Career Path Analyzer API

A FastAPI backend service that uses Google's Vertex AI (Gemini-1.0-pro model) to analyze skills and expertise, generating personalized career paths, roadmaps, and course recommendations.

Features

• POST /analyze endpoint that accepts skills and expertise
• Generates top 3 career paths based on input
• Creates a detailed roadmap for the best matching career path
• Recommends 3-5 relevant courses
• CORS middleware configured for React frontend (localhost:3000)
• Comprehensive error handling and fallback responses
• JSON-only responses (no plain text)

Requirements

• Python 3.10+
• Google Cloud Project with Vertex AI enabled
• Required environment variables

Installation

1. Clone the repository:

git clone <repository-url>
cd gemini2

2. Install dependencies:

pip install -r requirements.txt

3. Set up Google Cloud credentials:

# Set your Google Cloud project ID
export GOOGLE_CLOUD_PROJECT="your-project-id"

# Authenticate with Google Cloud
gcloud auth application-default login

Environment Variables

Create a .env file or set the following environment variable:

GOOGLE_CLOUD_PROJECT=your-google-cloud-project-id

Running the Application

1. Start the development server:

python main.py

Or using uvicorn directly:

uvicorn main:app --reload --host 0.0.0.0 --port 8000

2. The API will be available at http://localhost:8000

3. View the interactive API documentation at http://localhost:8000/docs

API Endpoints

POST /analyze

Analyzes skills and expertise to generate career recommendations.

Request Body:

{
  "skills": "Python, JavaScript, React, Node.js",
  "expertise": "Full-stack web development with 2 years experience"
}

Response:

{
  "career_paths": [
    {
      "title": "Senior Full-Stack Developer",
      "description": "Lead development of complex web applications",
      "required_skills": ["Python", "JavaScript", "React", "Node.js"],
      "salary_range": "$80,000 - $150,000",
      "growth_prospect": "High - Strong demand for full-stack developers"
    }
  ],
  "selected_path": {
    "title": "Senior Full-Stack Developer",
    "description": "Lead development of complex web applications using modern technologies",
    "required_skills": ["Python", "JavaScript", "React", "Node.js", "System Design"],
    "salary_range": "$80,000 - $150,000",
    "growth_prospect": "High - Strong demand for full-stack developers"
  },
  "roadmap": [
    {
      "step": 1,
      "title": "Master Advanced Concepts",
      "description": "Deepen understanding of advanced programming concepts",
      "duration": "3-6 months",
      "resources": ["Advanced tutorials", "Code reviews", "Open source contributions"]
    }
  ],
  "courses": [
    {
      "title": "Advanced React Patterns",
      "provider": "Frontend Masters",
      "duration": "8 weeks",
      "difficulty": "Advanced",
      "url": "https://frontendmasters.com/courses/advanced-react-patterns/"
    }
  ]
}

GET /

Health check endpoint.

Response:

{
  "message": "Career Path Analyzer API is running"
}

GET /health

Detailed health check endpoint.

Response:

{
  "status": "healthy",
  "service": "career-analyzer"
}

Project Structure

gemini2/
├── main.py                    # FastAPI application entry point
├── requirements.txt           # Python dependencies
├── models/                    # Pydantic models
│   ├── __init__.py
│   └── schemas.py            # Request/Response schemas
├── services/                  # Business logic services
│   ├── __init__.py
│   └── ai_service.py         # Vertex AI integration
├── routes/                    # API route handlers
│   ├── __init__.py
│   ├── analyze.py            # Career analysis endpoints
│   └── health.py             # Health check endpoints
├── config/                    # Configuration settings
│   ├── __init__.py
│   └── settings.py        # Application settings
├── frontend/                   # React frontend application
│   ├── package.json          # Frontend dependencies
│   ├── tailwind.config.js    # Tailwind CSS configuration
│   ├── postcss.config.js     # PostCSS configuration
│   ├── public/               # Static assets
│   │   └── index.html        # HTML template
│   └── src/                  # React source code
│       ├── App.js            # Main App component
│       ├── App.css           # Custom styles
│       ├── index.js          # React entry point
│       ├── index.css         # Global styles
│       ├── components/       # Reusable components
│       │   └── Navbar.js     # Navigation component
│       ├── pages/            # Page components
│       │   ├── Landing.js    # Landing page
│       │   ├── Dashboard.js  # Dashboard page
│       │   ├── CareerPath.js # Career paths page
│       │   ├── Roadmap.js    # Roadmap page
│       │   ├── Courses.js    # Courses page
│       │   └── Settings.js   # Settings page
│       ├── context/          # React Context
│       │   └── AppContext.js # Global state management
│       └── services/         # API services
│           └── api.js        # Backend API integration
└── README.md                 # This file

