Unlock the full potential of Streamlit, mastering web app development from setup to deployment with practical guidance, advanced techniques, and real-world examples Key Features Identify and overcome web development challenges, crafting dedicated application skeletons using Streamlit Understand how Streamlit's widgets and components work to implement any kind of web app Manage web application development and deployment with ease using the Streamlit Cloud service Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis book is a comprehensive guide to the Streamlit open-source Python library and simplifying the process of creating web applications. Through hands-on guidance and realistic examples, you’ll progress from crafting simple to sophisticated web applications from scratch. This book covers everything from understanding Streamlit's central principles, modules, basic features, and widgets to advanced skills such as dealing with databases, hashes, sessions, and multipages. Starting with fundamental concepts like operation systems virtualization, IDEs, development environments, widgets, scripting, and the anatomy of web apps, the initial chapters set the groundwork. You’ll then apply this knowledge to develop some real web apps, gradually advancing to more complex apps, incorporating features like natural language processing (NLP), computer vision, dashboards with interactive charts, file uploading, and much more. The book concludes by delving into the implementation of advanced skills and deployment techniques. By the end of this book, you’ll have transformed into a proficient developer, equipped with advanced skills for handling databases, implementing secure login processes, managing session states, creating multipage applications, and seamlessly deploying them on the cloud.What you will learn Develop interactive web apps with Streamlit and deploy them seamlessly on the cloud Acquire in-depth theoretical and practical expertise in using Streamlit for app development Use themes and customization for visually appealing web apps tailored to specific needs Implement advanced features including secure login, signup processes, file uploaders, and database connections Build a catalog of scripts and routines to efficiently implement new web apps Attain autonomy in adopting new Streamlit features rapidly and effectively Who this book is for This book is for Python programmers, web developers, computer science students, and IT enthusiasts with a foundation in Python (or any programming language) who have a passion for creating visually appealing applications. If you already know how to write programs, this book will help you evolve into an adept web application developer skilled at converting command-line tools into impressive, cloud-hosted applications.

Craft resilient web applications with Supabase by leveraging advanced features such as authentication, data and user management, and seamless AI integration using its powerful Postgres infrastructure Key Features Learn how to integrate Supabase and Next.js to create powerful and scalable web apps Explore real-world scenarios with a multi-tenant ticket system Master real-time data handling, secure file storage, and application security enhancement, while discovering the full potential of the database beyond holding data Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDiscover the powerful capabilities of Supabase, the cutting-edge, open-source platform flipping the script on backend architecture. Guided by David Lorenz, a battle-tested software architect with over two decades of development experience, this book will transform the way you approach your projects and make you a Supabase expert. In this comprehensive guide, you'll build a secure, production-grade multi-tenant ticket system, seamlessly integrated with Next.js. You’ll build essential skills for effective data manipulation, authentication, and file storage, as well as master Supabase's advanced capabilities including automating tasks with cron scheduling, performing similarity searches with artificial intelligence, testing your database, and leveraging real-time updates. By the end of the book, you'll have a deeper understanding of the platform and be able to confidently utilize Supabase in your own web applications, all thanks to David's excellent expertise.What you will learn Explore essential features for effective web app development Handle user registration, login/logout processes, and user metadata Navigate multi-tenant applications and understand the potential pitfalls and best practices Discover how to implement real-time functionality Find out how to upload, download, and manipulate files Explore preventive measures against data manipulation and security breaches, ensuring robust web app security Increase efficiency and streamline task automation through personalized email communication, webhooks, and cron jobs Who this book is for This book is for developers looking for a hassle-free, universal solution to building robust apps using Supabase and its integration libraries. While a basic understanding of JavaScript is useful, it’s not essential as the book focuses on Supabase for creating high-performance web apps using Next.js. Experienced professionals from non-JavaScript backgrounds will find this book useful. Familiarity with Postgres, although helpful, is not mandatory as the book explains all the SQL statements used.

Create, deploy, and test your Python applications, analyses, and models with ease using Streamlit Key Features Learn how to showcase machine learning models in a Streamlit application effectively and efficiently Become an expert Streamlit creator by getting hands-on with complex application creation Discover how Streamlit enables you to create and deploy apps effortlessly Book DescriptionStreamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time. You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you’ll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps. By the end of this book, you’ll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.What you will learn Set up your first development environment and create a basic Streamlit app from scratch Explore methods for uploading, downloading, and manipulating data in Streamlit apps Create dynamic visualizations in Streamlit using built-in and imported Python libraries Discover strategies for creating and deploying machine learning models in Streamlit Use Streamlit sharing for one-click deployment Beautify Streamlit apps using themes, Streamlit Components, and Streamlit sidebar Implement best practices for prototyping your data science work with Streamlit Who this book is for This book is for data scientists and machine learning enthusiasts who want to create web apps using Streamlit. Whether you’re a junior data scientist looking to deploy your first machine learning project in Python to improve your resume or a senior data scientist who wants to use Streamlit to make convincing and dynamic data analyses, this book will help you get there! Prior knowledge of Python programming will assist with understanding the concepts covered.

