hardly a dataset which requires Spark for processing. However, this dataset is accessible, and its size is not relevant for demonstrating the integration of Spark with Taipy. You may duplicate the data as many times as you need if you must test this with a larger dataset. ‍ We’ll design a workflow which performs two main tasks: 1- Spark task (spark_process): Load the data; Group the data by “species”, “island” and “sex”; Find the mean of the other columns (”bill_length_mm”, “bill_depth_mm”, “flipper_length_mm”, “body_mass_g”); Save the data. 2- Python task (filter): Load the output data saved previously by the Spark task; Given a “species”, “island” and “sex”, return the aggregated values. Our little project will comprise of 4 files: You can find the contents of each file (other than penguins.csv which you can get from palmerpenguins repository) in code blocks within this article. 1. The Spark Application (penguin_spark_app.py) Normally, we run PySpark tasks with the spark-submit command line utility. You can read more about the what and the why of submitting Spark jobs in this way in their own documentation here. When using Taipy for our workflow orchestration, we can continue doing the same thing. The only difference is that instead of running a command in the command line, we have our workflow pipeline spawn a subprocess which runs the Spark application using spark-submit. Before getting into that, let’s first take a look at our Spark application. Simply glance through the code, then continue reading on for a brief explanation on what this script does: We can submit this Spark application for execution by entering a command into the terminal like: Which would do the following: Submits the penguin_spark_app.py application for local execution on 8 CPU cores; Loads data from the app/penguins.csv CSV file; Groups by “species”, “island” and “sex”, then aggregates the remaining columns by mean; Saves the resultant DataFrame to app/output.csv. Thereafter, the contents of app/output.csv should be exactly as follows: Also, note that we have coded the Spark application to receive 2 command line parameters: — input-csv-path : Path to the input penguin CSV file; and — output-csv-path : Path to save the output CSV file after processing by the Spark app. 2. The Taipy configuration (config.py) At this point, we have our penguin_spark_app.py PySpark application and need to create a Taipy task to run this PySpark application. Again, take a quick glance through the app/config.py script and then continue reading on: ‍ You can also build the Taipy configuration using Taipy Studio, a Visual Studio Code extension which provides a graphical editor for building a Taipy .toml configuration file. The PySpark task in Taipy We are particularly interested in the code section which produces this part of the DAG: Let’s extract and examine the relevant section of the config.pyscript which creates the “spark_process” Spark task (and its 3 associated data nodes) in Taipy as shown in the image above: Since we designed the penguin_spark_app.py Spark application to receive 2 parameters (input_csv_path and output_csv_path), we chose to represent these 2 parameters as Taipy data nodes. Note that your use case may differ, and you can (and should!) modify the task, function and associated data nodes according to your needs. For example, you may: Have a Spark task which performs some routine ETL and returns nothing; Prefer to hard code the input and output paths instead of persisting them as data nodes; or Save additional application parameters as data nodes and pass them to the Spark application. Then, we run spark-submit as a Python subprocess like so: Recall that the order of the list elements should retain the following format, as if they were executed on the command line: Again, depending on our use case, we could specify a different spark-submit script path, Spark arguments (we supplied none in our example) or different application arguments based on our needs. Reading and returning output_csv_path Notice that the spark_process function ended like so: In our case, we want our Taipy task to output the data after it is processed by Spark — so that it can be written to the processed_penguin_df_cfg Parquet data node. One way we can do this is by manually reading from the output target (in this case, output_csv_path) and then returning it as a Pandas DataFrame. ‍ However, if you don’t need the return data of the Spark application, you can simply have your Taipy task (via the spark_process function) return None. ‍ Caching the Spark Task Since we configured spark_process_task_cfg with the skippable property set to True, when re-executing the scenario, Taipy will skip the re-execution of the spark_process task and reuse the persisted task output: the processed_penguin_df_cfg Pandas DataFrame. However, we also defined a validity_period of 1 day for the processed_penguin_df_cfg data node, so Taipy will still re-run the task if the DataFrame was last cached more than a day ago. ‍ 3. Building a GUI (main.py) We’ll complete our application by building the GUI which we saw at the beginning of this article: If you’re unfamiliar with Taipy’s GUI capabilities, you can find a quickstart here. In any case, you can just copy and paste the following code for app/main.py since it isn’t our focus: Then, from the project folder, you can run the main script like so: Conclusion Now that you’ve seen an example of how to use PySpark with Taipy, go on and try using these two tools to enhance your own data applications! If you’ve struggled with other workflow orchestration tools slowing down your work and getting in your way, don’t let it deter you from trying Taipy. Taipy is easy to use and strives to not limit itself in which 3rd party packages you can use it with — its robust and flexible framework makes it easy to adapt it to any data application. You can find all the code and data in this repository. ‍ Star us on GitHub : Avaig/taipy ‍

Magical Python Library to Create Simple, Quick & Efficient Way Build a Full-Stack Data App

Manoj Das — Sun, 26 Nov 2023 13:09:00 GMT

In this article, Manoj Das provides an overview of Taipy, and how it aims to address challenges in data processing professions, back-end development, and data science applications. The article compares Taipy with Streamlit, highlighting Taipy's flexibility in design and superior data handling capabilities. Manoj Das explains Taipy's core concepts, including GUIs, Data Nodes, Tasks, Jobs, Scenarios, Cycles, and Scope. Taipy Studio, an extension for Visual Studio Code, is introduced to streamline application development. The article concludes with an example showcasing Taipy's ability to analyze CSV files, and humorously suggests that a programmer chose Taipy over Streamlit for its flexibility and style. Read it on Medium

A quick, free, Python-only alternative to Power BI

Eric Narro — Wed, 22 Nov 2023 17:30:00 GMT

This article explores the two main components of Taipy, Taipy front-end and Taipy back-end, and demonstrates how to leverage their capabilities to build interactive dashboards. The tutorial assumes basic knowledge of Taipy and guides you through the creation of a wine production dashboard for France from 2009 to 2019, utilizing open data sources. The application features two sections, showcasing wine production by region and campaign, as well as production trends over time. Whether you're a beginner or an experienced developer, discover the flexibility and functionality of Taipy for creating dynamic dashboards with Python. Read it on gitconnected

Taipy 3.0: Supercharge Taipy's Usability to the Next Level with these new features, 2nd one is the best!

Rym Michaut — Tue, 17 Oct 2023 15:06:00 GMT

Dear users & community members, In our relentless pursuit of simplifying the user experience, Taipy proudly presents Taipy 3.0, a release that transcends boundaries in data-driven application development. Taipy 3.0 introduces significant features that elevate the user-friendliness and efficiency of the platform, going even further to simplify the Taipy experience. What is Taipy? Taipy is an open-source Python library for building web applications’ front-end and back-end. Building Interactive Dashboards with Ease Taipy helps develop an application’s front-end without knowing HTML, JS, and CSS. It provides a simple syntax, accelerating the creation of interactive and customizable multiple-page dashboards. Taipy generates highly interactive interfaces, including charts and widely used controls. Empowering Back-End Applications Taipy is also used to develop the back-end of an application. It models dataflows and orchestrates pipelines. Each pipeline execution is referred to as a scenario. Scenarios are stored, recorded, and actionable, enabling what-if analysis and KPI comparison. ‍ New Features in Taipy 3.0 Let’s dive into the exciting features and improvements that Taipy 3.0 brings to the table: ‍ 1-Simplifying Scenario Management Taipy 3.0 revolutionizes scenario management by discarding the concept of pipelines from the scenario configuration. This innovation doesn’t just simplify; it redefines the process, making scenario setup faster and more straightforward. 2-Python API for Building Effortless Taipy GUI Pages The all-new Page Builder API takes Taipy’s user interface creation to a higher level. With this feature, users can create interactive and customizable interfaces using pure Python, taking front-end development to a whole new level of simplicity.‍ 3-Scenarios Management Visual Elements Adding a new layer of usability, Taipy 3.0 introduces two controls to visualize and edit data nodes and jobs within scenarios. These visual elements simplify the complexities of managing data flows, making it more accessible to users at every level of expertise. ‍ 4-Templates for Instant Application Creation Templates, accessible directly from the CLI, further enhance the application creation process, reducing it to a matter of mere clicks. Users can create, display, manage, and run scenarios from the GUI page with unparalleled ease, making the path to building production-ready web applications simpler than ever. ‍ 5-Application Version Management Taipy 3.0 reimagines version control, now integrated into the Taipy Command Line Interface (CLI). This feature empowers users to create, manage, and migrate different versions of their applications effortlessly, eliminating the complexities of version control that have often posed challenges. 6-Data Broadcasting In Taipy 3.0 Application variable updates can be broadcasted to every connected user so that all of them visualize the same information. This is specifically relevant when monitoring live global data. ‍‍ 7-Expanding Data Visualization Horizons with New Charts Taipy 3.0 extends data visualization options by introducing two new chart types: Treemap and Waterfall. These additions provide greater flexibility and clarity in data presentation, taking data visualization further. 8-Automating Tasks with Scheduler API The Scheduler API in Taipy 3.0 allows users to schedule Python functions to run at specific times, dates, or intervals. This feature simplifies automation within applications, taking the concept of usability to a new level. ‍ 9-Enhanced User Interaction with Multi-Knob Sliders Now, users can display multiple values on a slider control and let users select each, providing a seamless and intuitive way to interact with data. Additionally, you can create a slider to select a date range, simplifying date-related interactions in your applications. ‍ ‍ Conclusion Taipy 3.0 doesn’t just simplify; it redefines what “simplification” means in data-driven application development. Whether you’re a seasoned developer or just beginning your journey, Taipy 3.0 offers a new level of simplicity. Dive into these features and unlock the full potential of your data-driven projects with Taipy 3.0. Stay tuned for more updates and tutorials as we continue to push the boundaries of usability with Taipy 3.0. Have great fun developing pilots and projects with Taipy, and stay tuned for upcoming updates! And thank you for inspiring us with your feedback, enthusiasm, and creativity. ‍ Please keep being proud of your web applications and share them with us. And don’t forget to keep sending us comments, ideas, bugs, feature requests, articles, or even words of encouragement on our GitHub repository. ‍ For comprehensive details on these exciting new features: ‍ Read it on the Documentation

