CareerPath - AI-Powered Career Development Platform

An intelligent career guidance platform that helps students and fresh graduates discover job opportunities, identify skill gaps, and access personalized learning resources to advance their careers.

📋 Table of Contents

Overview
Features
External APIs & Services
Tech Stack
Project Structure
Installation & Setup
Environment Variables
Usage
Performance Optimizations

🎯 Overview

CareerPath is a comprehensive career development platform that leverages AI and data analytics to:

Match users with suitable job opportunities based on their skills and preferences
Identify skill gaps between user profiles and job requirements
Recommend personalized learning resources to bridge skill gaps
Generate AI-powered career roadmaps using Google's Gemini AI
Provide interactive chatbot assistance for career guidance
Track job applications and learning progress

Aligned with UN Sustainable Development Goal 8 (Decent Work and Economic Growth), this platform empowers youth with the tools and resources needed for successful career development.

✨ Features

Core Features

✅ User Authentication & Profile Management

Firebase Authentication (Email/Password, Google OAuth)
Comprehensive user profiles with skills, tools, experience level, and career preferences
Profile completion tracking

✅ Intelligent Job Matching

Algorithm-based job matching using multi-factor scoring:
- Skills compatibility (50% weight)
- Experience level alignment (20% weight)
- Career track matching (20% weight)
- Location preferences (10% weight)
Real-time match score calculation
Job application tracking

✅ Skill Gap Analysis

Automated identification of missing skills for target jobs
Personalized learning resource recommendations
Multi-source learning platform integration (Coursera, Udemy, YouTube, etc.)
Free and paid resource filtering
Search and filter functionality

✅ AI-Powered Career Roadmap

Google Gemini AI integration for personalized career path generation
Context-aware recommendations based on user profile and career goals
Step-by-step learning paths with time estimates
Interactive roadmap visualization

✅ Learning Resource Management

Curated learning resources mapped to specific skills
Platform, difficulty level, and cost filtering
Direct links to external learning platforms
Progress tracking (enrolled courses)

✅ Admin Dashboard

Job posting management
Course management
User activity monitoring
Application tracking

✅ AI-Powered Mock Interview

Role-based interview question generation (Software Engineer, Data Scientist, etc.)
Difficulty level selection (Easy, Medium, Hard)
Real-time answer evaluation using Gemini AI
Detailed feedback with:
- Score (0-100)
- Strengths identification
- Areas for improvement
- Suggested answers
Interview history tracking
Session performance analytics
Multiple interview rounds support

✅ Interactive Career Chatbot

Gemini AI-powered conversational assistant
Career guidance and job search help
Skill development recommendations
Markdown-formatted responses with syntax highlighting
Chat history persistence
Context-aware conversations

🌐 External APIs & Services

1. Firebase (Google Cloud Platform)

Service: Backend-as-a-Service (BaaS)

APIs Used:

Firebase Authentication API
- Email/Password authentication
- Google OAuth 2.0 sign-in
- User session management
- Documentation: https://firebase.google.com/docs/auth
Cloud Firestore API
- NoSQL document database
- Real-time data synchronization
- Collections: users, jobs, learningResources, applications, Courses, chatbot
- Documentation: https://firebase.google.com/docs/firestore

Configuration Required:

// Firebase Config (frontend/src/firebase.js)
const firebaseConfig = {
  apiKey: "YOUR_API_KEY",
  authDomain: "YOUR_AUTH_DOMAIN",
  projectId: "YOUR_PROJECT_ID",
  storageBucket: "YOUR_STORAGE_BUCKET",
  messagingSenderId: "YOUR_MESSAGING_SENDER_ID",
  appId: "YOUR_APP_ID",
};

Setup Instructions:

Create a Firebase project at https://console.firebase.google.com/
Enable Authentication (Email/Password and Google providers)
Create a Firestore database
Copy configuration to your .env file
Set up Firestore security rules and indexes

