AI Resume Analyzer

A web-based AI-powered application that analyzes resumes and provides structured feedback including scoring, strengths, weaknesses, missing skills, and improvement suggestions.

It also supports job description matching and PDF report generation.

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Try the Application

👉 Click here to open the live app

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System Architecture

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Features

Resume Analysis

• Upload resume (PDF / DOCX)

• Extract and clean text automatically

• AI-powered structured evaluation

• Resume score (0–100)

• Rating: Excellent / Good / Average / Needs Improvement

• ATS compatibility check (Pass / Fail)

• Profile summary generation

• Strengths & weaknesses identification

• Missing sections / skills detection

• Improvement suggestions

• Keyword extraction (Top 12 only)

• Section-wise scoring:

  • Skills
  • Projects
  • Experience
  • Education

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Job Description Matching

• Compare resume with job description
• Match score (0–100)
• Matched skills (exact overlap only)
• Missing skills extraction
• AI-based comparison analysis
• Personalized improvement suggestions

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Additional Features

• Clean and simple UI
• Download analysis as PDF report
• Supports both ATS analysis and job matching

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Tech Stack

Frontend

• HTML
• CSS
• JavaScript

Backend

• Python
• FastAPI

AI Integration

• Groq API
• Model: llama-3.1-8b-instant

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Libraries Used

• PyPDF2 → PDF text extraction
• docx2txt → DOCX parsing
• FastAPI → Backend framework
• Groq → AI model inference
• jsPDF → PDF generation
• re (Regex) → Text cleaning

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Setup Instructions

1. Clone Repository

git clone <your-repo-link>
cd resume-analyzer

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2. Install Dependencies

pip install fastapi uvicorn PyPDF2 docx2txt groq

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3. Set API Key

import os
client = Groq(api_key=os.getenv("GROQ_API_KEY"))

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4. Run Backend

uvicorn main:app --reload

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5. Run Frontend

Open index.html in your browser.

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🔄 Application Workflow

1. Upload resume
2. Extract text (PDF/DOCX)
3. Clean text using regex
4. Send data to AI model
5. Receive structured JSON response
6. Validate and process output
7. Display results in UI
8. Optional: Match with job description
9. Download report as PDF

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AI Implementation

Resume Analysis

• Structured prompt-based evaluation
• Strict JSON output format
• Controlled randomness (temperature = 0.3)
• Ensures realistic feedback

Job Matching

• Direct skill comparison
• Overlap-based matching
• Missing skills extraction
• AI-generated improvement suggestions

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Prompt Strategy

• Enforced valid JSON output
• No markdown formatting in response
• Consistent field structure
• No duplicate keywords
• Must include weaknesses
• Concise output format

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Error Handling

• JSON cleaning logic implemented
• Safe parsing for AI responses
• Fallback values for missing fields
• Frontend crash prevention
• Regex-based text normalization

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Limitations

• Depends on AI response quality
• Limited parsing of complex resume layouts
• No deep visual structure understanding
• Basic UI design
• No authentication system

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Future Improvements

• Advanced NLP-based parsing
• Improved UI/UX design
• Cloud deployment
• Styled PDF reports
• Enhanced scoring system
• Multi-user support

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Summary

This project demonstrates practical integration of AI into a real-world use case, focusing on structured output generation, prompt engineering, and robust handling of inconsistent AI responses.