🚀 LifeOS AI

LifeOS AI is a full-stack AI-powered platform that helps users make smarter career decisions by analyzing their skills, identifying gaps, and providing personalized roadmaps along with productivity and burnout insights.

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🌟 Overview

LifeOS AI combines career intelligence, skill analysis, and wellness tracking into a single system.
It is designed to act like a personal AI assistant for professional growth.

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✨ Key Features

🧭 Career GPS

• Generates personalized career paths based on user skills and goals
• Suggests next steps and a learning roadmap

🧠 Skill Gap Predictor

• Identifies missing skills for target roles
• Provides a job readiness score and project recommendations

❤️ Burnout Monitor

• Tracks stress, mood, and work patterns
• Predicts burnout risk and suggests actionable improvements

📄 Resume AI (LLM Powered)

• Parses resumes and extracts key information
• Generates structured career insights using Gemini API

📊 AI Insights Dashboard

• Unified view of career, skills, and productivity data
• Personalized daily recommendations

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🛠️ Tech Stack

Frontend

• Next.js (App Router)
• TypeScript
• Tailwind CSS
• shadcn/ui

Backend

• FastAPI (Python)
• MongoDB Atlas
• JWT Authentication

AI Integration

• Google Gemini API (LLM)
• Resume parsing (PDF processing)

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🧠 How It Works

1. User inputs skills or uploads a resume
2. Backend processes data and extracts meaningful information
3. AI modules analyze:
   • Career direction
   • Skill gaps
   • Productivity patterns
4. System generates structured insights and recommendations
5. Results are stored and displayed via a dashboard

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📁 Project Structure

LifeOS-AI/
│
├── api/
│   ├── app/
│   │   ├── routes/
│   │   ├── services/
│   │   ├── db/
│   │   └── models/
│   └── main.py
│
├── web/
│   ├── src/
│   │   ├── app/
│   │   ├── components/
│   │   └── lib/
│
└── README.md

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⚙️ Setup Instructions

1. Clone Repository

git clone https://github.com/your-username/lifeos-ai.git
cd lifeos-ai

2. Backend Setup

cd api
pip install -r requirements.txt

Create a .env file:

MONGODB_URI=your_mongodb_uri
GEMINI_API_KEY=your_gemini_key
GEMINI_MODEL=gemini-1.5-flash
JWT_SECRET=your_secret

Run the backend:

uvicorn main:app --reload

3. Frontend Setup

cd web
npm install
npm run dev

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🔐 Environment Variables

Variable	Description
MONGODB_URI	MongoDB Atlas connection string
GEMINI_API_KEY	Google Gemini API key
GEMINI_MODEL	LLM model name
JWT_SECRET	Authentication secret

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📌 Future Improvements

• Resume embedding + semantic search
• Real-time job market integration
• ML-based burnout prediction model
• Multi-user analytics dashboard

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🤝 Contribution

Contributions are welcome.