ResumeIQ | AI Resume Analyzer

Result
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Inspiration

Applying for jobs often feels confusing because candidates don’t know why their resume gets rejected. Most companies use ATS (Applicant Tracking Systems), but there are very few tools that clearly explain how well a resume matches a job description.
We wanted to build a solution that uses AI to give instant, actionable feedback so users can improve their resumes and increase their chances of getting hired.

What it does

ResumeIQ analyzes a user's resume against a job description using AI and provides:

📊 ATS Match Score (0–100)
✅ Matched Skills
❌ Missing Skills
💡 Suggestions for improvement

This helps users understand exactly what they need to improve before applying for a job.

How we built it

Frontend: React + Tailwind CSS for a clean and responsive UI
Backend: Django REST API to handle file uploads and processing
AI Integration: Google Gemini API for intelligent analysis
PDF Parsing: PyMuPDF to extract resume text

Workflow:

User uploads a resume (PDF)
User pastes a job description
Backend extracts text from the PDF
Data is sent to Gemini AI
AI returns structured JSON insights
Frontend displays results visually

Challenges we ran into

API Errors & Model Issues:
Faced multiple issues like invalid models, API version mismatch, and 404 errors while integrating Gemini.
API Key Security:
Our API key got leaked and blocked, forcing us to implement secure .env handling.
AI Response Formatting:
Gemini sometimes returned extra text instead of clean JSON, requiring custom cleaning logic.
Frontend-Backend Data Mismatch:
Inconsistent field names like match_score vs ats_match_percentage caused UI bugs.

Accomplishments that we're proud of

Successfully built a full-stack AI-powered application within hackathon time
Integrated real-time resume analysis using Gemini AI
Designed a modern, clean, and interactive UI
Solved real-world problems like ATS mismatch and resume optimization