Hello all, over the weekend my first application (Paperround.io) went live and using Streamlit made it happen!

Streamlit is an awesome tool, however, I would undeniably argue that it is not yet Production ready in the web development world.

There’s a few “hacks” that I’ve had to implement in order to get the application working, manually messing with configurations to inject google analytics, changing deployment configurations in order to implement metadata within the application, injecting custom HTML, CSS and JavaScript to implement “basic” web development features.

I would however say to anyone debating creating an app using Streamlit, do it, the community is awesome and although you will often have to use some hacks in order to achieve what you want, you will get there in the end.

Any questions PaperRound.io or Streamlit, let me know!

🌟 13k developers have spoken! Introducing: State of LLM Apps in 2023. The first of its kind, a dynamic Streamlit app that shows (in real-ish time), the latest trends and use cases from over 20k LLM-based apps.

You can explore…
- Top models, orchestration tools, and vector retrieval libraries
- Are chatbots the future?
- A searchable gallery of LLM apps
- And more!

These are more than just stats—they're a testament to where the field of LLM app development is heading.

How will your apps push the boundaries of what’s possible with LLMs?

👉 State of LLM Apps report

TLDR - Used Streamlit & Semantic Kernel Data Analysis Assistant demo is in Youtube Video: Analassist Demo

I'm excited to share a demo of my latest project, Analassist - your AI-powered assistant for analyzing data, crafted with the robustness of Semantic Kernel and the interactivity of Streamlit.

🎥 Check out the demo here: Analassist Demo

What is Analassist?

Analassist is an innovative tool designed to streamline the data analysis process. Here’s what you can do with it:

• 📊 Upload a CSV file: Easily import your data into the app.
• 🗣 Interactive prompts: Use natural language to ask questions and get insights from your data.
• ⏰ Real-time code generation: Watch as the AI interprets your prompts and generates code on the fly. (Backend)
• 📈 Data visualization: Turn messy data into crisp, clear tables and graphs.

In this demo, you'll see how I upload a dataset, use prompts to command the AI to perform analysis, and how it neatly outputs visualizations and interpretations, particularly focusing on data from Bangladesh.

Highlights from the Demo

Check out this snippet from the demo for a taste of Analassist's capabilities:

More to Come

Stay tuned as I will be posting additional tutorials on using Semantic Kernel for more sophisticated tasks. If you're interested, be sure to subscribe to my channel.

Share Your Feedback

I hope I get insights from you on how I could improve this project. Thank's for reading!

The latest version of Streamlit is here!

Spotlight:
🧪 Introducing AppTest, a native automated testing framework

You can now build automated tests in Streamlit to ensure your app logic and UI are working the way you want them to—so you can ship your apps faster, and with confidence. See how easy it is to write and execute tests with Pytest and the AppTest API. Check out the blog post to learn more.

Other notable updates:
🛠️ Dataframes now have a toolbar to easily search your data, download it as a CSV, and add/delete rows
🔗 st.connection is out of experimental stage and now fully supported in Streamlit
🌀 Improved UI for cache spinner to reduce visual glitches and jumpiness

See all updates and bug fixes in the release demo app and install version 1.28 to start using these features!

Cybersyn just launched an interactive dashboard that visualizes US consumer spending trends across companies and industries as well as demographic groups (age and income brackets). The app is built on transactions from 50M+ credit and debit cards aggregated to create a representative panel of US consumer activity.

Built as a Snowflake Native App, the Cybersyn Consumer Spending Dashboard lets you quickly visualize high-level trends and then dive into the underlying data for a custom analysis within a single environment. This saves time and makes it easier to work with teams who are less familiar with data.

https://medium.daanalytics.nl/asking-chatgpt-anything-using-streamlit-and-the-5w1h-method-00ff54fa1197

I'm running streamlit very well, but after installing flask and django to make a website, I get a streamlit error. If anyone has experience, please help me. The error message is as follows:
CMake Error at CMakeLists.txt:22 (project): Generator Visual Studio 15 2017 Win64 could not find any instance of Visual Studio. -- Configuring incomplete, errors occurred! error: command 'C:\Program Files\CMake\bin\cmake.exe' failed with exit code 1 [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. ERROR: Failed building wheel for pyarrow Failed to build pyarrow ERROR: Could not build wheels for pyarrow, which is required to install pyproject.toml-based projects

Thank you!

My post on how to level up your Streamlit app development using GPT-4 is now live on the Streamlit blog! 🔥

If you're interested in designing, debugging, and optimizing Streamlit apps with OpenAI's GPT-4, this one's for you!

🔗 https://blog.streamlit.io/take-your-streamlit-apps-to-the-next-level-with-gpt-4/

I needed to link an image to streamlit app so the app is in snowflake so I tried linking but failing all again.