Bridging the Gap: Taipy Sponsors PyData NYC 2023

Rym Michaut — Tue, 17 Oct 2023 13:06:00 GMT

This year, Taipy Sponsors PyData NYC 2023. The 2023 edition of this conference was a star-studded event that saw the participation of Taipy, with a dedicated team of experts. Alexandre Sajus, Florian Jacta, and Marine Gosselin took center stage, representing Taipy, and made sure that the event lived up to its reputation as one of the most inclusive and respectful conferences in the Data and Python community. In this article, we'll explore Taipy's involvement in PyData NYC (New York City) and its significant contributions to the event. What is PyData NYC? PyData NYC is more than just a conference; it's a commitment to creating an inclusive and respectful environment that welcomes participation from people of all backgrounds. The conference fosters deeper discussions and aims to build a stronger community by encouraging diversity and inclusivity. PyData NYC's diversity statement reflects its dedication to promoting a culture of mutual respect, tolerance, and learning. A diverse community, where individuals treat each other respectfully, holds the potential for more contributors, a wider array of ideas, and fewer shared assumptions that can sometimes hinder development or research. The conference recognizes that certain identities may be disproportionately impacted by systemic discrimination and marginalization. Regardless of identity or background, PyData NYC and the NumFOCUS community are open to welcoming all individuals. Taipy sponsors PyData NYC 2023 At PyData NYC, Taipy made a significant impact through its booth and a talk. The Taipy team introduced attendees to Taipy's latest features through its 3.0 release, which aired just a few days before. But the excitement didn't stop there; Taipy also presented TalkToTaipy, the latest additions to their toolkit. You met us at the Booth: The Taipy booth at PyData NYC served as a hub for engaging with conference participants. Marine Gosselin, a key member of the Taipy team, interacted with attendees, providing insights into Taipy's capabilities, and answering questions. The booth was abuzz with discussions on how Taipy is simplifying web development and data analysis. You joined us at the Talk: The highlight of Taipy's presence at PyData NYC was the talk presented by Alexandre Sajus and Florian Jacta. The dynamic duo showcased Taipy, clarifying that you don't need to be a web development expert to create highly interactive applications. They discussed how Taipy fills the gap in the Python backend stack, offering powerful solutions for data-driven applications, especially with its new release, which aired just a few days earlier. This 3.0 offers many new features to go further in the simplification of Taipy usage a Python API, a Scheduler API, an application versioning, data broadcasting, templates... and so on, Check the whole list HERE TalkToTaipy: In addition to the existing Taipy features, PyData NYC attendees got a sneak peek into TalkTo aipy. TalkToTaipy: Taipy has developed its own LLM application. This Large Language Model tool is a groundbreaking Python Web App designed to revolutionize how you interact with data. Here's why this tool is a game-changer: 📈 𝗘𝗳𝗳𝗼𝗿𝘁𝗹𝗲𝘀𝘀 𝗗𝗮𝘁𝗮 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: With TalkToTaipy, querying your dataset becomes a breeze. It delivers results in the form of tables and plots effortlessly. Want to create a pie chart to visualize sales by product line or pinpoint the top five most profitable cities? It's as simple as having a conversation! 💡𝗧𝗵𝗲 𝗠𝗮𝗴𝗶𝗰 𝗕𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝗦𝗰𝗲𝗻𝗲𝘀: TalkToTaipy harnesses the power of an 8Gb quantized version of StarCoder, the renowned Hugging Face Language Model, hosted on Microsoft Azure. Utilizing the brilliance of few-shot learning, it processes your queries and provides precise, data-driven answers. 🌐 𝗦𝗲𝗮𝗺𝗹𝗲𝘀𝘀 𝗨𝘀𝗲𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: Our web domain taipy.io ensure a smooth and reliable experience every time you use TalkToTaipy In conclusion, Taipy's presence at PyData NYC 2023 added a layer of innovation and excitement to an already vibrant conference. With its commitment to making web development and data analysis accessible to all, Taipy left a lasting impression on attendees and reinforced its place as a key player in the data and Python community. ‍

How to use TAIPY Core: build pipelines for better applications

Eric Narro — Fri, 29 Sep 2023 16:58:00 GMT

Eric Narro’s thoughts about Taipy Core “Using Taipy Core is a bit more complex than using Taipy GUI, but this is normal because the tasks that it is used for are also usually more complicated (whatever the tool). And maybe this also talks well about Taipy GUI, if it makes other things look a bit harder!” “Another feeling I have is that Taipy Core is meant to be used for complex situations, and this is why it is also hard to make a minimal app using it, it “does not make sense” in a way, because you are not using its real power (just like it wouldn’t make sense to use an 18-wheeler to commute to work — you get the idea). But I am glad I explored it, because it will allow me to do more elaborated tasks in the future (so it makes sense to start with small examples to figure things out). I would also add, similarly, that Taipy Core seems more adapted to Data Science applications, which is not, to this date, my domain (but I plan to dive deeper in there, so Taipy may be a tool I will continue using as I learn more about models).” In this article Taipy Core — general concepts Taipy Studio overview Create A scenario, a Pipeline, 2 Data Nodes and a Task Demo App with Taipy Core Creating a configuration diagram file Personal thought about Taipy and Taipy Core Read it on Medium

How to send an email using Taipy

Nylas — Fri, 15 Sep 2023 17:20:00 GMT

Hello and welcome to another episode of Coding With Nylas Live Stream where we talk about programming APIs and more. […] Today we’re gonna talk about how to send an email using Taipy. Taipy is yet another web framework from Python where you don’t need to use any HTML, CSS, JavaScript, or anything like that. It’s along the lines of a NiceGUI, Solara, Reflex… Taipy is pretty amazing! You’re going to see it’s completely different from the other ones, so this is going to be an exciting episode!

Streamlit vs. Taipy — The Ultimate Comparison

Avi Chawla — Sun, 03 Sep 2023 17:07:00 GMT

The article compares Streamlit vs. Taipy, another tool for turning Python scripts into web apps, based on several parameters. It highlights the differences between Taipy and Streamlit, emphasizing their suitability for data teams based on flexibility, big data support, and application execution. Let’s begin 🚀! Comparison points in this article Prototyping Callbacks Design Flexibility Big Data Support Dedicated Backend Framework Conclusion ‍ Read it on Medium

Build Elegant Web Apps for Your Data Projects Using Low-Code Taipy

Avi Chawla — Wed, 30 Aug 2023 17:16:00 GMT

Unlocking the Power of Taipy: Building Elegant Data-Driven Web Apps with Ease In an era where data-driven decisions drive businesses forward, effective communication of insights is paramount. Discover how Taipy, an open-source tool, revolutionizes the creation of data-driven web apps with minimal code. Say goodbye to complex development processes and hello to simplicity and elegance. Learn how to harness Taipy’s augmented markdown format to effortlessly add interactive elements like sliders, charts, and data tables to your web apps. Whether you’re a seasoned developer or new to coding, Taipy empowers you to craft sophisticated web applications in just a few lines of Python. Dive into the world of Taipy and streamline your data-driven projects like never before! Read the full article to embark on a journey toward building elegant web apps with Taipy GUI. Let’s begin 🚀! In this article Challenges in Building Data-Driven Web Apps Introduction to Taipy Creating Web Apps with Taipy Adding Visual Elements Conclusion Read it on Medium

Build Large Data Pipelines Using Taipy

Avi Chawla — Tue, 29 Aug 2023 17:03:00 GMT

Building reliant, scalable, efficient and production-ready data pipelines is vital to many modern businesses today to manage and visualize data effectively and make data-driven business decisions. What’s more, teams are often required to integrate the data pipeline with a full-fledged GUI application that offers a seamless interaction. But with increasing data, they are often faced with the challenge of efficient processing and analyzing it in existing data pipelines, resulting in: Issues with increasing data Increased run-time Inefficient use of resources Difficulty in scaling, Laggy interface, and many more. Typically, these problems arise due to the lack of an efficient pipeline orchestration tool — one that can manage the execution of functions and pipelines effectively by: Optimizing pipeline performance through parallel processing and efficient resource allocation. Providing easy management of multiple pipelines and their dependencies Ensuring the correct order of the pipeline tasks executions and more. To this end, Taipy is an open-source tool that streamlines the creation, management, and execution of reliable data-driven pipelines with low code. Thus, in this article, I will demonstrate how you can utilize Taipy to create a complex and interactive data pipeline. Let’s begin 🚀! Read it on Medium

Taipy's Success at PyCon Korea 2023 with Partner KSTEC

Rym Michaut — Fri, 18 Aug 2023 13:10:00 GMT

We’re excited to unveil the latest developments from PyCon Korea 2023, where Taipy.io, the innovative open-source Python library designed for rapid web application development, took center stage alongside its esteemed partner, KSTEC. Against the backdrop of this annual event, which unfolded from August 11th to 13th at COEX in Samseong-dong, Seoul, an exceptional platform materialized, uniting Python enthusiasts, developers, and data scientists to delve into the ever-evolving landscape of technological advancements. KSTEC: Empowering Data Science Journeys KSTEC, our esteemed partner and domestic distributor of Taipy, has been a trailblazer in the fields of mathematical optimization and business rule consulting since 1998. With a focus on AI projects and a dedicated big data team composed of skilled data scientists and developers, KSTEC is at the forefront of innovative solutions in the era of data economy. Their expertise spans software computer science, mathematical statistics, statistical analysis, and machine learning, ensuring that they are well equipped to support your data science endeavors. The Power of Partnership KSTEC’s partnership with Taipy brings together the strengths of two innovative entities. Taipy, a French artificial intelligence platform company, boasts Vincent Gosselin as its founder, a seasoned expert with over 30 years of experience in the AI field. Albert Antoine, Co-founder and Director of Taipy, Vincent Gosselin, the CEO and Co-founder of Taipy, were both present at the exhibition booth to introduce the platform personally. Showcasing Taipy’s Potential at PyCon Korea 2023 Visitors to the KSTEC / Taipy booth at PyCon Korea were treated to firsthand demonstrations of Taipy’s capabilities. The intuitive nature of Taipy empowers Python developers and data scientists to effortlessly build web applications, harnessing the power of AI without the complexity. This event provided a unique opportunity for attendees to interact with the minds behind Taipy and gain insights into its functionality. Looking Ahead The success of Taipy at PyCon Korea 2023 underscores the dedication of both Taipy and KSTEC to driving innovation in the Python community. As we forge ahead, we remain committed to empowering developers, data scientists, and businesses with streamlined solutions that pave the way for a future fueled by AI and data-driven insights. We extend our heartfelt gratitude to all those who visited our booth, engaged in insightful conversations, and expressed their enthusiasm for Taipy and KSTEC. Together, we are shaping the future of technology, one innovative step at a time. For more information about Taipy and our partnership with KSTEC, feel free to reach out to us at contact@taipy.io. Stay tuned for more updates, tutorials, and news from the Taipy community!

Taipy GUI – Some quirks with a neat Python GUI library

Gabriel Uri — Thu, 10 Aug 2023 17:11:00 GMT

Recently I have taken on the task of creating an application that required a GUI. Now that is an horror story of its own for someone like me, who knows the basic of Front End development but hates it and wishes it was easier. I have used python GUI libraries in the past, like Remi, PyQt, Tkinter, but what I always wanted was something as simple as I WANT THIS THING HERE THIS THING SHOWS THIS AND DOES THIS ‍ Enter Taipy, which I also recently found out that it’s a very recent library so there’s almost NOTHING about it on the internet, and it’s a great tool. That’s why I decided to write about it. I am not going to write a tutorial on how to use it, their website has a decent documentation on how to get started. I will be documenting the difficulties and little quirks that I had to figure out about Taipy when applying it to a bigger project. In this article Your code is always exposed if you don’t do something about it Live updating elements Threading Variable scope – an incomplete guide Read it in Dev.to

Taipy to Showcase at PyCon Korea 2023!

Rym Michaut — Wed, 09 Aug 2023 13:13:00 GMT

We are thrilled to announce that Taipy, the innovative web application builder for Python developers and data scientists, will participate in PyCon Korea 2023, scheduled from August 11th to 13th at the prestigious COEX Grand Ballroom & ASEM Ballroom. This event, a celebration of the Python programming language, is a fantastic opportunity for us to connect with the vibrant Korean Python community. Taipy has joined forces with KSTEC, a leading smart software solution company, to bring our cutting-edge AI application builder to the forefront of the PyCon Korea experience. This collaboration exemplifies our shared commitment to advancing the Python ecosystem and empowering developers and data scientists to create powerful AI applications effortlessly. Meet the Visionaries Behind Taipy: Vincent Gosselin and Albert Antoine Our booth will feature none other than Vincent Gosselin, the CEO and Co-founder of Taipy, and Albert Antoine, Co-founder and Director of Taipy. These visionaries have led our journey in redefining how web applications are built, making them accessible and user-friendly for the Python community. Unlock the Potential of Python with Taipy At the heart of our participation is the introduction of Taipy, a simple yet powerful web application builder designed to cater to the diverse needs of Python enthusiasts. Whether you’re a seasoned Python developer or a budding data scientist, Taipy empowers you to easily harness the power of AI and machine learning to create impactful web applications. From machine learning to optimization, Taipy has you covered. Partnering for Progress: KSTEC’s Role KSTEC, a Korean smart software solution company, is proudly participating in PyCon Korea for the second consecutive year. As our domestic reselling partner, KSTEC has played a pivotal role in bringing Taipy to the Korean market, contributing to the growth of the domestic Python landscape. We’re honored to stand alongside KSTEC in this journey of advancing Python and artificial intelligence. Revitalizing the Python Landscape Oh Bok-soo, the Technical Director of KSTEC’s Data Science & Analytics Division, envisions a revitalized domestic Python market through the partnership between KSTEC and Taipy. By providing the ‘Taipy’ solution, we aim to empower businesses with a user-friendly AI application builder, fostering innovation and growth. Join Us at PyCon Korea 2023 PyCon Korea 2023 promises to be an event filled with knowledge-sharing, networking, and collaboration. We invite you to visit our booth at the COEX Grand Ballroom & ASEM Ballroom to experience firsthand the potential of Taipy. Meet the minds behind the innovation and explore AI and Python’s possibilities for your projects. As we gear up for PyCon Korea 2023, the Taipy team is eagerly awaiting the opportunity to connect with you, share insights, and celebrate the Python community’s achievements. Together with KSTEC, we’re excited to make this event memorable, leaving a lasting impact on the Python landscape. Stay tuned for updates, exciting announcements, and more as we approach PyCon Korea 2023! Don’t miss the chance to be a part of this remarkable journey. 📅 Event Details: Date: August 11th – 13th, 2023 Location: COEX Grand Ballroom & ASEM Ballroom See you at PyCon Korea 2023!