Transition from a back-end developer to a full-stack developer with knowledge of all the dimensions of web application development, namely, front-end, back-end and server-side software. This book provides a comprehensive overview of Streamlit, allowing developers and programmers of all backgrounds to get up to speed in as little time as possible. Streamlit is a pure Python web framework that will bridge the skills gap and shorten development time from weeks to hours. This book walks you through the complete cycle of web application development, from an introductory to advanced level with accompanying source code and resources. You will be exposed to developing basic, intermediate, and sophisticated user interfaces and subsequently you will be acquainted with data visualization, database systems, application security, and cloud deployment in Streamlit. In a market with a surplus demand for full stack developers, this skill set could not possibly come at a better time. In one sentence, Streamlit is a means for the empowerment of developers everywhere and all stand to gain from it. You will: Mutate big data in real-time Visualize big data interactively Implement web application security and privacy protocols Deploy Streamlit web applications to the cloud using Streamlit, Linux and Windows servers.

Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development aims at showcasing different structure-based, ligand-based, and machine learning tools currently used in drug design. It also highlights special topics of computational drug design together with the available tools and databases. The integrated presentation of chemometrics, cheminformatics, and machine learning methods under is one of the strengths of the book.The first part of the content is devoted to establishing the foundations of the area. Here recent trends in computational modeling of drugs are presented. Other topics present in this part include QSAR in medicinal chemistry, structure-based methods, chemoinformatics and chemometric approaches, and machine learning methods in drug design. The second part focuses on methods and case studies including molecular descriptors, molecular similarity, structure-based based screening, homology modeling in protein structure predictions, molecular docking, stability of drug receptor interactions, deep learning and support vector machine in drug design. The third part of the book is dedicated to special topics, including dedicated chapters on topics ranging from de design of green pharmaceuticals to computational toxicology. The final part is dedicated to present the available tools and databases, including QSAR databases, free tools and databases in ligand and structure-based drug design, and machine learning resources for drug design. The final chapters discuss different web servers used for identification of various drug candidates. - Presents chemometrics, cheminformatics and machine learning methods under a single reference - Showcases the different structure-based, ligand-based and machine learning tools currently used in drug design - Highlights special topics of computational drug design and available tools and databases

DESCRIPTION Streamlit Essentials is a comprehensive guide aimed at helping you build interactive data applications using Python. With easy-to-use syntax, it allows developers to quickly build visualizations, dashboards, and machine learning models. This book is a practical guide to building data science applications using the Streamlit framework. It covers everything from installation to advanced topics like ML integration and deployment. With real-world projects and examples, you will learn how to use Streamlit's widgets, styling, and data visualization tools to create dynamic real-time dashboards, containerize your applications with Docker, securely handle sensitive data, and deploy the applications on leading cloud platforms, all while building practical projects that can be added to enhance your portfolio. Throughout the book, you will develop the skills needed to turn data insights into interactive visualizations, ensuring your projects are not only functional but also engaging. The focus is hands-on learning, with step-by-step guidance to help you build, optimize, and share your work. By the time you have completed this book, you will be able to confidently deploy applications, showcase your skills through a professional portfolio, and position yourself for success. KEY FEATURES ● Learn how to present data insights quickly and clearly using Streamlit for smoother collaboration between business and tech teams. ● Master Streamlit’s core and advanced features through hands-on projects like product recommenders. ● Build and deploy data applications while exploring over 25 project ideas to enhance your Streamlit skills. ● Explore the Gen AI toolkit to speed up your development cycle from ideation to deployment. WHAT YOU WILL LEARN ● Understanding of Streamlit's capabilities, from its core functionalities to advanced features. ● Create engaging and informative visualizations using Streamlit's extensive library of charts, graphs, and maps. ● Develop efficiently using time-saving techniques for rapid prototyping and iterative development. ● Optimize app performance with advanced topics like caching, session tracking, and theming. ● Create a compelling portfolio to demonstrate your Streamlit proficiency. WHO THIS BOOK IS FOR Whether you are a data scientist, analyst, developer, or business professional, this book will provide you with the knowledge and skills needed to build engaging and informative dashboards, visualizations, and ML models. TABLE OF CONTENTS 1. Introduction to Streamlit 2. Getting Started with Streamlit 3. Exploring Streamlit Widgets 4. Styling and Layouts in Streamlit 5. Data Visualization with Streamlit 6. Streamlit and Machine Learning 7. Advanced Streamlit Concepts 8. Deployment of Streamlit Apps 9. Hands-On Projects: Easy 10. Hands-On Projects: Intermediate 11. Hands-On Projects: Advanced 12. Build and Enhance Your Portfolio 13. Enhancing Streamlit Development with AI Tools Appendix A: Streamlit Cheat Sheet Appendix B: Additional Resources and References Appendix C: Docker 101: Beginner’s Guide to Containers