Dependencies

Backend Dependencies

• fastapi: Web framework for building APIs
• uvicorn: ASGI server for running FastAPI
• google-cloud-aiplatform: Google Cloud AI Platform client
• pydantic: Data validation using Python type annotations
• python-multipart: Support for multipart form data

Frontend Dependencies

• react: JavaScript library for building user interfaces
• react-router-dom: Declarative routing for React
• axios: Promise-based HTTP client
• tailwindcss: Utility-first CSS framework
• autoprefixer: PostCSS plugin to parse CSS and add vendor prefixes
• postcss: Tool for transforming CSS with JavaScript

Error Handling

The API includes comprehensive error handling:

• 500 Internal Server Error: When Vertex AI generation fails, a fallback response is provided
• 422 Validation Error: When request data doesn't match the expected schema
• Fallback Response: If AI generation fails, the API returns a generic but useful response

CORS Configuration

The API is configured to accept requests from:

• http://localhost:3000 (React development server)

To modify CORS settings, update the allow_origins list in main.py.

Development

Backend Development

To run the backend in development mode with auto-reload:

# Install dependencies
pip install -r requirements.txt

# Set environment variables
export GOOGLE_CLOUD_PROJECT="your-project-id"

# Run the server
uvicorn main:app --reload --host 0.0.0.0 --port 8000

Frontend Development

To run the frontend in development mode:

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

# Start development server
npm start

The frontend will be available at http://localhost:3000 and will automatically connect to the backend API.

Frontend Features

The React frontend includes:

Pages

• Landing: Input form for skills and expertise analysis
• Dashboard: Overview of career analysis results
• Career Path: Detailed view of all recommended career paths
• Roadmap: Step-by-step career development guide
• Courses: Curated learning resources and courses
• Settings: User profile and application preferences

Key Features

• Responsive Design: Mobile-first approach with Tailwind CSS
• Global State Management: React Context for data sharing across components
• API Integration: Axios-based service for backend communication
• Error Handling: Comprehensive error states and user feedback
• Loading States: Visual feedback during API calls
• Navigation: React Router for seamless page transitions

Modular Architecture

The project follows a clean, modular architecture:

Backend

• models/: Contains all Pydantic models for request/response validation
• services/: Contains business logic and external service integrations
• routes/: Contains API route handlers organized by functionality
• config/: Contains application settings and configuration
• main.py: Application entry point that ties everything together

Frontend

• components/: Reusable UI components
• pages/: Page-level components for different routes
• context/: React Context for global state management
• services/: API service layer for backend communication

This structure makes the codebase:

• Maintainable: Easy to find and modify specific functionality
• Testable: Each module can be tested independently
• Scalable: Easy to add new features without affecting existing code
• Readable: Clear separation of concerns

Production Deployment

For production deployment, consider:

1. Using a production ASGI server like Gunicorn with Uvicorn workers
2. Setting up proper logging
3. Configuring environment variables securely
4. Setting up monitoring and health checks
5. Using a reverse proxy like Nginx

Troubleshooting

Common Issues

1. Google Cloud Authentication: Ensure you're authenticated with gcloud auth application-default login
2. Project ID: Make sure GOOGLE_CLOUD_PROJECT is set correctly
3. Vertex AI Access: Ensure Vertex AI is enabled in your Google Cloud project
4. CORS Issues: Check that your frontend URL matches the allowed origins

Logs

The application logs errors to the console. Check the terminal output for detailed error messages.

License

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