Inspiration

Recruiters spend a huge amount of time manually screening resumes, and candidates often don’t know how well their resume matches a job role. I wanted to build a system that can instantly analyze resumes and give clear, actionable feedback using AI.

What it does

AIRS is an AI-powered resume screening system that compares a resume with a job description and provides:

  • Match score
  • Skill gap analysis
  • Improvement suggestions

How I built it

I built AIRS using Flask for the backend and HTML, CSS, and JavaScript for the frontend. The core intelligence comes from the Gemini API, which analyzes resume text extracted using PyPDF2 and compares it with the job description to generate insights. The application is deployed using Render.

Challenges I ran into

  • Extracting clean text from PDFs
  • Structuring prompts for accurate AI responses
  • Managing API keys securely
  • Connecting frontend and backend smoothly

What I learned

  • Integrating Gemini API in a real project
  • Building full-stack AI applications
  • Deploying using Render
  • Designing clean UI/UX

What's next

  • Improve scoring accuracy
  • Add support for more file formats
  • Advanced analytics dashboard
  • User login and history tracking

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