https://github.com/tonykipkemboi/streamlit_pycon24_tutorial
Streamlit tutorial presentation for PyCon 2024
https://github.com/tonykipkemboi/streamlit_pycon24_tutorial
github llamaindex pycon pycon2024 streamlit
Last synced: 1 day ago
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Streamlit tutorial presentation for PyCon 2024
- Host: GitHub
- URL: https://github.com/tonykipkemboi/streamlit_pycon24_tutorial
- Owner: tonykipkemboi
- Created: 2024-05-09T17:17:02.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-12T19:23:12.000Z (about 1 year ago)
- Last Synced: 2025-09-09T12:53:09.588Z (28 days ago)
- Topics: github, llamaindex, pycon, pycon2024, streamlit
- Language: Python
- Homepage: https://pycon24tutorial.streamlit.app/
- Size: 1.88 MB
- Stars: 8
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Advanced Streamlit for Python Developers: A PyCon US `24 Tutorial
_By [Caroline Frasca](https://us.pycon.org/2024/speaker/profile/89/) and [Tony Kipkemboi](https://us.pycon.org/2024/speaker/profile/90/)_
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](https://docs.streamlit.io/) to get started!
## 🚀 Getting Started
To run the GitHub Repository Analytics Dashboard locally, follow these steps:
1. Clone the repository:
```md
git clone https://github.com/tonykipkemboi/streamlit_pycon24_tutorial.git
```
2. Navigate to the project directory:
```md
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:
```md
OPENAI_API_KEY = "your_api_key_here"
```
4. Install the required dependencies:
```md
pip install -r requirements.txt
```
5. Run the Streamlit app:
```md
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](https://www.llamaindex.ai/?gad_source=1&gclid=CjwKCAjwrvyxBhAbEiwAEg_Kgvh_e5ZuJINu47FgMRntEWXEtO6an_TCqXmVJs0P9XeKUohTtSuexhoCCaIQAvD_BwE) 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](https://discuss.streamlit.io/). We'd love to hear from you! 💬
Happy Streamlit-ing! 🎈