🚀 CareerCopilot AI

AI-Powered Resume & Career Intelligence Platform

CareerCopilot AI is an AI-powered career intelligence platform that analyzes a candidate's resume against a job description and provides personalized insights to help them prepare for their target role.

It uses RAG, embeddings, semantic search, OCR, and LLMs to generate meaningful career insights instead of relying only on simple keyword matching.

✨ Features

📄 Resume Analysis
🎯 Resume–Job Description Match Analysis
🔍 Skill Gap Analysis
💼 AI-Generated Interview Questions
🛣️ Personalized Career & Learning Roadmap
📑 PDF Document Processing
🔎 OCR Support for scanned documents
🧠 RAG-based contextual analysis
🔢 Semantic similarity using embeddings and FAISS
🤖 LLM-powered career insights

🧠 How It Works

Resume PDF + Job Description PDF
                ↓
        Document Extraction
                ↓
          OCR if required
                ↓
          Text Chunking
                ↓
        BGE Embeddings
                ↓
        FAISS Vector Search
                ↓
       Relevant Context
                ↓
          Groq LLM
                ↓
       CareerCopilot Engine
                ↓
     Resume & Career Insights

The system generates:

Resume Skills
Job Description Skills
Skill Gap Analysis
Match Score
Interview Questions
Personalized Roadmap

🛠️ Tech Stack

AI / ML

Python
Machine Learning
Retrieval-Augmented Generation (RAG)
LangChain
Sentence Transformers
BGE Embeddings
FAISS
Groq LLM

Document Processing

PyPDF
PyMuPDF
Tesseract OCR

Backend

FastAPI
Pydantic
Uvicorn

Frontend

HTML5
CSS3
JavaScript
Marked.js
DOMPurify

Original UI

Streamlit

Development

uv
Git
GitHub
VS Code

🏗️ Project Architecture

CareerCopilot AI currently has two interfaces built on top of the same core AI functionality.

                 CareerCopilot AI
                        │
          ┌─────────────┴─────────────┐
          │                           │
    Streamlit UI              FastAPI Backend
          │                           │
          │                    HTML/CSS/JS UI
          │                           │
          └─────────────┬─────────────┘
                        │
                        ▼
              CareerCopilot Engine
                        │
          ┌─────────────┼─────────────┐
          │             │             │
         RAG        Embeddings      LLM
          │             │             │
          ▼             ▼             ▼
       FAISS        BGE Models     Groq

The Streamlit version is the original working interface, while the FastAPI + HTML/CSS/JavaScript version is the newer API-based architecture for production-oriented deployment.

📂 Project Structure

CareerCopilot-AI/
│
├── backend/
│   ├── main.py
│   ├── schemas.py
│   └── routes/
│       └── analysis.py
│
├── frontend/
│   ├── index.html
│   ├── css/
│   │   ├── style.css
│   │   ├── components.css
│   │   └── responsive.css
│   └── js/
│       ├── api.js
│       ├── app.js
│       ├── dashboard.js
│       ├── interview.js
│       ├── roadmap.js
│       ├── skill-gap.js
│       ├── upload.js
│       ├── state.js
│       ├── icons.js
│       └── markdown-parser.js
│
├── src/
├── config/
├── data/
├── app.py
├── pyproject.toml
├── uv.lock
├── .gitignore
└── README.md

🔌 FastAPI Endpoints

Health Check

GET /api/v1/health

Resume & JD Analysis

POST /api/v1/analyze

Request:

multipart/form-data

resume: PDF
job_description: PDF

Response:

{
  "status": "success",
  "message": "Candidate analysis completed successfully.",
  "data": {
    "resume_skills": "...",
    "jd_skills": "...",
    "skill_gap": "...",
    "match_score": "...",
    "interview_questions": "...",
    "roadmap": "..."
  }
}

Swagger UI:

http://127.0.0.1:8000/docs

🚀 Run Locally

1. Clone

git clone https://github.com/YOUR_USERNAME/CareerCopilot-AI.git
cd CareerCopilot-AI

2. Create environment

uv venv

Windows

.venv\Scripts�ctivate

Linux / macOS

source .venv/bin/activate

3. Install dependencies

uv sync

4. Configure environment

Create .env:

GROQ_API_KEY=your_groq_api_key

Never commit .env or API keys to GitHub.

▶️ Streamlit Version

The original Streamlit application is still available:

uv run streamlit run app.py

▶️ FastAPI + Frontend Version

Start the backend:

uv run uvicorn backend.main:app --reload

Backend:

http://127.0.0.1:8000

Serve the frontend:

python -m http.server 5500 --directory frontend

Open:

http://127.0.0.1:5500

🔐 Security

API keys are stored server-side
.env is excluded from Git
Frontend never directly accesses Groq
Uploaded files are validated
API responses are validated using Pydantic
LLM-generated Markdown is sanitized before rendering
Backend errors are handled without exposing sensitive internal details

📌 Current Status

AI / RAG Pipeline          ✅
Resume Analysis            ✅
Skill Gap Analysis         ✅
Match Score                ✅
Interview Questions        ✅
Career Roadmap             ✅
OCR Support                ✅
FAISS Semantic Search      ✅
Groq LLM Integration       ✅
Streamlit Application      ✅
FastAPI Backend            ✅
HTML/CSS/JS Frontend       ✅
API Documentation          ✅
Local End-to-End Testing   ✅
GitHub                     🔄
Production Deployment      ⏳

🔮 Future Improvements

🌐 Production deployment
🔐 User authentication
📊 Career analytics dashboard
📄 Downloadable career reports
🎯 ATS optimization
🔄 Multiple job-description comparison
💼 Job recommendation system
📈 Career progress tracking

👨‍💻 Author

Monish Raj A T

B.Tech — Artificial Intelligence & Machine Learning
University Visvesvaraya College of Engineering (UVCE)

⭐ Support

If you find CareerCopilot AI interesting, consider giving the repository a ⭐ on GitHub.