Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/elizabethsiegle/wnba-analytics-dash-ai-insights
Generate/view WNBA stats, analytics, player comparison, chatbot, and AI insights
https://github.com/elizabethsiegle/wnba-analytics-dash-ai-insights
cloudflare cloudflare-workers cloudflare-workers-ai folium folium-python langchain langchain-app langchain-python pandas plotly plotly-python streamlit wnba-analytics wnba-stats workers-ai
Last synced: 9 days ago
JSON representation
Generate/view WNBA stats, analytics, player comparison, chatbot, and AI insights
- Host: GitHub
- URL: https://github.com/elizabethsiegle/wnba-analytics-dash-ai-insights
- Owner: elizabethsiegle
- Created: 2024-10-14T22:02:17.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-10-16T20:16:34.000Z (4 months ago)
- Last Synced: 2024-10-19T10:03:17.211Z (4 months ago)
- Topics: cloudflare, cloudflare-workers, cloudflare-workers-ai, folium, folium-python, langchain, langchain-app, langchain-python, pandas, plotly, plotly-python, streamlit, wnba-analytics, wnba-stats, workers-ai
- Language: Python
- Homepage: https://wnba-analytics-ai-insights.streamlit.app/
- Size: 104 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# WNBA Analytics Dashboard with AI Insights
## Overview
This project is an interactive WNBA (Women's National Basketball Association) analytics dashboard powered by Streamlit, LangChain, and Cloudflare Workers AI. It provides comprehensive statistics, player comparisons, and AI-driven insights about WNBA teams and players.
## Features
- Player statistics data visualizations
- Player vs Player comparisons
- Interactive map showing WNBA team locations
- AI-powered chatbot for WNBA-related queries powered by Cloudflare Workers AI and LangChain
- Data filtering and sorting capabilities
## Technologies Used
- Python
- Streamlit
- LangChain
- Cloudflare Workers AI
- Pandas for data manipulation
- Plotly for interactive charts
- Folium for map visualizations
## Getting Started
### Prerequisites
- Python 3.7+
- Cloudflare account--you'll need an account ID and Workers AI API token to run the app locally
### Installation
1. Clone the repository:
```
git clone https://github.com/elizabethsiegle/wnba-analytics-dash-ai-insights.git
cd wnba-analytics-dash-ai-insights
```
2. Install required packages:
```
pip install -r requirements.txt
```
3. Set up environment variables:
Create a `.env` file in the project root and add your Cloudflare credentials:
```
CF_ACCOUNT_ID=your_account_id
CF_AUTH_TOKEN=your_api_token
```
### Running the App
1. Start the Streamlit app:
```
streamlit run app.py
```
2. Open your web browser and navigate to whatever port it says the app is running on
## Usage
- Use the sidebar to filter data and select different views
- Interact with the charts to explore WNBA statistics
- Use the AI chatbot to ask questions about WNBA teams and players