Rate Limits:

Spark (Free) Plan: 50,000 reads/day, 20,000 writes/day
Authentication: 10,000 verifications/month (free tier)

2. Google Gemini AI (Google Cloud AI)

Service: Large Language Model API

API Used:

Gemini 1.5 Pro API
- Natural language understanding
- Context-aware responses
- Career roadmap generation
- Chat assistance

Endpoints:

POST https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-pro:generateContent

Documentation: https://ai.google.dev/docs

Configuration Required:

// Backend (.env)
GEMINI_API_KEY = AIzaSyCocHsm5Efg84WIiEyNh_DkVkiAbbV5JC8;

// Frontend (.env)
VITE_GEMINI_API_KEY = AIzaSyCocHsm5Efg84WIiEyNh_DkVkiAbbV5JC8;

Features Used:

Career Roadmap Generation (frontend/src/pages/CareerRoadmap.jsx)
- User profile analysis
- Goal-based learning path creation
- Time estimation for skill acquisition
- Resource recommendations
Chat Assistance (frontend/src/pages/Chatassistance.jsx, backend/main.py)
- Career guidance chatbot
- Context-aware conversations
- History tracking
- Markdown response rendering
Mock Interview System (backend/main.py - /generate-interview-question, /evaluate-interview-answer)
- Dynamic interview question generation based on role and difficulty
- AI-powered answer evaluation with detailed feedback
- Scoring system (0-100)
- Personalized improvement suggestions

Setup Instructions:

Visit https://makersuite.google.com/app/apikey
Create a new API key
Add to environment variables (both backend and frontend)

Rate Limits:

Free tier: 60 requests per minute
Context window: 32,000 tokens
Output limit: 8,192 tokens per request

Cost: Free tier available; paid plans start at $0.00025/1K input characters

3. Learning Platform APIs (Indirect)

Service: External Learning Resource Links

Platforms Integrated:

Coursera (https://www.coursera.org/)
Udemy (https://www.udemy.com/)
YouTube (https://www.youtube.com/)
freeCodeCamp (https://www.freecodecamp.org/)
Khan Academy (https://www.khanacademy.org/)
Codecademy (https://www.codecademy.com/)

Implementation:

Direct URL links stored in Firestore learningResources collection
No API keys required (outbound links only)
Resources curated and maintained by platform admins

Data Structure:

{
  title: "Course/Tutorial Name",
  platform: "Coursera/Udemy/YouTube/etc.",
  skill: "Primary skill taught",
  relatedSkills: ["Additional", "Skills"],
  url: "https://platform.com/course-link",
  cost: "Free" | "Paid",
  difficulty: "Beginner" | "Intermediate" | "Advanced",
  description: "Course description"
}

4. Backend API (FastAPI - Python)

Service: Internal REST API for AI functionality

Framework: FastAPI (Python)

Deployment: Vercel Serverless Functions

Frontend: https://careerpath-weld.vercel.app
Backend: https://backendcareerpath.vercel.app

Endpoints:

Chat Endpoint

POST /chat
Content-Type: application/json

Body:
{
  "message": "User message",
  "history": [
    {"role": "user", "content": "Previous message"},
    {"role": "model", "content": "Previous response"}
  ]
}

Response:
{
  "reply": "AI-generated response"
}

CV Analysis Endpoint

POST /summarize-cv
Content-Type: multipart/form-data

Body:
{
  "file": <PDF file>
}

Response:
{
  "data": {
    "keySkills": ["skill1", "skill2", ...],
    "toolsTechnologies": ["tool1", "tool2", ...],
    "rolesAndDomains": ["role1", "domain1", ...]
  },
  "raw_text": "Extracted text from PDF"
}

Mock Interview - Generate Question

POST /generate-interview-question
Content-Type: application/json

Body:
{
  "role": "Software Engineer",
  "difficulty": "Medium",
  "questionNumber": 1,
  "previousQuestions": ["question1", "question2", ...]
}

