AI Portfolio Assistant

An intelligent Streamlit app that helps you create professional portfolio content through natural conversation. Chat with the AI to generate comprehensive README-style profiles, project summaries, and learning reflections.

Features

• Personal Bio Generation: Create professional profiles with contact info, skills, and experience
• Project Summaries: Generate detailed project descriptions and technical documentation
• Learning Reflections: Document learning objectives, skills acquired, and future goals
• Live Preview: See your content update in real-time as you chat
• Smart Information Extraction: AI automatically extracts and organizes your professional details

Installation

1. Clone the repository

   git clone <your-repo-url>
   cd ai-portfolio-assistant

2. Install dependencies

   pip install -r requirements.txt

3. Set up environment variables

   Create a .env file in the project root with your Google API key:

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

   Get your Google API key from Google AI Studio.

Usage

1. Run the app

   streamlit run app.py

2. Start chatting

   • Share your professional background naturally
   • Include details about your experience, skills, and projects
   • Watch as the AI generates polished README content in real-time

3. Switch modes

   • Personal Bio: Professional profile and resume content
   • Project Summaries: Technical project documentation
   • Learning Reflections: Educational and skill development content

Requirements

• Python 3.8+
• Google API key for Gemini AI
• Internet connection for AI model access

Project Structure

ai-portfolio-assistant/
├── app.py                    # Main application entry point
├── requirements.txt          # Python dependencies
├── backend/                  # Core logic and AI integration
├── frontend/                 # Streamlit UI components
├── tests/                    # Test suite
├── docs/                     # Deployment documentation
├── configs/                  # Cloud-specific configuration
└── .github/workflows/        # CI/CD pipeline

Development & Testing

Run tests:

python -m pytest tests/ -v

Test coverage:

pytest --cov=backend --cov=frontend tests/

Deployment

This project supports deployment to multiple cloud platforms:

• AWS: See docs/aws_setup.md
• Azure: See docs/azure_setup.md
• CI/CD: See docs/pipeline_setup.md

Automated deployments trigger on pushes to main branch via GitHub Actions.

Team

• AWS Specialist: Handles AWS infrastructure and deployments
• Azure Specialist: Manages Azure infrastructure and deployments
• DevOps Lead: Maintains CI/CD pipeline and monitoring
• QA Engineer: Ensures test coverage and quality standards

Contributing

1. Create feature branch from develop
2. Make changes and add tests
3. Run test suite locally
4. Submit PR to main branch
5. Wait for CI checks to pass
6. Merge after review approval

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Built with Streamlit and Google Generative AI