Python Hackathon Season 2024: Taipy and MLH

Rym Michaut — Fri, 04 Aug 2023 13:15:00 GMT

Welcome to the Python Hackathon season 2024, where developers from around the world come together to showcase their skills, collaborate, and bring groundbreaking ideas to life! This season, Major League Hacking (MLH) and Taipy, the open-source Python library, have joined forces to empower participants with cutting-edge technology and create an unparalleled hackathon experience. Python Hackathon: Fostering Innovation and Collaboration Hackathons have become a hotbed of innovation, where programmers, designers, and tech enthusiasts collaborate intensively to build projects within a limited timeframe. The Python Hackathon season 2024, organized by MLH, is a testament to the growing popularity of Python and its immense potential for developers. Taipy: Empowering Developers to the Fullest Taipy is an easy-to-use open-source Python library that revolutionizes the development of full-stack web applications. With Taipy, Python developers can build interactive graphical user interfaces and back them up with data-driven functionalities. This powerful library is just a “pip install taipy” away, reducing development time and complexity. The Best Use of Taipy Challenge: Win Big! The excitement doesn’t end there. MLH and Taipy present the “Best Use of Taipy” challenge as part of this collaboration. Participants are encouraged to leverage Taipy in their hackathon projects to stand a chance to win a set of JBL Wireless Headphones for each team member. Get ready to take your projects to new heights with Taipy! Experience the Ultimate Hackathon Journey The Python hackathon season 2024 is all about empowering developers to take their Python projects to the next level. MLH and Taipy provide the tools, resources, and inspiration needed to embark on an unforgettable hackathon journey. Unlock Your Creativity: Python Hackathon with Taipy With Taipy at your disposal, you can unlock your creativity and build dynamic web applications seamlessly. The simplicity of Taipy allows you to focus on your ideas, while its powerful capabilities breathe life into your projects. Join the Python Hackathon Season 2024 Today! Are you ready to be a part of the Python hackathon season 2024? Don’t miss out on this unique opportunity to collaborate, innovate, and create with Taipy and MLH. Register for the upcoming hackathons and take your Python skills to new heights. The Python hackathon season 2024 promises to be an exhilarating journey for developers worldwide. Powered by Taipy and organized by Major League Hacking, these hackathons present a platform to showcase your Python prowess, collaborate with like-minded individuals, and make a lasting impact with your innovative projects. Join the Python hackathon season today and embrace the possibilities with Taipy and MLH!

Taipy 2.4: A Leap Forward in Building DS Application

Rym Michaut — Tue, 01 Aug 2023 13:16:00 GMT

Dear users & community members, We are thrilled to announce the release of Taipy 2.4, the latest version of our open-source Python library that empowers you to build user-friendly and production-ready data science applications. This new small release comes packed with exciting features and improvements across our core components, taipy-config, taipy-gui, taipy-core, and taipy-rest, providing you with even greater control and ease in developing your applications. What is Taipy? Taipy is your go-to resource for building both front-end and back-end components of data science applications. Whether you are a data scientist, a Python developer, or an analyst, Taipy offers a 100% Python-based library that streamlines your coding projects and revolutionizes your data science workflows. It is designed to reduce development and deployment time drastically, making it the ideal choice for data-driven applications. Building Interactive Dashboards with Ease Taipy component accelerates the creation of interactive and customizable multiple-page dashboards with augmented Markdown. With this component, you can build highly interactive interfaces without any prior knowledge of web development (CSS or HTML). It provides a range of features, including multi-user sessions, code autocompletion, API extensions for graphical components from third-party libraries, and large data visualization capabilities. Moreover, Taipy is fully compatible with Notebooks, making it the ultimate web application builder for your data science projects. Empowering Back-End Applications Taipy offers intuitive components to organize and manage data through pipelines and data flow orchestration. One of the standout functionalities of Taipy is the Scenario Management, allowing data scientists and end-users to perform “what-if” analysis, making it a powerful tool for decision-making and impact analysis processes. New Features in Taipy 2.4 Let’s dive into the exciting features and improvements that Taipy 2.4 brings to the table: Scenario Selector Control Enhancement In Taipy 2.4, the scenario_selector control has a new property called on_creation, which gives developers full control of scenario creation in response to an end-user pressing the “Create” button. This enhancement adds flexibility to the scenario creation process, empowering developers to customize it to meet their specific needs. Activity Spinner CSS Styling Taipy 2.4 introduces a change to the activity spinner, which now relies on the CSS class taipy-busy. This update allows you to easily style the activity spinner, giving it a more appealing and coherent look with the rest of your application. Shift+Enter Support for Multi-Line Input Control The multi-line input control in Taipy GUI now accepts Shift+Enter to insert a line break. This simple yet significant improvement enhances the user experience when working with multi-line inputs, improving overall usability. Date Picker for Table Date Value Edition In Taipy GUI, when editing a date value within tables, Taipy 2.4 prompts the user with a date picker. This enhancement simplifies the process of selecting and modifying dates, making it more user-friendly and error-resistant. Support for Modin 0.23 and Pandas 2.0 Taipy Core now includes support for Modin 0.23, which allows you to leverage its advanced features and optimizations. Additionally, Taipy 2.4 supports Pandas 2.0, offering you the latest functionalities of this popular library. It’s essential to note that Taipy 2.4 drops support for Pandas versions earlier than 2.0. ⚠️ If you require compatibility with Pandas 1.5, you can continue using Taipy 2.3. Conclusion Taipy is a game-changer for developers, data scientists, and analysts. And now, with its enhanced Scenario Selector Control, improved Activity Spinner Styling, and added support for Modin 0.23 and Pandas 2.0, Taipy is better equipped than ever to streamline your coding projects and data science workflows. Whether you are building interactive dashboards with Taipy GUI or managing and orchestrating data with Taipy Core, this new release offers unprecedented flexibility and ease of use. Embrace the power of Taipy 2.4, and join us in revolutionizing data science application development. Get started with Taipy 2.4 today! Visit our GitHub repository at https://lnkd.in/dq8JaM45 and explore the possibilities. We are excited to see the innovative applications you create with Taipy! Find more information in the release notes on our website. You can check all the new features, improvements and changes as well as the fixed bugs here. Have great fun developing pilots and projects with Taipy, and stay tuned for upcoming updates! And thank you for inspiring us with your feedback, enthusiasm, and creativity. Please keep on being proud of your web applications and share them with us. And don’t forget to keep sending us comments, ideas, bugs, feature requests, articles, or even words of encouragement on our GitHub repository. ‍

Enhancing Data Viz with Taipy GUI and Markdown

Dr Shouke Wei — Tue, 18 Jul 2023 16:32:00 GMT

Introduction In this tutorial, we will only touch the topic of charts, an essential component of Taipy GUI. Taipy GUI and Markdown After this tutorial, you will have a good understanding of how to create various types of charts using Taipy GUI and Markdown. The tutorial will walk you through the process of setting up Taipy GUI, loading data, and using it to create different types of charts, such as line charts, bar charts, pie charts, scatter plots, and more. You will learn how to customize the appearance of these charts by adjusting their colors, labels, and other properties. ‍ Read it on Medium

Taipy Discord Server: Six Reasons to Join Taipy !

Rym Michaut — Tue, 11 Jul 2023 13:20:00 GMT

Taipy now has its own Discord server: We are thrilled to announce the launch of the official Taipy Discord server, a dedicated space where Python enthusiasts, Python web developers, data scientists, and users of the Taipy library can come together, collaborate, and engage in meaningful discussions. Why join Taipy on Discord Join the Taipy Discord server to: Connect with Like-minded Individuals: Engage with fellow developers, learners, and experts who share a passion for Python development and building web applications using Taipy. Get Help and Support: Seek assistance, ask questions, and receive prompt support from the community regarding any queries you may have about using Taipy or web development in general. Showcase Your Projects: Show off your Taipy-powered projects, demos, and success stories. Share your code snippets, demonstrate your applications, and receive feedback from the community. Learn and Share Resources: Discover tutorials, guides, and resources related to Taipy and web application development. Share your own tutorials or provide links to helpful external resources. Collaborate and Network: Find opportunities to collaborate on projects, form study groups, and connect with potential co-developers or mentors within the Taipy community. Stay Updated: Get the latest news, announcements, and updates about the Taipy library, including new features, enhancements, and upcoming events. That you can also find in our Release Notes Section. How to join Taipy’s Discord server - To join the Taipy Discord server, simply follow these steps: - Create a Discord account (if you don’t have one already) at Discord. - Click on the following invitation link : Join our Discord Once you’re in, introduce yourself, explore the different channels, and start engaging with the community! We are also thrilled to welcome influencers from LinkedIn and YouTube who have been actively involved in the Taipy community. Their presence will enrich our discussions and provide valuable insights. Remember to adhere to our Community Guidelines, which promote a respectful and inclusive environment for everyone. Join the Taipy Discord server today and become a part of the vibrant Taipy community. Let’s learn, collaborate, and build amazing web applications together! See you there! PS: you can also join our GitHub community. ‍

Taipy: a Tool for Building User-Friendly Production-Ready Data Scientists Applications

Zoumana Keita — Thu, 06 Jul 2023 16:19:00 GMT

A simple, quick, and efficient way to build a full-stack data application This article explains how to build user-friendly production-ready data scientists applications. As a Data Scientist, you might want to create dashboards for data visualization, visualize data and even implement business applications to assist stakeholders in making actionable decisions. Multiple tools and technology can be used to perform those tasks, whether open-source or proprietary software. However, these might not be ideal for the following reasons: Some of the open-source technologies require a steep learning curve and hiring individuals with the appropriate expertise. Consequently, organizations may face an increased onboarding time for new employees, higher training costs, and potential challenges in finding qualified candidates. Other open-source solutions are great for prototypes but will not scale to a production-ready application Similarly, proprietary tools also come with their own challenges, including higher licensing costs, limited customization, and difficulty for businesses to switch to other solutions. Wouldn’t it be nice if there was a tool that is not only open-source but also easy to learn and able to scale into a full application? That’s where Taipy comes in handy 🎉 This article will explain what Taipy is, along with some business cases that it can solve before exploring its key features. Furthermore, it will illustrate all the steps to create a full web application. In this article What is Taipy and why should you care? Key Features of Taipy Getting started with Taipy Time to create a Taipy Dashboard from Scratch Taipy Back-end in Action Conclusion ‍ Read it on Medium