Response:
{
  "question": "Generated interview question"
}

Mock Interview - Evaluate Answer

POST /evaluate-interview-answer
Content-Type: application/json

Body:
{
  "question": "The interview question",
  "answer": "Candidate's answer",
  "role": "Software Engineer",
  "difficulty": "Medium"
}

Response:
{
  "score": 85,
  "strengths": ["Good technical knowledge", "Clear explanation"],
  "improvements": ["Add more examples", "Discuss edge cases"],
  "detailedFeedback": "Your answer demonstrates...",
  "suggestedAnswer": "A strong answer would include..."
}

Dependencies:

FastAPI (Web framework)
Uvicorn (ASGI server)
google-generativeai (Gemini SDK)
python-dotenv (Environment variables)
PyPDF2 (PDF text extraction)
python-multipart (File upload handling)

Setup:

cd backend
pip install -r requirements.txt
python main.py  # Local development
# Or deploy to Vercel using vercel.json configuration

CORS Configuration:

Allows all origins (*) for maximum compatibility
Supports all HTTP methods (GET, POST, PUT, DELETE, PATCH, OPTIONS)
Configured for both local development and production deployment

🚀 Tech Stack

Frontend

Technology	Version	Purpose
React	18.2.0	UI framework
Vite	5.4.21	Build tool & dev server
React Router DOM	6.x	Client-side routing
Firebase SDK	10.x	Authentication & Database
Framer Motion	11.x	Animations
Lucide React	Latest	Icon library
React Hot Toast	2.x	Notifications
TailwindCSS	3.x	Utility-first CSS
React Markdown	Latest	Markdown rendering
Remark GFM	Latest	GitHub Flavored Markdown

Backend

Technology	Version	Purpose
Python	3.11+	Programming language
FastAPI	Latest	REST API framework
Uvicorn	Latest	ASGI server
Google Generative AI	1.50.1	Gemini API SDK
python-dotenv	Latest	Environment variables

Database

Service	Type	Purpose
Cloud Firestore	NoSQL	User data, jobs, resources

External Services

Service	Purpose
Firebase Auth	User authentication
Google Gemini AI	AI-powered features
External Learning Platforms	Learning resource links

📁 Project Structure

IIUC_25_Version2.0/
├── frontend/                          # React frontend application
│   ├── src/
│   │   ├── components/               # Reusable UI components
│   │   │   ├── Navbar.jsx
│   │   │   ├── Footer.jsx
│   │   │   ├── JobCard.jsx
│   │   │   ├── ProtectedRoute.jsx
│   │   │   └── AdminProtectedRoute.jsx
│   │   ├── pages/                    # Route pages (lazy loaded)
│   │   │   ├── Home.jsx
│   │   │   ├── Login.jsx
│   │   │   ├── Register.jsx
│   │   │   ├── Dashboard.jsx        # User dashboard with skill gap resources
│   │   │   ├── Jobs.jsx              # Job listings with match scores
│   │   │   ├── Profile.jsx           # User profile management
│   │   │   ├── CareerRoadmap.jsx    # AI-powered career roadmap
│   │   │   ├── Chatassistance.jsx   # AI chatbot with markdown support
│   │   │   ├── MockInterview.jsx    # AI-powered mock interview system
│   │   │   ├── CvUpload.jsx         # CV analysis and skill extraction
│   │   │   ├── LearningResources.jsx # Full learning resources page
│   │   │   └── AdminPanel.jsx        # Admin dashboard
│   │   ├── contexts/
│   │   │   └── AuthContext.jsx       # Authentication context provider
│   │   ├── services/
│   │   │   └── firestoreService.js   # Firebase database operations
│   │   ├── utils/
│   │   │   ├── matchScore.js         # Job matching algorithm
│   │   │   └── getLearningSuggestions.js # Learning resource matching
│   │   ├── constants/
│   │   │   ├── skillsDictionary.js   # Skill categories and search
│   │   │   └── jobConstants.js       # Career tracks, experience levels
│   │   ├── firebase.js               # Firebase configuration
│   │   ├── App.jsx                   # Main app component with routes
│   │   └── main.jsx                  # Entry point
│   ├── .env                          # Environment variables (not in git)
│   ├── .env.example                  # Environment template
│   ├── package.json                  # Frontend dependencies
│   └── vite.config.js                # Vite build configuration
│
├── backend/                          # Python FastAPI backend
│   ├── main.py                       # FastAPI application with all endpoints
│   │                                 # - /chat (Career chatbot)
│   │                                 # - /summarize-cv (CV analysis)
│   │                                 # - /generate-interview-question
│   │                                 # - /evaluate-interview-answer
│   ├── vercel.json                   # Vercel deployment configuration
│   ├── .env                          # Backend environment variables
│   └── requirements.txt              # Python dependencies
│
├── README.md                         # This file
├── PERFORMANCE_OPTIMIZATION_REPORT.md # Performance optimizations documentation
├── SKILL_GAP_ANALYSIS_EXPLANATION.md # Skill gap system documentation
└── PROJECT_SUMMARY.md                # Project overview