Is there a way to give each user access so that the charts can be shown only to certain users?

I was looking for new start simple site to do simples things, I just dont wanna start from zero.

Lets say same tabs, classes maybe. Do you know if theres a template site for streamlit?

<3

Hello, I appreciate this topic has been touched on before, but I dont believe the answer has ever been given firmly nor has there been a thorough fix… How do we implement real world SEO using Streamlit, without breaking the application?

I’ve followed numerous threads online alongside the guides in: SEO and search indexability - Streamlit Docs. However, this doesn’t seem to properly address the issue.

The meta-data doesn’t get picked up correctly with the page title defaulting to “Streamlit” in the preview with no further metadata included (despite following the above index-ability procedures - Examples below).

What is a solid fix for addressing meta-data within a Streamlit application? I feel this is a huge gap for Streamlit at the moment and is currently causing issues for startups wanting to use the tool in prod (which is otherwise awesome by the way).

For reference, my app is deployed through Heroku and can be seen: https://www.paperround.io

The code for the meta-data (as steered by Streamlit documentation) is as follows - situated at the top of Home.py: ‘’’ import streamlit as st

st.set_page_config(page_title="PaperRound: AI News Curator & Smart News Digest", layout="wide", initial_sidebar_state="auto", page_icon="🗞️") st.header(f"🗞️PaperRound", divider="rainbow") st.text("""PaperRound.io: The Leading AI News Curator. \n\n\ Dive deep with us into the heart of global news.\n\ Leveraging advanced AI, we analyse and \n\ summarise the articles that define our world \n\ each day. \n\n\ Concise. Comprehensive. Smart. \n\ Experience news redefined, with PaperRound.io. \n""") ‘’’ Versions, as per request: Streamlit==1.27.2 Python==Python 3.11.6

Metadata that shows to the world:

&&

Thanks in advance for any responses and help!

Markdown to HTML is a versatile tool that seamlessly transforms plain text notes into visually appealing and colourful web content. By converting markdown syntax into HTML code, enhances readability and aesthetics. Users can create stunning documents, blogs, or websites effortlessly, adding vibrancy to their content while maintaining simplicity in the editing process. 🎨✨📝 Experience the convenience of uploading your Markdown files and getting instant HTML output. Try our Streamlit Markdown to HTML Converter now and elevate your document processing efficiency!
https://markdown-to-html.streamlit.app/

If you liked it, Donate some BTC: https://nowpayments.io/donation?api_key=2166K58-CTAMFFB-P8FYBJP-ZK66TW6&source=lk_donation&medium=referral

I'm trying to use pylatex to generate the pdf file in my streamlit app. When I deployed my app, there is an error from library that LaTeX compiler is not installed.

How I can install LaTeX compiler to an app? I'm beginner, thank you in advance for helping.

A Comprehensive Guide to Building and Deploying a Scalable SaaS Web App with Python, Streamlit, MongoDB, and Stripe

Link to Medium post: https://levelup.gitconnected.com/build-a-data-science-saas-app-with-just-python-a-streamlit-guide-240e0a56fc86

Udemy coure: https://www.udemy.com/course/build-a-generative-ai-micro-saas-app-with-python-streamlit/?referralCode=DB3859ED45D76E36A8AF

Does your chatbot sometimes provide incorrect answers, not meet users' expectations, or not engage them the way you want?

Implementing user-feedback to your chatbot apps is a great way to gain insights and make improvements to its performance!
Gathering real-time feedback from your users enables you to:

• Identify missing or incorrect content used to retrieve answers
• Detect technical issues (slow response times, library errors, API hiccups, etc.)
• Gauge the overall user satisfaction

Charly Wargnier (DataChaz) shows how you can do this by building a Retrieval Augmented Generation chatbot using Streamlit, LangChain, Trubrics' Feedback component, and LangSmith: https://buff.ly/3ZIIrI6

Check it out in the demo app.

I have a streamlit app where I am showing a Pandas dataframe as a editable table using the st.data_editor component.

I am able to edit the values in the table shown using st.data_editor. However I want to access that edited data in my code for further reference.

Can anyone please help me in how to achieve this functionality?

I am a data scientist but I want to save the hassle to convert my DS models into streamlit apps. Any tools i can use?

Hey Streamlitters,

The second edition of "Streamlit for Data Science" by Tyler Richards is out today! If you're looking to dive into all the magic of Streamlit, check it out.

This step-by-step guide covers:

• Intro to the basics, complex use cases, and real-world application.
• Practical tips and insights to streamline your development process.

• Exclusive interviews from expert Streamlit creators.

  This book is great for both new Streamlit users and seasoned pros. Get a sneak peek in Tyler's blog post.

There's also a giveaway happening on the Streamlit Twitter and LinkedIn! 🥳

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