Career Path Project

This repository contains the codebase for a Career Path recommendation and guidance system. The project is divided into two main components:

1. ml_backend: A Python-based Machine Learning backend responsible for career clustering and initial recommendations.
2. Cloud & backend: A Python Flask application deployed on Google Cloud Run that acts as an orchestration layer, integrating the ML backend with the Google Gemini API for detailed career guidance and chat functionalities.
3. webapp: A React/Vite frontend application for user interaction, assessment, and displaying career recommendations and chat.

Architecture

The overall architecture involves:

• Users: Interact with the webapp.
• Web Application (webapp): A React/Vite frontend that collects user assessment data and handles chat interactions.
• Cloud & Backend Service (Cloud & backend):
  • Deployed on Google Cloud Run.
  • Receives assessment data and chat queries from the webapp.
  • Calls the ml_backend (deployed on Vertex AI) for career clustering.
  • Calls the Google Gemini API for detailed career guidance and chat responses.
  • Returns combined responses to the webapp.
• ML Backend (ml_backend):
  • Deployed as a Vertex AI endpoint.
  • Processes assessment data to determine career clusters and provides initial recommendations.
• Google Gemini API: Provides advanced natural language capabilities for personalized career guidance and conversational AI.
• Relational DB (e.g., Supabase): Stores user data, assessment results, and chat history (managed by webapp's Supabase Edge Functions or directly by the Cloud & backend in a more integrated setup).
• Auth & IAM: Handles user authentication and authorization.

Project Setup

Prerequisites

• Node.js and npm/bun (for webapp)
• Python 3.9+ and pip (for ml_backend and Cloud & backend)
• Docker
• Google Cloud SDK
• Google Cloud Project with billing enabled
• Google Gemini API Key

Getting Started

1. Clone the repository:

   git clone https://github.com/topspeed69/Career-Path.git
   cd Career-Path

2. ML Backend Setup (ml_backend folder): Refer to the ml_backend/README.md (if it exists, otherwise create one) for instructions on how to set up and deploy the ML model to Google Cloud Vertex AI. Ensure you obtain the endpoint URL for the deployed model.

3. Cloud & Backend Service Setup (Cloud & backend folder): Refer to the Cloud & backend/README.md for instructions on how to set up, run locally, and deploy this Flask application to Google Cloud Run. You will need the Vertex AI endpoint URL and your Gemini API key.

4. Web Application Setup (webapp folder): Refer to the webapp/README.md for instructions on how to set up and run the frontend application. This will involve configuring API endpoints to point to your deployed Cloud Run service.

Development

Each sub-project (ml_backend, Cloud & backend, webapp) has its own README.md with specific development instructions.

Deployment

Deployment instructions for each component are provided in their respective README.md files.

Contributing

Feel free to contribute to this project by submitting issues or pull requests.

License

MIT License