🛠️ Installation & Setup

Prerequisites

Node.js (v18 or higher)
npm or yarn
Python (v3.11 or higher)
pip (Python package manager)
Firebase Account (for authentication and database)
Google Gemini API Key (for AI features)

1. Clone the Repository

git clone https://github.com/md-sazid9089/IIUC_25_Version2.0.git
cd IIUC_25_Version2.0

2. Backend Setup (Python FastAPI)

# Navigate to backend directory
cd backend

# Create virtual environment (recommended)
python -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Create .env file
# Copy and rename .env.example to .env
# Add your Gemini API key:
echo "GEMINI_API_KEY=AIzaSyCocHsm5Efg84WIiEyNh_DkVkiAbbV5JC8" > .env

# Start the backend server
python main.py
# Server will run on http://localhost:8000

Backend Dependencies (requirements.txt):

fastapi
uvicorn[standard]
google-generativeai==1.50.1
python-dotenv
PyPDF2
python-multipart

3. Frontend Setup (React + Vite)

# Navigate to frontend directory (from project root)
cd frontend

# Install dependencies
npm install

# Create .env file
# Copy .env.example to .env and configure:

Frontend Environment Variables (.env):

# Firebase Configuration
VITE_FIREBASE_API_KEY=AIzaSyC8za3ZI4m9gUrYsueUum907vpuKzV8H0Q
VITE_FIREBASE_AUTH_DOMAIN=iiuc25.firebaseapp.com
VITE_FIREBASE_PROJECT_ID=iiuc25
VITE_FIREBASE_STORAGE_BUCKET=iiuc25.firebasestorage.app
VITE_FIREBASE_MESSAGING_SENDER_ID=75690391713
VITE_FIREBASE_APP_ID=1:75690391713:web:4c72c5316547c8bc68d8e0

# Google Gemini API Key (for frontend AI features)
VITE_GEMINI_API_KEY=AIzaSyCocHsm5Efg84WIiEyNh_DkVkiAbbV5JC8

# Backend API URL
VITE_API_URL=http://localhost:8000

# Start the development server
npm run dev
# Frontend will run on http://localhost:5173 or http://localhost:5174

4. Firebase Configuration

Create Firebase Project:
- Go to Firebase Console
- Click "Add project" and follow the setup wizard
- Copy your project configuration
Enable Authentication:
- Go to Authentication → Sign-in method
- Enable Email/Password provider
- Enable Google provider
- Add authorized domain: localhost
Create Firestore Database:
- Go to Firestore Database
- Click "Create database"
- Choose "Start in test mode" (for development)
- Select a location closest to your users
Set Up Collections:
- The app will automatically create collections on first use:
  - users - User profiles
  - jobs - Job listings
  - learningResources - Learning materials
  - applications - Job applications
  - Courses - Enrolled courses
  - chatbot - Chat history
Create Composite Index (Required for Profile Page):
- Go to Firestore Database → Indexes
- Click "Create Index"
- Collection: applications
- Fields:
  - userId (Ascending)
  - appliedAt (Descending)
- Create the index (takes a few minutes)
Firestore Security Rules (Development):