2024 Edition – Get to grips with the LangChain framework to develop production-ready applications, including agents and personal assistants. The 2024 edition features updated code examples and an improved GitHub repository. Purchase of the print or Kindle book includes a free PDF eBook. Key Features Learn how to leverage LangChain to work around LLMs’ inherent weaknesses Delve into LLMs with LangChain and explore their fundamentals, ethical dimensions, and application challenges Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality Book DescriptionChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Gemini. It demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications. Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.What you will learn Create LLM apps with LangChain, like question-answering systems and chatbots Understand transformer models and attention mechanisms Automate data analysis and visualization using pandas and Python Grasp prompt engineering to improve performance Fine-tune LLMs and get to know the tools to unleash their power Deploy LLMs as a service with LangChain and apply evaluation strategies Privately interact with documents using open-source LLMs to prevent data leaks Who this book is for The book is for developers, researchers, and anyone interested in learning more about LangChain. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs using LangChain. Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily.

Learn concepts, methodologies, and applications of deep learning for building predictive models from complex genomics data sets to overcome challenges in the life sciences and biotechnology industries Key FeaturesApply deep learning algorithms to solve real-world problems in the field of genomicsExtract biological insights from deep learning models built from genomic datasetsTrain, tune, evaluate, deploy, and monitor deep learning models for enabling predictions in genomicsBook Description Deep learning has shown remarkable promise in the field of genomics; however, there is a lack of a skilled deep learning workforce in this discipline. This book will help researchers and data scientists to stand out from the rest of the crowd and solve real-world problems in genomics by developing the necessary skill set. Starting with an introduction to the essential concepts, this book highlights the power of deep learning in handling big data in genomics. First, you'll learn about conventional genomics analysis, then transition to state-of-the-art machine learning-based genomics applications, and finally dive into deep learning approaches for genomics. The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets. By the end of this book, you'll have learned about the challenges, best practices, and pitfalls of deep learning for genomics. What you will learnDiscover the machine learning applications for genomicsExplore deep learning concepts and methodologies for genomics applicationsUnderstand supervised deep learning algorithms for genomics applicationsGet to grips with unsupervised deep learning with autoencodersImprove deep learning models using generative modelsOperationalize deep learning models from genomics datasetsVisualize and interpret deep learning modelsUnderstand deep learning challenges, pitfalls, and best practicesWho this book is for This deep learning book is for machine learning engineers, data scientists, and academicians practicing in the field of genomics. It assumes that readers have intermediate Python programming knowledge, basic knowledge of Python libraries such as NumPy and Pandas to manipulate and parse data, Matplotlib, and Seaborn for visualizing data, along with a base in genomics and genomic analysis concepts.

"Streamlit for Everything!" is your ultimate compass for navigating the universe of Streamlit, from the first steps to advanced techniques. This meticulously crafted guide is suitable for both beginners eager to enter the world of interactive web application development and experienced professionals looking to deepen their skills in data science and complex visualizations. Deivison Viana, with his rich and multifaceted experience, unfolds Streamlit in chapters that are true gems: learn to install and configure, create dynamic dashboards, integrate APIs, and even use websockets for real-time data analysis. Discover how Streamlit can be applied in Human Resources, optimizing evaluation and recruitment processes, and dive into financial applications, simulating markets and monitoring portfolios. Not only that, but marketing professionals will find strategies to leverage data and conduct A/B testing for more effective campaigns. Each chapter is reinforced with practical challenges, encouraging you to apply the knowledge and build an impressive portfolio. Whether you are a student, a data scientist, a web developer, or a manager, "Streamlit for Everything!" promises to elevate your skills and understanding of Streamlit to new heights.

An introduction to the Python programming language and its most popular tools for scientists, engineers, students, and anyone who wants to use Python for research, simulations, and collaboration. Python Tools for Scientists will introduce you to Python tools you can use in your scientific research, including Anaconda, Spyder, Jupyter Notebooks, JupyterLab, and numerous Python libraries. You’ll learn to use Python for tasks such as creating visualizations, representing geospatial information, simulating natural events, and manipulating numerical data. Once you’ve built an optimal programming environment with Anaconda, you’ll learn how to organize your projects and use interpreters, text editors, notebooks, and development environments to work with your code. Following the book’s fast-paced Python primer, you’ll tour a range of scientific tools and libraries like scikit-learn and seaborn that you can use to manipulate and visualize your data, or analyze it with machine learning algorithms. You’ll also learn how to: Create isolated projects in virtual environments, build interactive notebooks, test code in the Qt console, and use Spyder’s interactive development features Use Python’s built-in data types, write custom functions and classes, and document your code Represent data with the essential NumPy, Matplotlib, and pandas libraries Use Python plotting libraries like Plotly, HoloViews, and Datashader to handle large datasets and create 3D visualizations Regardless of your scientific field, Python Tools for Scientists will show you how to choose the best tools to meet your research and computational analysis needs.