Edwin Salguero
Prepare for Streamlit Cloud deployment - Add deployment files, fix clustering chart error, update requirements
6ce20d9
| # 🚀 Streamlit Cloud Deployment Checklist | |
| ## ✅ Pre-Deployment Checklist | |
| ### 1. Code Preparation | |
| - [x] `requirements.txt` updated with all dependencies | |
| - [x] `streamlit_app.py` created as main entry point | |
| - [x] `.streamlit/config.toml` configured | |
| - [x] `.env` file in `.gitignore` (security) | |
| - [x] All import paths working correctly | |
| ### 2. GitHub Repository | |
| - [ ] Push all changes to GitHub | |
| - [ ] Ensure repository is public (for free Streamlit Cloud) | |
| - [ ] Verify no sensitive data in repository | |
| ### 3. Environment Variables (Set in Streamlit Cloud) | |
| - [ ] `FRED_API_KEY` - Your FRED API key | |
| - [ ] `AWS_ACCESS_KEY_ID` - Your AWS access key | |
| - [ ] `AWS_SECRET_ACCESS_KEY` - Your AWS secret key | |
| - [ ] `AWS_REGION` - us-east-1 | |
| ## 🚀 Deployment Steps | |
| ### Step 1: Push to GitHub | |
| ```bash | |
| git add . | |
| git commit -m "Prepare for Streamlit Cloud deployment" | |
| git push origin main | |
| ``` | |
| ### Step 2: Deploy to Streamlit Cloud | |
| 1. Go to https://share.streamlit.io/ | |
| 2. Sign in with GitHub | |
| 3. Click "New app" | |
| 4. Repository: `your-username/FRED_ML` | |
| 5. Main file path: `streamlit_app.py` | |
| 6. Click "Deploy" | |
| ### Step 3: Configure Environment Variables | |
| 1. In Streamlit Cloud dashboard, go to your app | |
| 2. Click "Settings" → "Secrets" | |
| 3. Add your environment variables: | |
| ``` | |
| FRED_API_KEY = "your-fred-api-key" | |
| AWS_ACCESS_KEY_ID = "your-aws-access-key" | |
| AWS_SECRET_ACCESS_KEY = "your-aws-secret-key" | |
| AWS_REGION = "us-east-1" | |
| ``` | |
| ### Step 4: Test Your Deployment | |
| 1. Wait for deployment to complete | |
| 2. Visit your app URL | |
| 3. Test all features: | |
| - [ ] Executive Dashboard loads | |
| - [ ] Advanced Analytics works | |
| - [ ] FRED API data loads | |
| - [ ] Visualizations generate | |
| - [ ] Downloads work | |
| ## 🔧 Troubleshooting | |
| ### Common Issues | |
| - **Import errors**: Check `requirements.txt` has all dependencies | |
| - **AWS errors**: Verify environment variables are set correctly | |
| - **FRED API errors**: Check your FRED API key | |
| - **Memory issues**: Streamlit Cloud has memory limits | |
| ### Performance Tips | |
| - Use caching for expensive operations | |
| - Optimize data loading | |
| - Consider using demo data for initial testing | |
| ## 🎉 Success! | |
| Your FRED ML app will be available at: | |
| `https://your-app-name-your-username.streamlit.app` | |
| ## 📊 Features Available in Deployment | |
| - ✅ Real FRED API data integration | |
| - ✅ Advanced analytics and forecasting | |
| - ✅ Professional enterprise-grade UI | |
| - ✅ AWS S3 integration (with credentials) | |
| - ✅ Local storage fallback | |
| - ✅ Comprehensive download capabilities | |
| - ✅ Free hosting with Streamlit Cloud |