Advanced Streamlit for Python Developers: A PyCon US `24 Tutorial

By Caroline Frasca and Tony Kipkemboi

This repository hosts the code for the Advanced Streamlit for Python Developers PyCon US 2024 tutorial talk that will take place in Pittsburgh, PA.

Streamlit is a faster way to build and share data apps. Streamlit turns data scripts into shareable web apps in minutes. All in pure Python. No front-end experience required.

Check out our docs to get started!

🚀 Getting Started

To run the GitHub Repository Analytics Dashboard locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/tonykipkemboi/streamlit_pycon24_tutorial.git

2. Navigate to the project directory:

   cd streamlit_pycon24_tutorial

3. Set up your OpenAI API key:

   • Create a file named .streamlit/secrets.toml in the project directory (see example file in .streamlit/example_secrets.toml)

   • Add your OpenAI API key to the file in the following format:

     OPENAI_API_KEY = "your_api_key_here"

4. Install the required dependencies:

   pip install -r requirements.txt

5. Run the Streamlit app:

   streamlit run 01_📈_Repository_analytics.py

6. Open your web browser and visit http://localhost:8501 to access the dashboard.

📂 Repository Structure

The repository contains the following files:

• 01_📈_Repository_analytics.py: The main Streamlit app file that contains the code for the Streamlit GitHub Repository Analytics Dashboard.
• 02_💬_Chat_with_the_Streamlit_docs.py: A chatbot app that demonstrates how to chat with the Streamlit documentation using LlamaIndex and OpenAI.
• data/: Directory containing the CSV files used for data analysis.
• docs/: Directory containing the Streamlit documentation files for the chat app.
• requirements.txt: File listing the required Python dependencies.

📊 Features

1. GitHub Repository Analytics Dashboard

The GitHub Repository Analytics Dashboard provides the following features:

• ⏰ Code Frequency: Visualize code changes over time, including weekly code changes comparison and cumulative code changes.
• 📬 Commit Activity: Track the total number of commits, average weekly commits, and week-over-week change.
• 👩‍💻 Contributors: Analyze contributors and their activity, view contributions over time for selected users, and explore filtered data.

2. Chat with the Streamlit Docs

A chat app that allows you to interact with the Streamlit documentation using natural language queries. Features include:

• 💬 Streamlit UI: Ask questions about Streamlit's open-source Python library using natural language and get responses from the LLM.
• 🧠 Powered by LlamaIndex: The chat app leverages LlamaIndex to efficiently search and retrieve relevant information from the Streamlit documentation.
• 🤖 OpenAI Integration: The app uses OpenAI's GPT-3.5-turbo model to generate human-like responses based on the retrieved information.

💡 Tutorial

By following this tutorial, you'll learn how to:

• Set up a Streamlit app and create tabs for different analytics views
• Implement a data loading function to handle CSV files
• Create visualizations for code frequency, commit activity, and contributor analysis
• Interact with the dashboard using sliders, dropdowns, and expandable sections
• Customize the app's appearance and layout
• Use the latest Streamlit feature @st.experimental_fragment; a new decorator that turns any function into a "fragment" that can run independently of the wider page

📧 Contact

If you have any questions or feedback, feel free to reach out to us in the Streamlit forum. We'd love to hear from you! 💬

Happy Streamlit-ing! 🎈