Skip to content

QUICK_START.md

Latest commit

 

History

History
79 lines (53 loc) · 1.92 KB

QUICK_START.md

File metadata and controls

79 lines (53 loc) · 1.92 KB

🚀 Quick Start Guide

Step 1: Prepare Optimized Data

Create a lightweight data file from your raw results:

cd PROMIS_Dashboard
python prepare_dashboard_data.py --source ../results/

This creates results/dashboard_data.parquet (typically 50-100x smaller than raw data).

Step 2: Test Locally

streamlit run app.py

The app will automatically use the optimized data file if it exists.

Step 3: Deploy Online

Easiest Option: Streamlit Cloud (Free)

  1. Push to GitHub:

    git init
git add .
git commit -m "PROMIS Dashboard"
git remote add origin <your-github-repo-url>
git push -u origin main

  2. Deploy:

    • Go to share.streamlit.io
    • Sign in with GitHub
    • Click "New app"
    • Select your repo
    • Main file: app.py
    • Click "Deploy"

  3. Done! Your dashboard will be live in ~2 minutes.

Alternative: Docker Deployment

# Build image
docker build -t promis-dashboard .

# Run locally
docker run -p 8501:8501 promis-dashboard

# Or deploy to any cloud provider that supports Docker

📊 What Gets Deployed?

  • ✅ app.py - Main application
  • ✅ interactive_metrics_viz.py - Standalone visualization code
  • ✅ results/dashboard_data.parquet - Optimized data (if < 100MB, include in git)
  • ✅ requirements.txt - Dependencies

💡 Tips

  • Data size: If dashboard_data.parquet > 100MB, add it to .gitignore and upload separately
  • Updates: Re-run prepare_dashboard_data.py when you have new results
  • Privacy: Ensure your data doesn't contain sensitive information

🆘 Troubleshooting

Import errors? Make sure all files are in the same directory.

Data not loading? Check that results/dashboard_data.parquet exists and is readable.

Deployment fails? Check requirements.txt has all dependencies.

For more details, see README_DEPLOYMENT.md