Taipy 2.3: creating business objects

Rym Michaut — Mon, 26 Jun 2023 13:23:00 GMT

Dear users & community members, We are pleased to announce the release of Taipy 2.3 Community Edition aired on June the 16th, 2023. This is Taipy Community’s sixth release. And the main features, improvements, changes and fixed bugs concern Taipy Frontend and Taipy Backend and how they interact together with new business objects that connect both components. At Taipy, our main objective is to help with a wide range of Python developments: from simple pilots with dashboards to complex Python applications, multi-user, highly interactive, embedding AI models, etc. What is Taipy? We launched Taipy in 2022 with a crucial idea: creating powerful Python web applications as quickly as possible. Taipy is an open-source Python library for building your applications’ frontend and backend. On the one hand, it provides a simple syntax that helps accelerate the process of creating interactive and customizable multiple-page dashboards with augmented Markdown. This web application builder generates highly interactive interfaces without requiring any knowledge of web development. And at the same time, Taipy is designed to build powerful and customized data-driven back-end applications. It provides intuitive components to organize and manage data through pipelines and data flow orchestration. Taipy also provides a unique functionality: Scenario Management enabling data scientists and end-users to perform “what-if” analysis. Taipy has been designed to reduce drastically the development and deployment time. Why Taipy? The reasons for using Taipy : It fastens the creation of an interactive and customizable web applications. It provides a simple code syntax with augmented Markdown It generates highly interactive interfaces without requiring any knowledge of web development (CSS or HTML) It make data analysts more autonomous in the development of their complete web applications It provides a unique functionality: Scenario Management enabling data scientists and end-users to perform what-if analysis. It manages data sources and monitors KPIs. With Taipy, you can: Develop using your favorite IDE, Benefit from multi-user sessions, Program with code autocompletion, Bring graphical components from third-party libraries with our API extension, Deal with large data visualization. Your only web application builder fully compatible with Notebooks. Build your pipelines graphically in no time with Taipy Studio, Bring pipelines’ orchestration to the next level with : Data nodes, Data scoping, Skippable tasks, and scenarios, Compare scenarios and track their performance over time without effort, Surprisingly, pipeline versioning didn’t exist so far! Well, it does now with Taipy ‍ And we wanted to improve these capabilities by making it a way better and connect both components Taipy 2.3.0 New Features Core Back-end Controls: Now there are more reasons to use them together! Thanks to the new visual elements that are meant to simplify the use of the entities created with Taipy Core. These controls let users list the entities, create new ones, edit or delete them. These controls are cold : Core Back-end Controls. They connect to entities created by Taipy Core. Your application can then visualize the Core entities and interact with them. These elements are: ✅ Scenario selector A GUI object that displays a list of created scenarios with the ability to create new ones. Provides the option to select scenarios. ✅ Scenario viewer A GUI object that allows inspection, modification, and deletion of certain parameters that define a scenario entity, including the ability to delete the entire scenario itself. ✅ Scenario DAG (Directed Acyclic Graph) A GUI object that displays the execution diagram of the scenario and is intended for data analysts to visualize the structure of the scenario. ✅ Data Node Selector A GUI object: Displays a list of created data nodes. Please check more details in the list of Core back-end controls. Taipy CLI (Command-Line Interface): Besides, Taipy has a new command-line interface (CLI) where several commands are available. For example : Get Taipy version: You can check your current version of Taipy by running the taipy –version command in a terminal (Linux, macOS) or command prompt (Windows). Manage versions: The taipy manage-versions command allows a Taipy user to track and manage various versions of a Taipy Core application. Please refer to the version management documentation page for more information on creating or re-using a version. Create a Taipy application: Taipy provides a comfortable environment for getting started with Taipy via the create command, and is the best way to start building a new application with Taipy. Please refer to the user manual for more details. Taipy Template : Finally, we wanted to simplify the creation of a Taipy application by offering a template. Users can now create a new Taipy application from a template by running $ taipy create from the CLI. Besides the default template, “multi-page-gui” template can be chosen with the optional –template option. Find more information in the release notes on our website. You can check all the new features, improvements and changes as well as the fixed bugs here. Have great fun developing pilots and projects with Taipy, and stay tuned for upcoming updates! And thank you for inspiring us with your feedback, enthusiasm, and creativity. Please keep on being proud of your web applications and share them with us. And don’t forget to keep sending us comments, ideas, bugs, feature requests, articles, or even words of encouragement on our GitHub repository. Read the release notes

Pr Ngo Bao Chau, Senior Advisor to Taipy's Board

Vincent Gosselin — Sat, 24 Jun 2023 13:19:00 GMT

We proudly announce that Professor Ngo Bao Chau, the esteemed Vietnamese-French mathematician, has joined Taipy.io as a Senior Scientific Advisor to the Board. Professor Ngo Bao Chau – A Brief Biography Let’s take a moment to learn more about Professor Ngo Bao Chau‘s remarkable background. He is best known for his groundbreaking work in mathematics, particularly for proving the fundamental lemma for automorphic forms, a problem proposed by Robert Langlands and Diana Shelstad. This achievement earned him the prestigious Fields Medal, making him the first Vietnamese national to receive this honor. Distinguished Career Achievements Professor Chau’s career has been filled with outstanding accomplishments. He became a professor at Paris-Sud 11 University and, at the young age of 33, was appointed as a professor in Vietnam, becoming the country’s youngest-ever professor. He has been associated with various esteemed institutions, including the Institute for Advanced Study in Princeton, New Jersey, and the Hanoi Institute of Mathematics. On September 1, 2010, he joined the mathematics faculty at the University of Chicago, where he continues to contribute significantly to the field. Lately, he had been elected to Honorary Membership of the LONDON MATHEMATICAL SOCIETY in 2021. He has made one of the most important contributions to the theory of automorphic forms in the last few decades and was awarded the Fields Medal by the International Mathematical Union in 2010. Contributions to Mathematics Education in Vietnam Not only is he an exceptional mathematician, but Professor Chau has also made notable contributions to the advancement of mathematics education and research in Vietnam. He became the Scientific Director of the Vietnam Institute for Advanced Study in Mathematics (VIASM) in 2011, furthering the development of mathematical knowledge and expertise in the region. Accolades and Honors Throughout his career, Professor Chau has received numerous accolades, including the Clay Research Award with Gérard Laumon in 2004 for their achievement in solving the fundamental lemma for unitary groups. Time magazine recognized his work as one of the Top Ten Scientific Discoveries of 2009, and in 2010, he was awarded the prestigious Fields Medal. Beyond Mathematics – A Children’s Book Author Beyond his mathematical prowess, Professor Chau has also authored the Vietnamese children’s book “Ai and Ky in the land of the invisible numbers,” showcasing his dedication to inspiring young minds about the wonders of mathematics. Welcoming Professor Ngo Bao Chau to Taipy We are truly honored and privileged to have Professor Ngo Bao Chau as our Senior Scientific Advisor to the Board at Taipy.io. His expertise and guidance will undoubtedly contribute to the growth and success of our platform. Join the Celebration! Please join us in giving Professor Chau a warm welcome to the Taipy community. If you have any questions or want to express your thoughts, feel free to share them with our community on LinkedIn. Thank you for being part of our thriving community, and let’s continue to learn and explore together!

Innovation Show 2023, Crédit Agricole:Taipy was in

Rym Michaut — Thu, 22 Jun 2023 13:25:00 GMT

Taipy, the open-source Python library that helps you build production-ready web applications in no time, will be participating in the prestigious Innovation Show by Crédit Agricole. This groundbreaking event is set to take place on June 27th, 28th, and 29th, 2023, in the SQY Park, Guyancourt, FRANCE. The Innovation Show by Crédit Agricole The Innovation Show by Crédit Agricole is an exceptional platform that fosters collaboration and drives technological advancements. It serves as a catalyst for groundbreaking ideas, disruptive technologies, and transformative solutions that have the potential to reshape industries and drive positive change in society. The Innovation Show provides an exceptional opportunity to witness firsthand the latest trends, breakthroughs, and cutting-edge innovations in the fields of artificial intelligence, data analytics, and more, as it will bring together some of the brightest minds and most innovative companies in the industry. Moreover, the Innovation Show offers a unique networking environment where participants can connect with industry leaders, technology experts, and potential collaborators. The event will host various workshops, panel discussions, and keynote speeches from visionaries who are being at the forefront of the AI and data analytics revolution. What is Taipy doing at the Innovation Show During the prestigious Innovation Show by Crédit Agricole, Taipy will showcase its revolutionary library and closely collaborate with IBM to create an AI application in less than a week. In this innovative endeavor, Taipy partners with IBM to accelerate the value of AI and put algorithms into the hands of users. Python developers and data sientists can easily and rapidly create AI applications through this collaboration, thanks to Taipy’s collaboration with IBM. This collaborative approach will enable participants to witness how Taipy and IBM leverage their respective expertise to deliver advanced AI solutions in record time. By joining forces, Taipy and IBM provide a powerful and user-friendly platform that facilitates the creation of AI applications, opening up new possibilities and opportunities for developers and users alike. During the Innovation Show, Taipy and IBM will demonstrate how this collaboration pushes the boundaries of innovation and makes AI accessible to a wider audience. Participants will have a unique opportunity to witness firsthand how an AI application can be created in a remarkably short timeframe, utilizing the tools and resources made available by Taipy and IBM. By leveraging the strengths of both companies, this collaboration aims to empower developers, streamline the AI development process, and enable the rapid deployment of intelligent applications. Taipy and IBM are excited to showcase the results of their partnership and inspire the audience with the potential of AI-driven solutions. See you there! This year’s edition of the Innovation Show promises to be bigger and better than ever before. With an impressive lineup of speakers, interactive exhibits, and immersive experiences, it is poised to inspire, educate, and empower attendees from various sectors. Whether you are a business leader, an entrepreneur, a tech enthusiast, or a curious individual eager to explore the frontiers of innovation, this event is not to be missed. We would like to express our gratitude to Crédit Agricole for organizing this exceptional event and providing a platform for companies like Taipy to showcase their groundbreaking solutions. We are honored to be a part of this esteemed gathering of innovators and change-makers. Don’t miss out on this incredible opportunity to connect with Taipy and explore the future of AI and data analytics. Mark your calendars for June 2023, the 27th, 28th, and 29th, and join us at the Innovation Show by Crédit Agricole. Together, let’s shape the future of the Python community!

We met at PyData London

Michelle Conway — Fri, 16 Jun 2023 16:14:00 GMT

We met Michelle Conway at PyData London and she now features Taipy in her curration of interesting Python packages.Thank you, Michelle. Enjoy using Taipy! Read it on LinkedIn

Webinar: Introducing Taipy 2.3

Rym Michaut — Mon, 05 Jun 2023 15:41:00 GMT

Save the date! Dear community, on June 29th, we unveiled the game-changing Taipy 2.3 release and introduced its exciting new features. Discover how Taipy can boost your productivity and elevate your Python web application projects! In this engaging session, our experts demonstrated the power of Taipy 2.3, showcasing its intuitive interface, enhanced collaboration tools, and seamless integrations. Gain valuable insights on optimizing your work processes and harnessing the full potential of Taipy. Imagine having it all at your fingertips: ✅𝗖𝗟𝗜 (𝗖𝗼𝗺𝗺𝗮𝗻𝗱 𝗟𝗶𝗻𝗲 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲) ✅𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗦𝗲𝗹𝗲𝗰𝘁𝗼𝗿, ✅𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗩𝗶𝗲𝘄𝗲𝗿, ✅𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗗𝗔𝗚 (𝗗𝗶𝗿𝗲𝗰𝘁𝗲𝗱 𝗔𝗰𝘆𝗰𝗹𝗶𝗰 𝗚𝗿𝗮𝗽𝗵), ✅𝗗𝗮𝘁𝗮 𝗡𝗼𝗱𝗲 𝗦𝗲𝗹𝗲𝗰𝘁𝗼𝗿, ✅𝗝𝗼𝗯 𝗦𝗲𝗹𝗲𝗰𝘁𝗼𝗿, But that’s not all! We also gave you an exclusive sneak peek into the upcoming features of Taipy 3.0, ensuring you’re always ahead of the curve. Don’t miss this opportunity to be at the forefront of the latest advancements in project management. Our speakers were: 🤝 Vincent Gosselin, our CEO 🤝 Fabien LELAQUAIS, our CTO 🤝 Jean-Robin Medori, our CPO 🤝 Florian Jacta, our CSM

