🚀 SkillBridge AI

An AI-powered career coaching platform that generates personalized interview strategies by analyzing job descriptions and user resumes. Built with React, Node.js, MongoDB, and the Google Gemini 2.5 Flash model, featuring a secure cross-origin microservice architecture.

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💡 About This Project

SkillBridge AI was developed to bridge the gap between candidate qualifications and employer expectations. Traditional interview preparation often relies on generic advice, but SkillBridge AI leverages Generative AI (Google Gemini 2.5 Flash) to provide hyper-personalized, actionable interview strategies.

By orchestrating a seamless flow between a React frontend and an Express.js backend, the platform securely parses PDF/DOCX resumes, cross-references them against targeted job descriptions, and outputs a strategic roadmap for the candidate.

Key Technical Achievements:

• Cross-Domain Architecture: Engineered a decoupled system with the frontend hosted on Vercel and the backend on Render, implementing robust CORS policies and secure, SameSite=None cross-origin cookies for JWT authentication.
• Prompt Engineering: Designed optimized, context-aware prompts to extract high-value insights from the Gemini 2.5 Flash model, ensuring low-latency generation of the final reports.
• Secure Data Pipeline: Implemented secure session management with token blacklisting and persistent storage using MongoDB Atlas in the AWS Mumbai region.

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🌐 Live Demo & Dashboard

• Live Application: https://skill-bridge-ai-orpin.vercel.app
• Backend API Base: https://skillbridge-ai-bn9a.onrender.com

(Note: The backend is hosted on Render's free tier. If the application has been inactive, the first request may take 30–50 seconds to "wake up" the server. Subsequent requests will be instant.)

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✨ Features

• 🤖 AI-Powered Interview Strategies: Integrates with Google's Gemini 2.5 Flash model to generate comprehensive, context-aware interview preparation reports.
• 🔐 Secure Authentication: Robust user authentication using JWTs, bcrypt password hashing, and secure, cross-site HTTP-only cookies.
• 📄 Resume Parsing: Seamless resume upload and processing to contextually match candidate skills with job requirements.
• 🌐 Cross-Domain Architecture: Fully separated frontend and backend environments configured for secure cross-origin resource sharing (CORS).
• 📱 Responsive UI: Modern, accessible user interface built with React and Radix UI components.

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🛠️ Tech Stack

Frontend:

• React (Vite)
• Tailwind CSS / Shadcn UI
• Axios (with cross-site credentials)
• Deployed on Vercel

Backend:

• Node.js & Express.js
• MongoDB Atlas (Mongoose)
• Google Generative AI SDK (Gemini API)
• JSON Web Tokens (JWT) & Cookie Parser
• Deployed on Render

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🏗️ Project Architecture

SkillBridge AI utilizes a decoupled, microservice-style architecture ensuring separation of concerns, secure authentication, and scalable AI integration.

                             REST API (HTTPS)       
                            w/ Secure Cookies       
+-------------------+                              +-------------------+
|                   |                              |                   |
|   Frontend (UI)   | <==========================> |  Backend API      |
|  (React / Vercel) |                              | (Express / Render)|
|                   |                              |                   |
+-------------------+                              +-------------------+
                                                      |             |
                        Google Gen AI SDK             |             |  Mongoose (ODM)
                      +-------------------------------+             +------------------+
                      |                                                |
                      v                                                v
            +-------------------+                            +-------------------+
            |                   |                            |                   |
            |    AI Engine      |                            |     Database      |
            | (Gemini 2.5 Flash)|                            | (MongoDB Atlas)   |
            |                   |                            |                   |
            +-------------------+                            +-------------------+

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🚦 Local Development Setup

Follow these steps to get the project running on your local machine.

Prerequisites

• Node.js (v18+ recommended)
• MongoDB Atlas account (or local MongoDB instance)
• Google Gemini API Key

1. Clone the Repository

git clone [https://github.com/yourusername/skillbridge-ai.git](https://github.com/yourusername/skillbridge-ai.git)
cd skillbridge-ai

2. Backend Setup

cd backend
npm install

Create a .env file in the backend directory:

PORT=3000
MONGO_URI=your_mongodb_connection_string
JWT_SECRET=your_secure_random_secret
GOOGLE_GENAI_API_KEY=your_gemini_api_key

Start the backend development server:

npm run dev

3. Frontend Setup

Open a new terminal window/tab:

cd frontend
npm install

Create a .env file in the frontend directory:

VITE_API_BASE_URL=http://localhost:3000

Start the Vite development server:

npm run dev

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🌍 Production Deployment

This project uses a separated deployment strategy.

1.Backend (Render): * Deployed as a Web Service.

*Express is configured with app.set("trust proxy", 1) to handle secure cookies behind Render's proxy.

*CORS is configured to strictly allow the Vercel frontend domains.

2.Frontend (Vercel): * Deployed via Vercel GitHub integration.

*Environment variable VITE_API_BASE_URL points to the live Render backend URL.

Important Production Variables: Ensure backend cookies are set with secure: true and sameSite: "none" for cross-domain authentication to work.

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