DevFolio AI - Portfolio Assistant

AI-powered tool for generating professional bios, project summaries, and learning reflections using Google's Gemini AI.

🚀 Features

• Interactive Chat Interface: Natural conversation with AI to build your portfolio content
• Multiple Content Types: Generate bios, project descriptions, skills summaries, and more
• Context-Aware: Maintains conversation history for coherent, personalized responses
• Modern UI: Clean, responsive interface built with Streamlit

📋 Prerequisites

• Python 3.10 or higher
• Google AI API Key (Get one here)
• Git (optional)

🛠️ Local Development Setup

1. Clone the Repository

git clone https://github.com/dev-api-org/ai-portfolio-assistant.git
cd ai-portfolio-assistant

2. Create Virtual Environment

Windows (PowerShell):

py -m venv .venv
.\.venv\Scripts\Activate

macOS/Linux:

python3 -m venv .venv
source .venv/bin/activate

3. Install Dependencies

# Upgrade pip (recommended)
python -m pip install --upgrade pip

# Install all dependencies
pip install -r requirements.txt

4. Configure Environment Variables

# Copy the example file
cp .env.example .env

# Edit .env and add your Google API key
# GOOGLE_API_KEY=your_actual_api_key_here

5. Run the Application

streamlit run frontend/streamlit_chat_canvas.py

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

☁️ Streamlit Cloud Deployment

Step 1: Prepare Your Repository

Ensure these files are in your repository:

• ✅ requirements.txt (unified dependencies)
• ✅ backend/__init__.py (makes backend a Python package)
• ✅ .streamlit/config.toml (Streamlit configuration)
• ✅ .env.example (template for local development)
• ✅ .streamlit/secrets.toml.example (template for cloud secrets)

Step 2: Deploy to Streamlit Cloud

1. Sign in to Streamlit Cloud
2. Click "New app"
3. Configure your app:
   • Repository: dev-api-org/ai-portfolio-assistant
   • Branch: main
   • Main file path: frontend/streamlit_chat_canvas.py
4. Click "Advanced settings"
5. Set Python version: 3.10 or higher

Step 3: Configure Secrets

In Streamlit Cloud, go to App Settings > Secrets and add:

GOOGLE_API_KEY = "your_google_api_key_here"
MODEL_NAME = "gemini-2.0-flash-exp"
MODEL_TEMPERATURE = "0.7"

Step 4: Deploy

Click "Deploy" and wait for the build to complete.

🔒 Security Best Practices

⚠️ NEVER commit sensitive data:

• ❌ .env file with real API keys
• ❌ .streamlit/secrets.toml with real secrets
• ✅ Use .env.example and .streamlit/secrets.toml.example as templates

Environment Variables

The app uses the following environment variables:

Variable	Required	Default	Description
GOOGLE_API_KEY	✅ Yes	-	Your Google AI API key
MODEL_NAME	No	gemini-2.0-flash-exp	Gemini model to use
MODEL_TEMPERATURE	No	0.7	Model creativity (0.0-1.0)
GLOBAL_SYSTEM_PROMPT	No	From config	Custom system prompt

📁 Project Structure

ai-portfolio-assistant/
├── backend/
│   ├── __init__.py          # Makes backend a package
│   ├── chat_core.py         # Core chat logic
│   ├── config.py            # Configuration management
│   ├── session_memory.py    # Session state management
│   ├── prompts.json         # Prompt templates
│   └── systemprompts.json   # System prompts
├── frontend/
│   ├── components/          # Reusable UI components
│   ├── img/                 # Images and assets
│   ├── pages/               # Additional pages
│   ├── streamlit_chat_canvas.py  # Main app
│   └── utils.py             # Utility functions
├── .streamlit/
│   ├── config.toml          # Streamlit configuration
│   └── secrets.toml.example # Secrets template
├── requirements.txt         # Python dependencies
├── .env.example             # Environment template
├── .gitignore              # Git ignore rules
└── README.md               # This file

🧪 Testing

To test the backend independently:

python backend/llm_service.py

This runs a terminal-based chat to verify your API connection.

🐛 Troubleshooting

ModuleNotFoundError: No module named 'backend'

Solution: Ensure backend/__init__.py exists (should be an empty file).

API Key Errors

Solution:

1. Verify your API key at Google AI Studio
2. Check that GOOGLE_API_KEY is set in .env (local) or Streamlit secrets (cloud)
3. Ensure no extra spaces or quotes in the key

Streamlit Cloud Build Fails

Solution:

1. Check that requirements.txt is in the repository root
2. Verify all imports use proper package structure (from backend import ...)
3. Review build logs in Streamlit Cloud dashboard

📝 Notes

• The app maintains conversation history per session
• Session data is stored in memory (resets on restart)
• For production, consider adding persistent storage
• Rate limits apply based on your Google AI API tier

🤝 Contributing

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

📄 License

This project is for educational and portfolio purposes.