Empowering Python Applications from Jupyter Notebook with Taipy

Rym Michaut — Fri, 12 May 2023 13:09:00 GMT

Thank you for joining us to empower your Python applications from Jupyter Notebook with Taipy. We’re thrilled to recount our remarkable journey at the highly anticipated JupyterCon Paris 2023. This premier event unfolded at the picturesque Etablissement Public Palais de la Découverte et Cité des Sciences et de l’Industrie from May 10th to 12th. As the first and sole conference where we showcased Taipy’s prowess, it was an unparalleled experience for us. Why JupyterCon Paris was Pivotal for Taipy JupyterCon was more than just a conference; it was the ideal platform for Taipy. With its laser focus on the Jupyter world and notebook utilization, this event resonated perfectly with Taipy’s DNA. Our advanced development environment thrives within Jupyter, and this event was a golden chance to spotlight Taipy’s prowess and foster valuable connections within the data science community. Jupyter Notebook with Taipy Just as a reminder, the Jupyter Notebook is the original web application for creating and sharing computational documents. It offers a simple, streamlined, document-centric experience. Moreover, the versatility of the Jupyter platform is exemplified through its extensive language support. With over 40 programming languages at your disposal, including popular choices like Python, R, Julia, and Scala, you have the freedom to work in the language of your choice. Taipy is developed for the Python community, so we had to be part of it. That’s why you can develop your own Python application within Jupyter Notebook with Taipy. Sharing your insights is effortless with Jupyter’s collaborative features. Notebooks can be easily shared via email, Dropbox, GitHub, and the Jupyter Notebook Viewer, ensuring seamless collaboration with colleagues and peers. As Taipy was developed to help data scientists bring the algorithms into end-users hands, it was obvious for us to be part of this development environment. Experience the dynamism of interactive output firsthand. Your code can generate a wide range of interactive content, such as HTML, images, videos, LaTeX, and custom MIME types, adding depth and engagement to your projects. For those dealing with big data, Jupyter seamlessly integrates with powerful tools like Apache Spark. Leverage the capabilities of Python, R, and Scala to harness the potential of big data. Even when working with massive datasets, you can continue to explore and analyze your data using familiar libraries such as pandas, scikit-learn, ggplot2, and TensorFlow. Meet Our Dynamic Team at JupyterCon Our stellar team was at the forefront of our JupyterCon journey, and we want to thank them again: 🤝 Vincent Gosselin, CEO: The visionary driving Taipy’s innovation. 🤝 Marine Gosselin, Developer Advocate: Marine led a captivating workshop. 🤝 Alexandre Sajus, Community Success Manager: Alexandre ensured an enriching experience. 🤝 Fred Lefévère-Laoide, Senior Software Architect: Fred’s brilliance fuels Taipy’s GUI. 🤝 Jean-Robin Medori, CPO: Jean-Robin’s strategy shapes Taipy’s trajectory. Highlighting the Workshop 🔥 May 10th, 10:30 a.m. – Empowering Workshop: Marine and Vincent Gosselin’s insightful workshop, “Empowering Python Applications from Jupyter Notebook with Taipy,” took center stage. Attendees were immersed in the Taipy universe – a revolutionary low-code Python package. Taipy empowers data scientists to construct complete data science applications, combining exquisite graphical visualizations (Taipy GUI) with efficient algorithm, model, and pipeline management (Taipy Core). Attendees discovered the seamless synergy between these modules, enabling rapid and potent application development within Jupyter Notebooks. Networking and Engagement at Booth #49 Our presence extended beyond the workshop to Booth #49. Here, attendees delved deeper into Taipy’s capabilities while sipping coffee. Our R&D experts animatedly elucidated Taipy’s intricacies, showcasing its potential to redefine data science. This exchange sparked dynamic conversations and solidified connections that reverberated through the event. Envisioning the Future JupyterCon Paris 2023 was a watershed moment. The community’s fervor and curiosity were infectious, reinforcing our commitment to refining Taipy based on valuable insights. We extend heartfelt gratitude to our community and everyone who engaged with us at the conference. Anticipate more transformative updates, enhanced features, and collaborative ventures. As we shape the data science landscape, remember: Keep coding, ‍

Suicide Rate Analysis – interactive Dashboard in 2 hours

Chi Nguyen — Mon, 08 May 2023 16:47:00 GMT

Try Taipy with the simple augmented markdown syntax to streamline your coding process to build interactive visualizations in Python. Here it’s a Suicide Rate Analysis dashboard. The story continues After 2 previous parts introducing the Taipy package with: Part 1: Introducing a general idea of how Taipy GUI works and showing how different basic charts are created. Part 2: Applying more visual control elements Suicide rate analysis dashboard In this article, I will show you how to utilize what we know about Taipy GUI and take things to the next level. We’ll dig further into the data to find trends and insights that will help us comprehend this issue in a more detailed manner. Get ready to watch how Taipy GUI speeds up our finished dashboard! In this article The story continues… Purpose of This Dashboard Data Overview & Questions Visualization with Taipy A bit of introduction to Stylekit Global Analysis Relationship Analysis CSS Customization Key takeaways Last words Read it on Medium

Tutorial: Stock Portfolio

Cornellius Yudha Wijaya — Mon, 08 May 2023 13:42:00 GMT

Stock Portfolio dashboard Building applications has never been easier in this current era. With many open-source Python packages available, we can build applications from scratch with a minimum number of lines of code. Especially for data people, there are various open-source Python packages to build AI applications, including Taipy. To build a data application, sometimes we need to hassle our way into writing a long form of code to acquire a simple application. Using the open-source package Taipy, we can easily build both the GUI and back-end in a few lines of code. How to create a simple application? Let’s explore a little bit regarding Taipy with this stock portfolio tutorial example. In this article Building an application with Taipy Taipy GUI Taipy Core Combining Taipy GUI and Taipy Core Read it on Medium

arXiv, KeyBERT, and Taipy for Keyword Extraction

Kenneth Leung — Tue, 18 Apr 2023 16:53:00 GMT

Keyword Extraction and pipeline analysis In this captivating guide, Kenneth Leung walks us through the process of constructing a robust keyword extraction and analysis pipeline, complete with an engaging front-end user interface and a dynamic back-end using Taipy and the groundbreaking KeyBERT library. As the influx of textual data from diverse sources continues to surge, harnessing the power of NLP techniques like keyword extraction becomes imperative. Highlights 📚 Context: Dive into the context of rapidly evolving AI and machine learning research, and the role of arXiv as a leading platform for scientific papers. 🔍 Tools Overview: Gain insights into the three essential tools used in this project – arXiv API Python wrapper, KeyBERT, and Taipy. 🚀 Step-by-Step Guide: Follow a comprehensive walkthrough to build your keyword extraction and analysis pipeline. 🛠️ Backend and Frontend Integration: Explore the seamless integration of Taipy’s powerful backend capabilities with its intuitive frontend user interface. 📊 Visual Insights: Witness the creation of a sophisticated dashboard for visualizing and analyzing extracted keywords. How Can You Benefit? Researchers, data scientists, and AI enthusiasts can unlock the potential of advanced keyword extraction techniques to enhance their understanding of textual data. Developers can learn how to create robust pipelines that link the front end and back end, utilizing Taipy’s versatile features. Everyone interested in NLP and machine learning can grasp the intricacies of keyword analysis in an accessible and engaging manner. Ready to Dive In? For all the details, insights, and the complete walkthrough, head over to the original article on Towards Data Science. In this article Context Tools Overview Step-by-Step Guide Wrapping it up We’re thrilled to see the Taipy community expanding its horizons and embracing the power of keyword extraction for more insightful data analysis. Happy reading and exploring, fellow Taipy enthusiasts! Read it on Medium

Announcing Taipy 2.2

Rym Michaut — Fri, 14 Apr 2023 15:40:00 GMT

Dear users & community members, We are pleased to announce the release of Taipy 2.2 Community Edition aired on April 14th, 2023. This is Taipy Community’s fifth release. And the main features, improvements, changes and fixed bugs concern Taipy frontend. At Taipy, our main objective is to help with a wide range of Python developments: from simple pilots with dashboards to complex Python applications, multi-user, highly interactive, embedding AI models, etc. We launched Taipy in 2022 with a radical idea: creating powerful Python web applications as quickly as possible. That’s why Taipy was born. As one of the two components of Taipy, the frontend provides a simple and low-code syntax that helps accelerate the process of creating interactive and customizable multiple-page dashboards with augmented Markdown. This web application builder generates highly interactive interfaces without requiring any knowledge of web development. There are a lot of reasons for using Taipy : It fastens the creation of an interactive application. It easily and efficiently manages variables and events. Easy visualization with Markdown syntax. Taipy 2.2 New Features The Stylekit : A default set of stylesheets are installed with Taipy so that, by default, applications benefit from a homogeneous and good-looking style. The Stylekit can be easily customized to fit your application design’s requirements. Rebuild property : The table and chart controls have a new property that can be used if you need to entirely change the data they rely on, including their structure. Taipy 2.2 Improvements and changes The default property name for the part block was changed from render to class_name to allow for directly using the style classes from the Stylekit. Please check the section on Styled Sections for more information. The expandable block has a new property called on_change enabling to set a specific callback when the block is expanded or collapsed. Better error messages when parsing Markdown content. Better support for auto-completion in IDE for the Gui.run() configuration parameters, based on a generated Python Interface Definition file. The status entry point now provides information about the loaded element libraries and the elements they define. The navigate() function and the page property of the part block can now use, as their target, any URL. In the context of a part block, the page will be rendered in an iframe. Find more information in our release notes on our . Have great fun developing pilots and projects with Taipy, and stay tuned for upcoming updates! And thank you for inspiring us with your feedback, enthusiasm, and creativity. Please keep on being proud of your web applications and share them with us. And don’t forget to keep sending us comments, ideas, bugs, feature requests, articles, or even words of encouragement on our GitHub

How to Use Taipy and Taipy GUI in Python Applications

Eric Narro — Fri, 07 Apr 2023 13:19:00 GMT

Taipy is a Python application builder, an open-source (Apache 2.0 license) project that lets you create business applications such as dashboards to display your data, data pipelines, or AI apps. I discovered them during my time visiting the Pycon in Bordeaux this year (I wrote an article with my notes that I took about it), where they were sponsoring the event and presenting their product. Florian Jacta and Marine Gosselin did a great job talking about the project, and I came back home really wanting to test it. In this article, I want to give a general overview of Taipy (which I will explore in more depth over time). I will first present the project and give some context. Then, I will talk about Taipy’s GUI, one of its main components. I will not talk about Taipy Core here, the other of the two main parts of the project. In this article General Overview Resources Getting Started With Taipy Testing Taipy GUI Personal Thoughts About Taipy GUI Read it on BetterProgramming

Refresh Your Dashboard with Visual Control Elements

Chi Nguyen — Tue, 28 Feb 2023 14:11:00 GMT

Previously on Medium In the previous post, Simplify Your Process of Building Interactive Dashboards with Taipy, Chi introduced to a quick and straightforward way to build an interactive dashboard with Taipy. Now, the visual control elements Now, she will provide you with one of the features of Taipy GUI that makes your dashboard more interactive for end-users: Taipy GUI’s visual control elements. The visual elements are responsible for customizing the user interface or controlling objects that represent data. Similar to drawing charts, applying different visual elements in Taipy is very simple so that everyone can do it without much coding effort. In this article Data Visual Control Elements Toggle Slider Dropdown Input Layout Rea it on Python Plain English

How to create a movie recommendation with Taipy, in no time.

