AI Resume Intelligence & Skill Gap Analyzer

A powerful AI-driven platform that bridge the gap between your resume and your dream job. Upload your resume, paste a job description, and get instant skill-gap analysis, a personalized 30/60/90-day learning roadmap, and recruiter-style feedback.

🚀 Features

• Skill Intelligence: Automated extraction of skills from your resume and comparison with JD requirements.
• Match Score: Precise calculation of how well your profile aligns with the role.
• Personalized Roadmap: Structured 30, 60, and 90-day learning plans including project ideas and resume highlights.
• Recruiter Feedback: Honest, AI-generated analysis of why your resume might be rejected and how to fix it.
• ATS Optimization: Direct suggestions on keywords and bullet point improvements.

🛠️ Tech Stack

Frontend

• Framework: Next.js 16 (App Router)
• Styling: Vanilla CSS + TailwindCSS (for glassmorphism and layout)
• Animations: Framer Motion
• Icons: Lucide React
• Markdown Rendering: React Markdown

Backend

• Framework: FastAPI (Python)
• AI Engine: Google Gemini 2.5 Flash
• NLP: SpaCy & Sentence-Transformers
• PDF Parsing: pdfplumber

📦 Setup & Installation

Prerequisites

• Node.js (v18+)
• Python (3.9+)
• Google Gemini API Key

Backend Setup

1. Navigate to the backend directory:

   cd backend

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Configure environment variables in .env:

   GEMINI_API_KEY=your_api_key_here
   PORT=8000

5. Run the server:

   python app/main.py

Frontend Setup

1. Navigate to the frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Run the development server:

   npm run dev

4. Open http://localhost:3000 in your browser.

🚀 Deployment Guide

This project is split into a Frontend (Next.js) and a Backend (FastAPI).

1. Backend Deployment (Render / Railway / Docker)

Since the backend is a Python FastAPI server, it needs a host that supports long-running processes.

Option A: Standard Deployment (Render Native)

• Service: Render or Railway are recommended.
• Build Command: pip install -r requirements.txt && python -m spacy download en_core_web_sm && python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('all-MiniLM-L6-v2')"
• Start Command: PYTHONPATH=. uvicorn app.main:app --host 0.0.0.0 --port $PORT

Option B: Docker Deployment

If you prefer containerization:

• Build: docker build -t resume-backend ./backend
• Run: docker run -p 8000:8000 -e GEMINI_API_KEY=your_key resume-backend
• The Dockerfile is optimized to use the $PORT environment variable assigned by your cloud provider.
• Environment Variables:
  • GEMINI_API_KEY: Your Google AI API Key.
  • PORT: Usually set automatically by the host.

2. Frontend Deployment (Netlify)

The frontend is optimized for Netlify.

• Link your repository to Netlify.
• Build Settings:
  • Base directory: frontend
  • Build command: npm run build
  • Publish directory: frontend/.next
• Environment Variables:
  • NEXT_PUBLIC_API_URL: The full URL of your deployed backend (e.g., https://skillzsprint.onrender.com).

🛡️ Security & Secret Management

Protecting your API keys is critical. This project is configured with the following security measures:

• Environment Isolation: All sensitive keys (like GEMINI_API_KEY) are stored in .env or .env.local files, which are explicitly ignored by Git.
• Production Secrets: When deploying to Render or Netlify, never upload your .env files. Instead, add your keys directly through the platform's dashboard:
  • Netlify: Site Settings > Environment Variables
  • Render: Dashboard > Your Service > Environment
• Docker Security: The .dockerignore file ensures that local secrets are not bundled into your production container.

Caution

If you accidentally commit an API key to a public repository, rotate it immediately in the Google AI Dashboard.

📄 License

© 2026 AI Resume Intelligence. All rights reserved.