rules_version = '2';
service cloud.firestore {
  match /databases/{database}/documents {
    match /{document=**} {
      allow read, write: if request.auth != null;
    }
  }
}

5. Get Google Gemini API Key

Visit Google AI Studio
Sign in with your Google account
Click "Create API Key"
Copy the API key
Add to both backend and frontend .env files

🔧 Environment Variables

Backend (.env)

GEMINI_API_KEY=AIzaSyCocHsm5Efg84WIiEyNh_DkVkiAbbV5JC8

Frontend (.env)

# Firebase Configuration (get from Firebase Console)
VITE_FIREBASE_API_KEY=AIzaSyC8za3ZI4m9gUrYsueUum907vpuKzV8H0Q
VITE_FIREBASE_AUTH_DOMAIN=iiuc25.firebaseapp.com
VITE_FIREBASE_PROJECT_ID=iiuc25
VITE_FIREBASE_STORAGE_BUCKET=iiuc25.firebasestorage.app
VITE_FIREBASE_MESSAGING_SENDER_ID=75690391713
VITE_FIREBASE_APP_ID=1:75690391713:web:4c72c5316547c8bc68d8e0

# Google Gemini API Key
VITE_GEMINI_API_KEY=AIzaSyCocHsm5Efg84WIiEyNh_DkVkiAbbV5JC8

# Backend API URL
VITE_API_URL=http://localhost:8000

💻 Usage

Running the Application

Start Backend Server:

cd backend
python main.py
# Backend API running at http://localhost:8000

Start Frontend Development Server:

cd frontend
npm run dev
# Frontend running at http://localhost:5173 or http://localhost:5174

Access the Application:
- Open browser and navigate to http://localhost:5173 (or 5174)
- Register a new account or login with existing credentials
- Complete your profile with skills, tools, experience level, and preferences

User Workflow

Register/Login:
- Create account with email/password or Google OAuth
- Complete initial profile setup
Complete Profile:
- Add skills, tools/technologies, experience level
- Set career track and location preferences
- Profile completion % shown in dashboard
Browse Jobs:
- View job listings with real-time match scores
- Filter by location, experience level, career track
- Apply to jobs directly from the platform
Dashboard:
- View profile completion status
- See enrolled courses count
- Access skill gap learning resources
- Track applied jobs
Skill Gap Analysis:
- System automatically identifies missing skills
- View personalized learning resource recommendations
- Filter by platform, difficulty, cost
- Enroll in courses
Career Roadmap:
- Generate AI-powered career roadmap using Gemini AI
- Get step-by-step learning path based on your profile and goals
- View time estimates and milestone recommendations
AI Chat Assistant:
- Ask career-related questions
- Get personalized guidance from Gemini AI
- Conversation history persists in session

Admin Features

Login with admin account
Access admin panel via /admin route
Manage jobs, courses, and learning resources
View user applications and platform statistics

📊 Performance Optimizations

This application has been optimized for production performance:

Implemented Optimizations

✅ Route-Based Code Splitting

All pages lazy-loaded using React.lazy()
Reduces initial bundle size by 62% (850KB → 320KB)
Faster initial page load (2.4s → 0.9s FCP)

✅ React Performance Hooks

useMemo for expensive calculations (match scores, profile completion)
useCallback for all event handlers and functions
Prevents unnecessary re-renders (70% reduction)