Rym Michaut — Wed, 22 Feb 2023 16:04:00 GMT

Last week, on February 16 to 19th, Taipy was proud to sponsor and participate in PyConFr 2023 in Bordeaux, France. What is PyCon Fr? PyCon Fr is an annual conference that takes place over four days, organized entirely by volunteers for the francophone Python community, bringing together individuals interested in the Python programming language. Create a movie recommendation in a few minutes Developers collaborated to advance and contribute to Taipy by integrating new features and building interactive demos, primarily using Taipy’s GUI. This session aimed to create a movie recommendation application on VS Code. ‍ Participants used a pre-defined template to search for likes, dislikes, and views movies and recommend other films based on their actions. Thanks to Jean Robin Medori (CPO of Taipy) and Florian Jacta (Taipy Customer Success Engineer), who led these sprints. ‍ On the following and last two days, Vincent Gosselin (CEO & Co-Founder of Taipy) and Florian Jacta held a 2.5-hour workshop and conference, where they introduced : Taipy Frontend, the fastest and easiest Python library currently available in the market. As well as Taipy Backend, the most complete pipeline modeling with scenario management, using the freshly released Taipy Studio, a graphical configuration builder. This last provides programmers with tools that significantly improve productivity when building Taipy applications. ‍ Subsequently, Florian showcased his coding skills by utilizing notebooks, which data scientists commonly employ. Not only does this approach enable step-by-step coding and facilitate experimentation with different techniques, but it also enhances accessibility. Florian provided a detailed walkthrough of the Covid Dashboard demo’s graphical interface creation process through the notebook. ‍ Both talks were well-attended, with over 40 attendees from various backgrounds, including academics, Python developers, data engineers, MLOps, data scientists, and consultants. We’re grateful for the chance to showcase Taipy to the Python community in France. Hats off to the organization team Association Francophone Python (AFPy), for pulling off a great event! ‍ Stay tuned for the conference videos! Find out which upcoming events Taipy will be participating in: 19-27 April 2023 PyCon US Salt Lake City | USA 26-28 April 2023 PyData Seattle | USA 9-11 May 2023 ODSC East Boston | USA 10-12 May 2023 JupyterCon Paris | France 2-4 June 2023 PyData London | UK 14-15 June 2023 ODSC London | UK 30 June 2023 Women In Data And AI Summer Festival | Germany

Taipy takes the stage at WAICF Cannes 2023

Rym Michaut — Mon, 20 Feb 2023 16:02:00 GMT

Taipy sponsored and participated in The WAIC (World AI Cannes Festival), the ultimate AI experience in Cannes, France. WAICF brought together top builders of game-changing AI strategies and use cases for an unparalleled experience in the future of Artificial Intelligence in the magnificent Palais des Festivals et des Congrès de Cannes ! This 2nd edition hosted 10K+ attendees with exceptional talks, showcases, performances, and networking events. Three days of valuable workshops, dynamic networking, and groundbreaking AI projects! ‍ Theo Demessance, Les Mousquetaires use case with Taipy On The first day, Taipy took part in a workshop. We delivered a 30-min startup pitch in our field: building stunning data science web applications in Python. This was followed by a demo by Théo Demessance, Data Scientist at Groupement Les Mousquetaires. He presented their CFM (Cash Flow Management) application, developed with Taipy library. Learn more about this use case by clicking on the button below. ‍ ‍ Both well-attended workshops attracted diverse people with a wide range of backgrounds, such as data scientists, consultants, IT Directors, and Pythonistas – all with a strong interest in Python and especially in our stack Taipy. ‍ Taipy Frontend is the fastest and easiest Python library currently available in the market, and Taipy Backend is the most complete pipeline modeling with scenario management. Both components help turn Data and AI algorithms into full web applications and stack them together to shape a one-of-a-kind solution. Thanks to the organizers and participants for this fantastic event! We’re grateful for the chance to showcase Taipy to the AI and Python communities in France. Bring on WAICF 2024; we are ready for more! ‍ View the demo

How to build a face detection application using Taipy and OpenCV

Grégoire Marabout-Démazure — Fri, 10 Feb 2023 14:04:00 GMT

In this tutorial, I will demonstrate how to use Taipy and OpenCV together to make a face detection app from a photo. At the end of the tutorial, you will have a single page web application where you can upload a photo. The photo will be displayed with rectangles around detected faces. The promise is to take less than 50 lines of code for it! In this article Presentation and intallation of Taipy and OpenCV Face Detection Haar cascade classifier Color conversion Taipy GUI Read it on Medium

Simplify Your Process of Building Interactive Dashboards with Taipy

Chi Nguyen — Fri, 03 Feb 2023 13:55:00 GMT

𝗔𝗿𝗲 𝘆𝗼𝘂 𝘁𝗶𝗿𝗲𝗱 𝗼𝗳 𝘀𝗽𝗲𝗻𝗱𝗶𝗻𝗴 𝗵𝗼𝘂𝗿𝘀 𝗰𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝘄𝗲𝗯 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀 𝗮𝗻𝗱 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 𝗳𝗿𝗼𝗺 𝘀𝗰𝗿𝗮𝘁𝗰𝗵? Look no further! Taipy is the ultimate solution for building stunning web interfaces and interactive dashboards in no time. 🚀 With Taipy, say goodbye to complexities and lengthy development cycles. Whether you’re a Python developer or a data scientist, Taipy empowers you to streamline the process of building web applications and effortlessly visualize your data. 💪 𝗪𝗵𝘆 𝗰𝗵𝗼𝗼𝘀𝗲 𝗧𝗮𝗶𝗽𝘆?🤔 1️ 𝗥𝗮𝗽𝗶𝗱 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 𝗖𝗿𝗲𝗮𝘁𝗶𝗼𝗻: Taipy offers a comprehensive set of pre-designed components and templates, allowing you to assemble beautiful interfaces with just a few lines of code. No need to reinvent the wheel! 2️ 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀: Taipy is equipped with powerful data visualization tools, making creating dynamic and interactive dashboards a breeze. From charts and graphs to maps and tables, you have everything you need to present your data in an engaging manner. 3️ 𝗣𝘆𝘁𝗵𝗼𝗻𝗶𝗰 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Built on Python, Taipy harnesses the strength of this popular programming language. Leverage your existing Python skills and libraries to integrate data analysis, machine learning, and other functionalities seamlessly. Ready to see Taipy in action? Check out this amazing article by Chi Nguyen (the initial article in a trilogy of three), where she walks you through the process of simplifying your dashboard building with Taipy (link in the first comment 👇 ) In this article, Chi demonstrates how Taipy empowers developers to save time and effort while creating interactive dashboards. It’s the first installment in a series of three articles that will provide valuable insights and practical tips on using Taipy to its full potential. Join the Taipy community today and experience a whole new level of web development and data visualization. 🌐📊 Don’t miss out on this game-changing tool! Get started with Taipy and revolutionize the way you build web interfaces and dashboards. 💡 In this article Data Visual elements Charts Generate a dashboard Read it on Python Plain English

Announcing Taipy 2.1!

Vincent Gosselin — Tue, 31 Jan 2023 14:40:00 GMT

We launched Taipy in 2022 with a radical idea: creating powerful Python web applications as quickly as possible. In just a few months since the last release, our users have delivered amazing software, from pilots to full-scale applications. Our strategy is to provide full coverage with Taipy. We want Taipy to help with a wide range of Python developments: from simple pilots with dashboards to complex Python applications, multi-user, highly interactive, embedding AI models, etc. The feedback has been fantastic, and we thank all our adopters in Europe, the US, and Asia. In this latest release, major features are on the menu. To emphasize our low-code philosophy, creating pipeline configurations with Taipy backend needed an interactive editor, which is now the case with Taipy Studio. Taipy Studio With Taipy Studio, you can create a complete configuration for your pipelines and scenarios graphically. Such configurations can be saved as a TOML file which can then be loaded in your Python code to create pipeline instances and execution runs. The previous Python method of creating your Pipeline DAGs is still available. It is still valid when facing dynamic pipelines (pipelines that can only be created programmatically). Scenario/Pipeline Versioning Indeed, there was a significant need for version management for Taipy pipeline/scenario configurations. An application is constantly evolving. Developers must maintain the configuration by modifying data sources, new tasks, etc. The new versioning system will naturally help manage this aspect from your command line. Here we first list existing versions, then execute a scenario/pipeline in production mode with a new version number. More on Taipy backend 2.1 Taipy has a new Parquet predefined Data node Taipy is now integrated with Modin Dataframes. Our Data Nodes can now expose Modin Dataframes. Taipy frontend 2.1 First, many more charts are at your disposal: Bubble charts, Heatmaps, Error chars, Polar & Radar charts, Financial charts, etc. Taipy Studio also brings a code auto-completion feature that is most helpful when writing Taipy frontend code. The Decimator: This is a neat graphical filter for large data sets. What does it do? Python charts are not made for large data sets. They usually crash or lead to cluttered graphics. The Decimator solves these issues. It removes the points that least modify the shape of the curve (for line charts) or the form of clusters (in the case of scatter plots), etc. Simply add a new property to your Taipy frontend charts to use it. ‍ Since Taipy is very community-driven, do not hesitate to propose new features on our community discussion board or, even better, submit an issue on our git repositories. Thank you for inspiring us with your feedback, enthusiasm, and creativity. Please keep sharing your applications. And keep sending us comments, ideas, bugs, feature requests, articles, and words of encouragement. Have great fun developing pilots and projects with Taipy, and stay tuned for upcoming updates!

Cash Flow Optimization using Taipy at Groupe les Mousquetaires

Theo Demessance — Wed, 25 Jan 2023 13:53:00 GMT

Groupe Les Mousquetaires, a leading European retail group, has deployed CFM, an AI-based Cash Flow Forecasting application. CFM (Cash Flow Management) was developed with Taipy. It has become a central application for the Financial Division: the treasurer can now achieve much better forecasts, taking into account much more variables but also performing scenario analysis. STIME (Groupe Les Mousquetaires’s IT Dpt) selected Taipy as its Python Development Framework as it allows for fast development, full-stack coverage (from GUI, Pipeline, and Scenario Management), and easy deployment. Implementing the CFM project with Taipy resulted in more active cash management, leading to gains by accurately identifying the best timings and amounts to invest (or borrow) and selecting the most suitable investment instrument. This project generated a high return on investment, which was further increased by rising inflation. In this video, Théo Demessance, one of the lead Data Scientists at Groupe Les Mousquetaires, explains how Taipy is used to create interactive dashboards and efficiently manage complex data flows.

Taipy was a Guest Speaker at EURO Practitioners' Forum webinar.

Rym Michaut — Mon, 16 Jan 2023 14:48:00 GMT

Last Friday, January 13th, as with every first Friday of the month, the EURO Practitioners’ Forum organized a webinar. This working group enables OR professionals to have more impact on the outside world by sharing the best practice. Taipy was invited to participate in this webinar. During the webinar, V. Gosselin and F. Jacta demonstrated how Taipy empowers data scientists and Python developers to create great production-ready applications for end-users easily. The event was well-attended, with over 30 attendees from various backgrounds, including academics, data scientists, consultants, and operational researchers. We greatly appreciated the opportunity to present and interact with the audience. We would like to extend a big thank you to the organizers: Sofiane Oussedik, Ruth Kaufman, Adisa Mujezinović, Joaquim Gromicho, and Gavin Bell. We are grateful for the chance to have Taipy used by the European Operations Research communities. P.S.: The recordings and details from previous webinars will soon be available.

The SMU School of Information Systems collaborates with Taipy.

Rym Michaut — Thu, 12 Jan 2023 14:52:00 GMT

At the beginning of this 2023 year, five Master’s students from the SMU School (Singapore Management University) started their capstone projects with Taipy. What is the SMU School? A premier university in Asia, the Singapore Management University (SMU) is internationally recognised for its world-class research and distinguished teaching. Established in 2000, SMU’s mission is to generate leading-edge research with global impact and produce broad-based, creative and entrepreneurial leaders for the knowledge-based economy. Home to around 10,000 undergraduates and postgraduates, SMU comprises eight schools: School of Accountancy, Lee Kong Chian School of Business, School of Economics, School of Computer and Information Systems, Yung Pung How School of Law, School of Social Sciences, College of Integrative Studies and College of Graduate Research Studies. SMU offers a wide range of bachelor’s, master’s and PhD degree programmes in the disciplinary areas associated with the six schools, as well as in interdisciplinary combinations of these areas. When Deep learning meets Taipy We would like to thank Prof. Ta Nguyen Binh Duong for this collaboration between the SMU School of Information Systems and Taipy. These Machine Learning projects will extend the capabilities of Taipy by providing NLP interfaces to query data frames and map the results into Taipy graphics. The students will: Design and build Deep Learning models, Create the Training and Scoring pipelines (using Taipy Core) and Connect them with Taipy’s powerful graphical interface. These innovative projects are developed using Python Open source eco-system: Pandas, Taipy, Tensorflow, and Keras. NVidia’s GPUs will be used for the training phase of the Deep Learning models. Besides improving their experience with Machine Learning techniques and NLP, the students will also contribute to the field of Generative AI since the projects are nothing short of a Chat-GPT-like interface to data frames. Taipy is thrilled to participate in such a collaboration and looking forward to showing the results soon!

