In the past we've looked at Data Viz and exploratory analysis with Streamlit, used Streamlit as the backend for library open publishing research, and had some fun with GIS and Streamlit to study local canopies. In this review we're looking at how ChatGPT can produce Streamlit boilerplate code and then how GitHub Codespaces may be used to do even faster prototyping with additional capabilities.
In the rapidly evolving field of library sciences, leveraging innovative technologies is crucial for efficient management and seamless user experiences. Python Streamlit, ChatGPT, and GitHub Codespaces offer a powerful combination of tools that can enhance various aspects of library sciences, from data visualization to user interaction. In this article, we will explore the benefits, use cases, associated costs, and means of obtaining support for utilizing these tools in the university and library sciences domain.
I. Python Streamlit: an open-source framework originally released in 2019, designed for building interactive web applications with minimal code. It enables library professionals to create intuitive and visually appealing interfaces for data exploration, analysis, and dissemination.
Cost: Python Streamlit is free and open-source, making it an economical choice for library sciences projects.
Common Use Cases:
Data Visualization: Streamlit simplifies the process of creating dynamic visualizations to present data in an engaging manner, aiding in data-driven decision-making.
User Interfaces: With Streamlit, libraries can build user-friendly interfaces for search, content faceting etc... improving user experience and accessibility.
Prototyping and Testing: Streamlit's rapid development capabilities make it ideal for prototyping new library services or experimenting with user workflows. This comes in especially handy with the Codespaces integration
Support: Python Streamlit has an active and supportive community. The official Streamlit documentation, community forums, and GitHub repository are excellent resources for learning and troubleshooting. Additionally, there are various online tutorials and blog posts available to aid in getting started. On grounds there is support in most libraries for Python, and there as well as various short courses offered on ad hoc basis.
II. ChatGPT: powered by OpenAI's language model, ChatGPT enables libraries to create conversational agents for improved user engagement and personalized assistance (like help writing the first draft of this article!). It allows users to interact with the library's services through natural language conversations. Every article detailing ChatGPT or other AI tools should include a caveat - it's possible for AI to make mistakes. Libraries should begin working on policies to determine when to open the doors to this powerful new technology, and how to set up guardrails to prevent the spread of misinformation. Let's delve into the details:
Cost: OpenAI offers a range of pricing plans for using ChatGPT. The cost depends on factors such as usage, model capacity, and API calls. It is recommended to review the OpenAI pricing page for specific details.
Possible Use Cases:
Virtual Reference Services: ChatGPT can be used to provide automated virtual reference services, answering user queries and providing assistance in real-time.
Recommender Systems: libraries can develop intelligent recommender systems that suggest relevant books, articles, or resources based on user preferences.
User Support: ChatGPT can handle frequently asked questions, guiding users through library services, and offering support for common issues with some minor training
Support: OpenAI provides comprehensive developer documentation and guides to assist in integrating ChatGPT into applications. The OpenAI community forum and support channels are valuable resources for addressing queries and troubleshooting issues.
III. GitHub Codespaces: GitHub Codespaces offers a cloud-based development environment that enables seamless collaboration and version control for library sciences projects. It provides a hassle-free setup for development and facilitates team collaboration. Although Codespaces is flexible it can help to use a Codespace template that has some configurations baked in. Port forwarding, Python versioning, and other libraries can be rolled out automagially - for this example I used https://github.com/robmarkcole/streamlit-codespace that had the above as well as the streamlit library itself pre-loaded. Here's some more information on Codespaces:
Cost: GitHub Codespaces offers both free and paid plans. The pricing structure is based on factors such as the number of concurrent Codespace instances and storage requirements. Detailed pricing information is available on the GitHub website.
Collaborative Development: Codespaces enables multiple library professionals to work together on codebases, fostering efficient collaboration and reducing development time.
Testing and Debugging: Codespaces provides an isolated and controlled environment for testing and debugging library-related code, ensuring code quality and reliability.
Continuous Integration/Continuous Deployment (CI/CD): By integrating Codespaces with CI/CD pipelines, libraries can automate the process of building, testing, and deploying applications. Github Actions provide additional CI/CD opportiunities
Support: GitHub provides extensive documentation and guides for getting started with Codespaces. The GitHub Community Forum and support resources are available to address any questions or issues that arise during development.
Conclusion: Incorporating Python Streamlit, ChatGPT, and GitHub Codespaces into library sciences can significantly enhance data visualization, user interaction, and collaborative development. The combination of these tools empowers library professionals to deliver improved services and user experiences. With their affordability, diverse use cases, and strong community support, adopting these technologies can lead to transformative outcomes in library sciences projects.
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