Akhilesh Singh - Professional Portfolio

A dual-focused Streamlit portfolio showcasing DevOps engineering expertise alongside passion for poetry writing and listening.

🚀 Features

• Professional Overview: Career highlights with interactive metrics
• Technical Journey: Interactive timeline with detailed work experience
• Skills Visualization: Charts and proficiency levels across multiple categories
• Poetry Section: Creative content with favorite quotes and reflections
• AI Assistant: Smart search and Q&A for instant information retrieval
• Admin Panel: Content management system for easy updates
• Contact Information: Professional links and contact details

📋 Prerequisites

• Python 3.11 or higher
• pip or uv package manager

🛠️ Local Setup

Quick Setup (Recommended)

Option 1: Automated Setup Script

# Clone repository
git clone <repository-url>
cd portfolio-website

# Run setup script
python setup.py

Option 2: Interactive Deployment Script

# Make script executable
chmod +x deploy.sh

# Run interactive setup
./deploy.sh

# Or use specific commands
./deploy.sh install    # Install dependencies
./deploy.sh both       # Start both applications
./deploy.sh portfolio  # Start portfolio only
./deploy.sh admin      # Start admin panel only

Manual Setup

1. Clone the Repository

git clone <repository-url>
cd portfolio-website

2. Install Dependencies

# Using pip
pip install streamlit pandas plotly

# Check installation
python -c "import streamlit, pandas, plotly; print('All packages installed successfully')"

3. Run the Applications

Main Portfolio

streamlit run app.py --server.port 5000

Visit: http://localhost:5000

Admin Panel

streamlit run admin.py --server.port 5001

Visit: http://localhost:5001

• Password: admin123

Run Both Applications

# In separate terminals
streamlit run app.py --server.port 5000 &
streamlit run admin.py --server.port 5001 &

🌐 Global Deployment

Streamlit Cloud (Recommended)

1. Prepare Repository

   • Push code to GitHub repository
   • Ensure requirements.txt exists with dependencies

2. Deploy to Streamlit Cloud

   • Go to share.streamlit.io
   • Connect your GitHub account
   • Select repository and branch
   • Set main file path: app.py
   • Deploy

3. Admin Panel Deployment

   • Create separate app for admin panel
   • Set main file path: admin.py
   • Deploy to different subdomain

Alternative Deployment Options

Heroku

# Create Procfile
echo "web: streamlit run app.py --server.port=\$PORT --server.address=0.0.0.0" > Procfile

# Deploy
heroku create your-portfolio-app
git push heroku main

Railway

# Install Railway CLI
npm install -g @railway/cli

# Deploy
railway login
railway init
railway up

Docker

Single Container

# Build and run
docker build -t akhilesh-portfolio .
docker run -p 8501:8501 akhilesh-portfolio

Multi-Container with Docker Compose

# Start both portfolio and admin panel
docker-compose up -d

# Access applications
# Portfolio: http://localhost:8501
# Admin Panel: http://localhost:8502

Docker Commands

# Build image
docker build -t portfolio-app .

# Run portfolio only
docker run -p 5000:8501 portfolio-app

# Run with volume for data persistence
docker run -p 5000:8501 -v $(pwd)/data:/app/data portfolio-app

# Stop containers
docker-compose down

📁 Project Structure

portfolio-website/
├── app.py                 # Main portfolio application
├── admin.py              # Admin panel for content management
├── requirements.txt      # Python dependencies
├── .streamlit/
│   └── config.toml       # Streamlit configuration
├── components/           # Reusable UI components
│   ├── career_timeline.py
│   ├── contact_info.py
│   ├── poetry_section.py
│   └── skills_visualization.py
├── data/                 # Portfolio data modules
│   ├── work_experience.py
│   ├── skills_data.py
│   ├── poetry_content.py
│   └── certifications.py
└── README.md

🔧 Configuration

Streamlit Configuration

Located in .streamlit/config.toml:

[server]
headless = true
address = "0.0.0.0"
port = 5000

[theme]
base = "light"

Environment Variables (Optional)

# For production deployment
STREAMLIT_SERVER_PORT=5000
STREAMLIT_SERVER_ADDRESS=0.0.0.0

📝 Content Management

Using Admin Panel

1. Access admin panel at port 5001
2. Login with password: admin123
3. Navigate through tabs to manage:
   • Work Experience
   • Skills & Certifications
   • Poetry Content
   • Data Backup

Manual Content Updates

Edit files in data/ directory:

• work_experience.py - Career history
• skills_data.py - Technical skills and certifications
• poetry_content.py - Poetry quotes and content

After manual edits, restart the application.

🤖 AI Assistant Features

The AI Assistant provides:

• Smart Q&A: Natural language queries about experience and skills
• Skills Search: Find specific technologies and proficiency levels
• Experience Search: Search through job descriptions and responsibilities
• Quick Facts: Instant metrics and statistics

Example queries:

• "What is Akhilesh's current role?"
• "What cloud platforms does he work with?"
• "Tell me about his DevOps skills"
• "How can I contact him?"

🔐 Security

Admin Panel Security

• Change default password in admin.py
• For production, implement proper authentication
• Consider environment variables for sensitive data

Deployment Security

• Use HTTPS in production
• Set proper CORS headers
• Implement rate limiting if needed

🚀 Performance Optimization

Local Development

• Use streamlit run with --server.runOnSave=true for auto-reload
• Enable caching for data loading functions

Production

• Implement Streamlit caching decorators
• Optimize image assets
• Use CDN for static resources

🐛 Troubleshooting

Common Issues

Port Already in Use

# Kill process on port 5000
lsof -ti:5000 | xargs kill -9

# Or use different port
streamlit run app.py --server.port 5001

Module Import Errors

# Ensure all dependencies are installed
pip install -r requirements.txt

# Check Python path
python -c "import streamlit; print('Success')"

Deployment Issues

• Ensure requirements.txt includes all dependencies
• Check Python version compatibility
• Verify file paths are relative

Logs and Debugging

• Check Streamlit logs in terminal
• Use st.write() for debugging
• Enable verbose logging in config

📊 Analytics and Monitoring

Built-in Metrics

• Portfolio views and interactions
• AI Assistant query patterns
• Admin panel usage

External Integration

# Google Analytics (optional)
# Add tracking code to Streamlit app

🔄 Updates and Maintenance

Regular Updates

1. Update portfolio data through admin panel
2. Review and refresh skills proficiency levels
3. Add new certifications and experiences
4. Update poetry content and quotes

Version Control

# Create feature branch
git checkout -b feature/new-content

# Commit changes
git add .
git commit -m "Update portfolio content"

# Deploy
git push origin main

📞 Support

For issues or questions:

• Email: akhileshranjan.ks@gmail.com
• Create GitHub issue for technical problems
• Check Streamlit documentation: https://docs.streamlit.io

📄 License

This project is open source and available under the MIT License.

🏗️ Contributing

1. Fork the repository
2. Create feature branch
3. Make changes
4. Test locally
5. Submit pull request

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Built with ❤️ using Streamlit, Python, and Plotly

"Where cloud architecture meets creative expression"