NeemaMwende/Terminal-portfolio

🖥️ AI-Powered Terminal Portfolio

An interactive, terminal-style portfolio website built with Streamlit, featuring AI-powered responses using RAG (Retrieval-Augmented Generation) and Google's Gemini AI.

✨ Features

• 🎨 Retro Terminal UI - Authentic terminal experience with typing animations
• 🤖 AI-Powered Responses - Intelligent answers using RAG + Gemini 2.5
• ⚡ Fast Loading - Optimized with persistent ChromaDB storage
• 📄 PDF Resume Integration - Automatically extracts info from your resume
• 💾 Smart Caching - Embeddings cached for instant subsequent loads
• 🎯 Interactive Commands - Navigate through skills, projects, experience, and more

🎥 Demo

neema@portfolio:~$ welcome
Hi, I'm Neema Mwende, a Software & AI Engineer.

Welcome to my interactive 'AI powered' portfolio terminal!
Type 'help' to see available commands.

neema@portfolio:~$ help
Available commands:
  about           - Learn about me
  projects        - View my projects
  skills          - See my technical skills
  experience      - My work experience
  contact         - How to reach me
  education       - My educational background
  certifications  - View my certifications
  leadership      - Leadership and community involvement
  clear           - Clear the terminal

📋 Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.12+ (or Python 3.8+)
• pip (Python package manager)
• Git (for cloning the repository)

API Keys Required

You'll need the following API keys:

1. OpenAI API Key - For embeddings (Get it here)
2. Google Gemini API Key - For AI responses (Get it here)

🚀 Installation

1. Clone the Repository

git clone https://github.com/yourusername/terminal-portfolio.git
cd terminal-portfolio

2. Create Virtual Environment

# Create virtual environment
python3 -m venv venv

# Activate virtual environment
# On Linux/Mac:
source venv/bin/activate

# On Windows:
venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

Required packages:

streamlit>=1.28.0
python-dotenv>=1.0.0
google-generativeai>=0.3.0
chromadb>=0.4.0
langchain>=0.1.0
langchain-community>=0.0.10
langchain-openai>=0.0.5
openai>=1.0.0
pypdf>=3.17.0

4. Set Up Environment Variables

Create a .env file in the project root:

touch .env

Add your API keys:

# OpenAI API Key (for embeddings)
OPENAI_API_KEY=your_openai_api_key_here

# Google Gemini API Key (for AI responses)
GEMINI_API_KEY=your_gemini_api_key_here

# Resume PDF path (optional, defaults to resume.pdf)
RESUME_PDF_PATH=resume.pdf

# Terminal customization (optional)
TERMINAL_PROMPT=neema@portfolio:~$
TERMINAL_COLOR=#00ff99
PROMPT_COLOR=#00aaff

5. Add Your Resume

Place your resume PDF in the project root:

# Make sure your resume is named resume.pdf
# Or update RESUME_PDF_PATH in .env
cp /path/to/your/resume.pdf ./resume.pdf

🎮 Usage

Run the Application

streamlit run ui.py

The terminal will open in your browser at http://localhost:8501

First Run (Slower - Embedding)

On the first run, the application will:

1. Load your resume PDF
2. Split it into chunks
3. Generate embeddings using OpenAI
4. Store them in ChromaDB

Expected time: 15-30 seconds

🚀 Initializing MCP Server...
📄 Loading and embedding resume (first time or updated)...
🔄 Generating embeddings...
💾 Storing in database...
✓ Resume embedded and stored (5 chunks)
✓ MCP Server cached and ready!

Subsequent Runs (Fast - Cached)

After the first run, embeddings are cached:

Expected time: 1-2 seconds ⚡

✓ Resume already loaded (5 chunks) - skipping embedding
✓ MCP Server cached and ready!

📁 Project Structure

terminal-portfolio/
├── ui.py                   # Main Streamlit UI with terminal interface
├── mcp_server.py           # MCP server handling commands
├── rag_engine.py           # RAG engine with ChromaDB
├── tools.py                # Command handlers and AI tools
├── ai_engine.py            # AI-powered command suggestions
├── config.py               # Configuration management
├── requirements.txt        # Python dependencies
├── .env                    # Environment variables (create this)
├── resume.pdf              # Your resume PDF (add this)
├── chroma_db/              # ChromaDB persistent storage (auto-created)
│   └── ...
├── README.md               # This file
└── .gitignore              # Git ignore file

🎨 Available Commands

You can also ask custom questions like:

• "What programming languages do you know?"
• "Tell me about your AI projects"
• "What's your most recent job?"

🔧 Troubleshooting

Issue: "OPENAI_API_KEY not found"

Solution: Make sure .env file exists and contains valid API keys:

cat .env  # Check if file exists and has keys

👨‍💻 Author

Neema Mwende

• Email: neemamwende009@gmail.com
• GitHub: @neemamwende
• LinkedIn: Neema Mwende

🙏 Acknowledgments

• Built with Streamlit
• AI powered by Google Gemini
• Embeddings by OpenAI
• Vector storage by ChromaDB
• RAG framework by LangChain

📊 Performance

• First Load: 15-30 seconds (embedding generation)
• Subsequent Loads: 1-2 seconds (cached)
• Response Time: < 1 second (cached queries)
• Memory Usage: ~200-300 MB
• Storage: ~50 MB (ChromaDB cache)