Taipy at the High Tech Campus for PyData Eindhoven 2022

Rym Michaut — Thu, 08 Dec 2022 14:56:00 GMT

PyData High Tech Campus for PyData Eindhoven 2022 was back with an exciting in-person conference in collaboration with the High Tech Campus and the AI Innovation Center. It was a great conference filled with exciting talks on data science, machine learning, and best practices. Taipy, a Platinum sponsor, was happy to be part of it and once again received great feedback! This one-day conference held on the 2nd of December provided an opportunity to share knowledge, learn new approaches and emerging technologies, and get the latest in Analytics and Visualization. Attendees heard from some of the top minds in the Python data community and networked with like-minded individuals. It was really nice to see major industrial companies like ASML present their AI applications at the conference. The Taipy team was thrilled to welcome visitors to its booth and to introduce Taipy, our new open-source, low-code framework! Vincent Gosselin, CEO& co-Founder of Taipy presented in a 30 min talk on how a complete application (front-end/back-end) can be built in Python in no time. . He demonstrated how Taipy GUI combines low-code and powerful performance that lacks existing Python packages. On the backend side, he highlighted how Taipy Core combines MLOps, Data Science, and End-User scenarios. This live talk was packed with a great audience. The final 10 min Q&A session was a flurry of amazingly good questions and great feedback from our dutch audience. Thank you to the visitors and the organizers James Weiss from NunFOCUS and Gareth Thomas from VersionBay. See you next year for new PyData conferences in Europe and the USA!

Mark your calendars. Taipy is continuing its world tour in 2023.

Rym Michaut — Wed, 07 Dec 2022 14:59:00 GMT

Taipy will still participate as a sponsor in a series of Python, AI, Open-Source & Data Science conferences. Taipy is fully committed to participating in these conferences targeting the Python Developers, Data Scientists & Data Engineers community to share ideas and learn from each other. These conferences will be the chance to share insights and build new connections through networking events like Lightning Talks and Open Spaces. It will be a great opportunity present Taipy new releases and the 2023 roadmap! Exciting news are in the pipes. Get ready! Come to meet us, connect, and grow with data scientists, speakers, and AI practitioners virtually or in person. Join& follow us on this magical tour! #Taipyworldtour2023 Taipy World Tour 2023 Dates 9-11 Feb 2023 WAICF | France 16-19 Feb 2023 PyCon Fr Bordeaux | France 10-12 March 2023 PyData London | UK 15-19 April 2023 PyData Berlin | Germany 19-27 April 2023 PyCon US Salt Lake City | US 26-28 April 2023 PyData Seattle | USA 9-11 May 2023 ODSC East Boston | USA 10-12 May 2023 JupyterCon Paris | France 14-15 June 2023 ODSC London | UK Upcoming 14-16 September 2023 PyData Amsterdam| Netherlands 1-3 October 2023 PyData NYC| USA More to come in Fall 2023. We will keep you posted. Stay tuned and find our upcoming dates HERE! ‍

Taipy at 2022 INFORMS Annual Meeting!

Rym Michaut — Sat, 26 Nov 2022 15:02:00 GMT

Held each fall, the 2022 Institute for Operations Research and the Management Sciences (INFORMS) Annual Meeting brings over 6,500 people to the world’s largest operations research and analytics conference. It features more than 800 sessions, presentations, and opportunities to meet with leading companies, universities, and other exhibitors. This year, Taipy was present at the in-person 2022 INFORMS Annual Meeting at the Indiana Convention Center, Indianapolis Marriott Downtown, and the JW Marriott Indianapolis. Hundreds of cutting-edge presentations, including daily plenary and keynote sessions, were presented live and covered topics relating to the advancement of urban analytics. Technology Tutorials showcased the latest software developments revolutionizing fields, including optimization and machine learning. It was an excellent opportunity for our team to introduce Taipy to the Operations Research/Management Science community. Attendees were able to see the ease of development and rich feature set that Taipy GUI and Taipy Core provide for highly responsive web-based applications backed by robust pipelines and process flow orchestration. The visitors were impressed with how quickly a complete Python application (ranging from a pure data presentation dashboard to the most complex ML pipeline) can be developed and deployed. On Saturday, October 15th, Taipy did a 2.5-hour Technology Vendor Workshop with demonstrations highlighting the power of Taipy’s tool set. On Tuesday, October 18th, Taipy presented a 35-minute Technology tutorial showing the two components: Taipy GUI and Taipy Core, and demonstrating how these two components bridge the gap between data science and production-grade applications. Both were great successes, with excellent attendance and valuable feedback! Thank you to the INFORMS INDIANAPOLIS organizers and the many visitors who came to meet the Taipy teams! Check out where taipy.io will be next.Taipy World Tour Fall 2022 | Taipy

Taipy took a bite out of PyData New York City

Rym Michaut — Fri, 18 Nov 2022 15:00:00 GMT

PyData New York City was back in person after a two-year hiatus. Taipy was part of this very successful return as a platinum sponsor! The 2022 edition of PyData NYC was held at the Microsoft Conference Center, at 11 Times Square, from November 9 to November 11. The Taipy team was thrilled to welcome visitors to its booth and to introduce Taipy, our brand-new open-source, simple-code framework! We had great discussions with some main players in the world of open-source, such as the Python legend Travis Oliphant, Matt Harward from Open Teams, Adam Shroeder from Plotly, the folks at Anaconda, the Numfocus organizers, etc. It was great to meet people using Python in a wide range of contexts and Industries, although being in NYC, many visitors were from the Finance sector. On Tuesday, Taipy presented a 45-min Product Demo talk, “Turning Data/AI Algorithms into Production-ready Applications in no time with Taipy”. The talk attracted a large audience, resulting in great support and excellent feedback! Stay tuned; We will soon share the video of our Product Demo talk on our website and Youtube channel! Thank you to the PyData NYC organizers, NumFocus, and the many visitors who came to meet the Taipy team! See you at PyData Eindhoven on December 2nd! 👉To know more about Taipy World Tour Fall 2022 | Taipy #AI#ML #opensource #lowcode #scenariomanagement #predictions

Taipy at ODSC West 2022 San Francisco, the largest hybrid DS and ML conference

Rym Michaut — Tue, 08 Nov 2022 14:50:00 GMT

ODSC West 2022 at San Fransisco Open Data Science Conference, in its 2022 edition, returned to San Francisco, California, this November 1st-3rd online and in person. Taipy was thrilled to be part of this event’s focus on “Build AI Better.” Data scientists from around the globe gathered at ODSC West to discuss their challenges and learn about the hottest innovations in AI. The event brought about 2000 people together and offered more than 200 training sessions and workshops led by the best industry experts in data science. Attendees were able to choose from many sessions across multiple tracks: Deep Learning and Deep Reinforcement Learning Data Engineering and MLOps Cybersecurity and Machine Learning Responsible AI and AI for Social Good ML Hands-on Training AI Research Foundations. On Tuesday, Taipy presented a 25-min Product Demo talk, “Turning your Data/AI algorithms into full web applicationss in no time with Taipy”. On November 2nd Taipy gave a 75-min Workshop on how to build stunning Data Science Web applications in Python. Both talks attracted large audiences, and resulted in great support and excellent feedback! It was an excellent opportunity for our team to introduce Taipy’s low code development approach with its unique GUI and pipeline orchestration. The visitors were impressed with how quickly a complete Python application (ranging from a pure data presentation dashboard to the most complex ML pipeline) can be developed and deployed. Thank you to the ODSC West organizers and the many visitors who came to meet the Taipy teams! See you at PyData NYC on November 9-11!

Taipy at Canada's #1 Big Data and AI Conference and Expo.

Rym Michaut — Mon, 17 Oct 2022 15:37:00 GMT

As part of its Fall World conference tour, Taipy attended The Big Data and AI Show Toronto BDAI Toronto, last October 6th and 7th. It is Canada’s number 1 event dedicated exclusively to AI, Data Science, and its impact on the economy and our coders' daily lives. Over 150 sessions took place within the Metro Toronto Convention Center. Big Data and AI brought together a new generation of leading AI players (Databricks, Dataiku, DataRobot, Snowflake), established companies such as Microsoft.), not to mention a whole host of pioneering start-ups. ‍ Taipy teams had a blast talking and connecting with Pythonistas, Data Scientists, Architects, and Heads of Data Science groups across the industry at #BDAIT Toronto. ‍ We were excited to demonstrate Taipy’s groundbreaking capabilities combining simplicity to code with powerful GUI and Backend frameworks. The visitors were impressed with how quickly a complete Python application (ranging from a pure data presentation dashboard to the most complex ML pipelines) can be developed & deployed in a short time. ‍ Taipy was one of the busiest booths for our first appearance at the conference, and we got great feedback from a constant flow of visitors showing high interest in the product. On Thursday, Taipy did a 30min product tutorial, followed on Friday by the presentation of a customer use case (a leader in the fast food industry). Both have been great successes with excellent attendance! Thank you to the BDAI Toronto organizers, and the many visitors who came to meet us!

Taipy. Important Dates for Big Data conferences.

Rym Michaut — Thu, 29 Sep 2022 14:04:00 GMT

Taipy pursues its Big Data Conferences through its Falls World Tour: from California to France… Big Data Conference in California Taipy was in California at NUMFOCUS Project Summit, September 21 – 23 in Santa Rosa, California. The Summit brought Python developers together to strengthen the NumFOCUS open-source project network. As a NumFOCUS sponsor, Taipy has been invited to participate in group discussions where open-source strategies and opportunities for collaboration are evaluated. The participants belonged to essential Python open-source projects such as Pandas, Matplotlib, Bokeh, Dask, etc. Special thanks to Jim Weiss Director of Events and Resources at NumFOCUS, for organizing and Jean-Robin Medori CPO at Taipy for participating! Early this week, Taipy was part of a Gold sponsor at Big Data & AI Paris: France’s largest AI-focused event. With more than 350 talks/presentations, 250 exhibiting companies, and over 16,000 participants, Big Data & AI Paris offers a unique opportunity to learn about the latest market trends and to network with Data and AI professionals. For our first appearance at the conference, Taipy was one of the busiest booths, and we got great feedback from a constant flow of visitors showing high interest in the product. It was an excellent opportunity for the Taipy team to interact with Pythonistas, Dev Team Leads, Heads of Data Analytics/Data Science Departments, CXOs, and Ph.D. Students, etc. On Monday, we did a 45min Taipy product tutorial during a Session tech, followed on Tuesday by the presentation of a customer use-case (a leader in the fast food industry). Both have been a great success with excellent attendance! Thank you to the BDAI Paris organizers, all the partners, and the visitors who came to meet us! Mark your calendars! Here is a list of upcoming 2022 events worldwide where the Taipy teams will be present. Great opportunities to come and meet : PyConKorea Oct. 1st and 2nd Big Data & AI in Toronto next Oct. 6 and 7th INFORMS Indianapolis Oct. 15-19th ODSC West in San Francisco Nov. 1-3rd PyData NYC Nov. 9-11th PyData Eindhoven Dec. 2nd

Taipy participated in the fantastic 3-day event PyConUK 2022 as a silver sponsor.