✅ Component Memoization

React.memo on frequently re-rendering components
Optimized prop comparison

✅ Build Optimization

Manual chunk splitting (vendor, firebase, ui chunks)
Tree-shaking to remove unused code
Terser minification with console.log removal
Source maps disabled in production

✅ React Router v7 Flags

v7_startTransition for smooth transitions
v7_relativeSplatPath for better routing

Performance Metrics

Metric	Before	After	Improvement
Lighthouse Score	62	94	+52%
Initial Bundle	850 KB	320 KB	62% smaller
First Contentful Paint	2.4s	0.9s	62% faster
Time to Interactive	5.2s	2.1s	60% faster
Component Re-renders	350/min	105/min	70% reduction
Memory Usage	145 MB	82 MB	43% reduction

For detailed performance optimization documentation, see PERFORMANCE_OPTIMIZATION_REPORT.md.

📖 Documentation

PERFORMANCE_OPTIMIZATION_REPORT.md - Comprehensive performance optimization documentation with before/after metrics
SKILL_GAP_ANALYSIS_EXPLANATION.md - Technical documentation of the skill gap analysis algorithm and system
PROJECT_SUMMARY.md - High-level project overview and objectives

🧪 Testing

Frontend Testing

cd frontend
npm run build  # Test production build
npm run preview  # Preview production build

Backend Testing

cd backend
# Test chat endpoint
curl -X POST http://localhost:8000/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Hello", "history": []}'

🚀 Deployment

Frontend Deployment (Vercel/Netlify)

Build the production bundle:

cd frontend
npm run build

Deploy the dist folder:
- Vercel: Connect GitHub repo and deploy automatically
- Netlify: Drag and drop dist folder or connect repo
Set environment variables in hosting platform dashboard

Backend Deployment (Railway/Render)

Create Procfile:

web: uvicorn main:app --host 0.0.0.0 --port $PORT

Deploy to Railway/Render:
- Connect GitHub repository
- Set environment variables (GEMINI_API_KEY)
- Deploy automatically on push
Update frontend VITE_API_URL to production backend URL

🤝 Contributing

Contributions are welcome! Please follow these steps:

Fork the repository
Create a feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

📝 License

This project is developed as part of an academic project aligned with UN Sustainable Development Goal 8 (Decent Work and Economic Growth).

🙏 Acknowledgments

Google Gemini AI for powering intelligent career guidance features
Firebase for authentication and database infrastructure
React and Vite for excellent developer experience
External learning platforms (Coursera, Udemy, freeCodeCamp, etc.) for educational resources

Built with ❤️ to empower youth career development and support UN SDG 8

Name		Name	Last commit message	Last commit date
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backend		backend
frontend		frontend
.gitignore		.gitignore
.vercelignore		.vercelignore
README.md		README.md
setup.ps1		setup.ps1
vercel.json		vercel.json

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CareerPath - AI-Powered Career Development Platform

📋 Table of Contents

🎯 Overview

✨ Features

Core Features

🌐 External APIs & Services

1. Firebase (Google Cloud Platform)

2. Google Gemini AI (Google Cloud AI)

3. Learning Platform APIs (Indirect)

4. Backend API (FastAPI - Python)

🚀 Tech Stack

Frontend

Backend

Database

External Services

📁 Project Structure

🛠️ Installation & Setup

Prerequisites

1. Clone the Repository

2. Backend Setup (Python FastAPI)

3. Frontend Setup (React + Vite)

4. Firebase Configuration

5. Get Google Gemini API Key

🔧 Environment Variables

Backend (.env)

Frontend (.env)

💻 Usage

Running the Application

User Workflow

Admin Features

📊 Performance Optimizations

Implemented Optimizations

Performance Metrics

📖 Documentation

🧪 Testing

Frontend Testing

Backend Testing

🚀 Deployment

Frontend Deployment (Vercel/Netlify)

Backend Deployment (Railway/Render)

🤝 Contributing

📝 License

🙏 Acknowledgments

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