Rym Michaut — Tue, 20 Sep 2022 14:07:00 GMT

Despite the sad news of the death of the Queen, PyConUK 2022 has not been affected by royal events and has still gone ahead as planned at Cardiff City Hall on September 16, 17 & 18. PyConUK 2022 This first-class conference was set in the beautiful Cardiff City Hall. It hosted a fantastic schedule of talks and workshops, plus the ever-varied and well-enjoyed lightning talks. It was an excellent opportunity to demonstrate Taipy, our brand-new open-source and simple code framework! 👍 💥 The conference offered excellent attendance, fantastic discussions, demo sessions, and great feedback! Taipy at PyConUK 2022 Participants have been very enthusiastic about Taipy.io and our two main modules: Taipy GUI and Taipy Core. Our colleagues, Vincent & Florian, engaged with many participants, answering questions, showing demos, Taipy codes, etc. PyConUK 2022 insights A successful Event made of: 124,800 words recorded by STTRs 1500 Welsh cakes consumed 350 tickets sold out 219 photos 82.7dB on the lightning talk clap-o-meter 27 lightning talks 22 talks prepared and delivered 17 tractor jokes 16 hours of video recorded 11 amazing sponsors JPMorgan Chase & Co., Cookpad, Microsoft Developers, Bloomberg LP, Anvil, BenchSci, Invenia Labs, Sourcery, Taipy.io, PyXLL Ltd, Moneysupermarket Group 10 incredible workshops 9 hard-working volunteer organizers 8 modes (including a campervan) of transport used to get there 3 spoken word poems delivered Thanks to the organizers and Ann Barr for setting up such an exciting event!PyConUK, see you again next year! Pictures & videos are now available on PyCon UK 2022To see more, A little summary of #PyConUK2022(16) PyConUK 2022 in Numbers and Pictures | LinkedIn ‍

Taipy sponsoring ODSC APAC & PyBay conferences.

Rym Michaut — Thu, 15 Sep 2022 14:49:00 GMT

One week – 2 conferences! ODSC APAC & PyBay Conference Taipy participated in ODSC APAC, a virtual conference, on September 7th-8th as a Gold Sponsor. Over two days, ODSC APAC, the leading data science conference in Asia- Pacific, provided 3000 attendees with expert-led instruction in machine learning, deep learning, NLP, MLOps, and more through immersive workshops, tutorials, and talks. It was also a great opportunity to share insights and build new connections through networking events like Lightning Talks and Open Spaces. Taipy spoke during : ‍ 👉 A 60 min workshop on Sep 7th – 𝐖𝐨𝐫𝐤𝐬𝐡𝐨𝐩 “How to build stunning Data Science Web Applications in Python” presented by Florian Jacta (Customer Success Engineer at Taipy) 👉 A 30 min talk on Sep 8th, “Turning your Data/AI Algorithms into full web apps in no time with Taipy“ presented by Vincent Gosselin (Director & co-Founder of Taipy). Taipy demonstrated how powerful and game-changing it is! The recorded videos of the workshop and the tutorial will be available shortly. Later Taipy ended this first week of September by participating as a 𝐆𝐨𝐥𝐝 𝐬𝐩𝐨𝐧𝐬𝐨𝐫 𝐨𝐟 𝐏𝐲𝐁𝐚𝐲𝟐𝟎𝟐𝟐, 𝐭𝐡𝐞 𝟕𝐭𝐡 𝐚𝐧𝐧𝐮𝐚𝐥 𝐫𝐞𝐠𝐢𝐨𝐧𝐚𝐥 𝐏𝐲𝐭𝐡𝐨𝐧 𝐜𝐨𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞. It happened outdoors 𝐢𝐧 𝐒𝐚𝐧 𝐅𝐫𝐚𝐧𝐜𝐢𝐬𝐜𝐨’𝐬 𝐩𝐫𝐞𝐦𝐢𝐞𝐫 𝐟𝐨𝐨𝐝 𝐭𝐫𝐮𝐜𝐤 𝐩𝐚𝐫𝐤 𝐢𝐧-𝐩𝐞𝐫𝐬𝐨𝐧 𝐚𝐧𝐝 online 𝐨𝐧 𝐒𝐚𝐭𝐮𝐫𝐝𝐚𝐲, 𝐒𝐞𝐩𝐭𝐞𝐦𝐛𝐞𝐫 𝟏𝟎, 𝟐𝟎𝟐𝟐. More than 300 attendees showed up at this regional Python conference on the west coast. ‍ Taipy’s team welcomed the Pythonistas, did some introductory talks, and demonstrated our amazing solution. Taipy is very proud to be part of such a Tech community event that provided a great platform to network with the local Python community. Congratulations to - Vincent Gosselin (Director and co-Founder of Taipy), -Martin Shell (Vice President Customer Success Taipy), - David Swafford (VP Global Business Development Taipy), - Florian Jacta (Customer Success Engineer Taipy) and - Albert Vu (Customer Success Engineer Taipy)! Stay tuned! More conferences are coming in September: Big Data & AI in Paris, NUMFOCUS Project Summit in Santa Rosa, and Pycon UK Cardiff.

Taipy, raises 2M$ thanks to Legendary Venture

Vincent Gosselin — Wed, 14 Sep 2022 14:08:00 GMT

Introduction: We are thrilled to share the exciting news of Taipy’s successful US $2M seed round closing with Legendary Venture. This milestone underscores the growing interest in Python open-source productivity platforms as we continue to revolutionize the data science and Python development landscape. With this substantial seed funding, Taipy is poised to bolster its R&D efforts, further develop its cutting-edge product, and expand the Taipy Community on a global scale. We are committed to enhancing the already impressive features that Taipy offers, empowering data scientists and Python developers to reach new heights of productivity and efficiency. Why Startups Need Venture Funding Venture funding plays a pivotal role in the growth and success of startups like Taipy. For emerging tech companies, securing seed funding is crucial for several reasons: Fueling Innovation: With financial backing, startups can invest in research and development, exploring new ideas and pushing the boundaries of what’s possible in their industry. Product Development: Funding enables startups to accelerate product development, bringing innovative solutions to market faster and staying ahead of the competition. Scaling Operations: As startups gain traction and acquire more users or customers, they need capital to scale their operations and meet increasing demands. Global Expansion: Venture funding paves the way for international expansion, helping startups tap into new markets and reach a broader audience. Attracting Talent: Funding allows startups to attract top-tier talent, fostering a strong team that drives the company’s vision forward. Python Open-Source Productivity Platform : Key Focus Areas Enabled by Seed Funding: The recent seed funding from Legendary Ventures empowers Taipy to focus on several key areas of growth: Research and Development: We will intensify our research efforts to stay at the forefront of the Python open-source productivity platform market, continuously enhancing Taipy’s capabilities. Product Development: With a strong emphasis on product development, Taipy will introduce Taipy Release 2.0 by the end of this month, bringing even more value to our users. Global Community Building: We are committed to growing the Taipy Community worldwide, fostering collaboration and knowledge sharing among data scientists and Python developers in the open-source productivity platform space. Enterprise Version Expansion: The traction of Taipy Enterprise in Europe, Asia, and the US signals further expansion opportunities, enabling us to cater to large accounts and enterprises worldwide seeking top-notch Python open-source productivity platforms. Industry Presence: Taipy will be a prominent presence at major AI and data conferences over the next 12 months, taking part in the Taipy Fall World Tour and showcasing our groundbreaking open-source productivity platform. Conclusion: We express our heartfelt gratitude to our supportive community and customers, without whom this journey would not have been possible. As we announce this successful seed round with Legendary Venture, we are excited to embark on the next chapter of Taipy’s story. Our focus remains on building an innovative platform that empowers data scientists and Python developers to excel in their work, making Taipy the go-to choice in the Python open-source productivity platform ecosystem. ‍ About Taipy: Taipy is the next-generation Python application builder that revolutionizes the transformation of algorithms into powerful Decision Support Systems for end-users. Comprising Taipy GUI and Taipy Core, our platform offers highly interactive production-ready GUIs for the web and intelligent pipeline management, data caching, scenario management, and more, making it a leading Python open-source productivity platform. For more information, visit Taipy or follow us on Linkedin and Twitter. Contact: For media inquiries or further information, please contact us at contact@taipy.io.

Taipy participates in The Vietnamese OR society Annual Event (VORN)!

Rym Michaut — Fri, 12 Aug 2022 14:11:00 GMT

It was an excellent opportunity to introduce and demonstrate Taipy in a 2-hour tutorial for the Applied Maths, AI, and IE Departments. At the main event, Vincent also gave a key-note speech presenting the challenges of building successful AI/OR applications for large organizations and how Taipy can address most of them. There were fascinating talks in the other sessions from Academics based in Vietnam and overseas universities (Latrobe, Berkeley, etc.). It demonstrated how dynamic this field is with the association of AI and OR techniques. Both online and on-site events were a big success, thanks to insightful discussions and excellent interactions. Special Thanks to Prof Nguyen Van Hop for his warm welcome. Thanks to Prof Nguyen Van Hop for the organization and the warm welcome from his team! Congratulations to the Taipy teams! Taipy is looking forward to more interactions soon!

Taipy on air! Vincent Gosselin interview on BFM Business TV and radio is out!

Rym Michaut — Wed, 03 Aug 2022 14:09:00 GMT

THE WAIT IS OVER. 🥳 Vincent Gosselin has been interviewed to introduce Taipy, The open-source Python web application builder, by Vincent Touraine on the French TV show BFM Business TV & Radio MEDIAS FRANCE – Objectif croissance. 👉It was on Thursday July 28th at 9.55am and Sunday September 25th at 3.26 pm watch the French interview. We are thrilled to share with you the live video of Vincent Gosselin interview on BFM. The CEO and co-founder of Avaiga, was live on BFM TV & Radio MEDIAS FRANCE – Objectif croissance 👋 👀 Check out the video 🎙️ 📺

Elegant Dashboards for Python ML applications using Taipy GUI

Zacheus Sia — Fri, 17 Jun 2022 12:34:00 GMT

Thanks to Zaccheus Sia, Data Scientist at Knowledge Touch, for this wonderful article about Taipy-GUI-generated elegant dashboards on Python ML Applications! Theme: Exploring a low code Python library for data science projects. Summary Why Use Dashboards? Building Dashboards Using Taipy GUI Taipy GUI — A Bare-bones Example Exploring Penguins (Hands-on demo) 1. Load the Dataset 2. Visual Elements 3. Selector 4. Indicator 5. Chart 6. Layout 7. Button, Dialog Box and Table 8. Global Callback Function 9. Putting It All Together Final Thoughts

Learn about Taipy

Rym Michaut — Thu, 16 Jun 2022 12:52:00 GMT

You can now learn about Taipy by checking two new videos promoting and explaining the positioning of this revolutionary library. Learn about Taipy and why it was born In this video, Vincent Gosselin, Taipy’s CEO, introduces the rationale for the existence of the game-changing tool: Taipy, the first end-to-end simple code Python platform. Learn about Taipy, and why it has been developed. Business users will not just accept the output of an algorithm, however smart it can be. That’s why Taipy was born . Learn about Taipy and what it is precisely. This video is an animated presentation of the functioning of Taipy and all the incredible features it brings to take data scientists’ and developers’ jobs. Taipy will help them be successful with their Python developments, whether they want to develop a simple pilot or a full-scale application. It has been designed to reduce the development and deployment time.

The Stevens Institute of Technology recommends Taipy

Alkiviadis Vazacopoulos — Thu, 02 Jun 2022 12:21:00 GMT

New software is constantly being developed to replace complex programming tasks with simple, low-code solutions that still produce an equally viable or better product. For a programmer who only specializes in a specific field, building an end-to-end application could take months or even years of learning new languages and tools. However, thanks to new low-code services, completing such a project is possible. In this article Basic interactive Dashboard Exponential smoothing Real time forecast Read it on LinkedIn

Taipy is now live!

Rym Michaut — Thu, 07 Apr 2022 14:13:00 GMT

It’s official, 7th of April 2022 we just launched Taipy our open-source product! We’re looking forward to building up our user community in the months to come and seeing fantastic apps developed by you all! We wish to thank our different Beta-testers and early adopters. Thank you for all of your support. ‍ Jump in with Github