AI Resume Analyzer

An AI-powered resume analysis platform that compares a candidate's resume against a job description and generates a match score, missing keywords, strengths, ATS tips, and improvement suggestions using the Gemini API.

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Features

• Upload resume files in PDF format
• Paste any job description
• AI-powered analysis using Gemini
• Resume-to-job match score
• Missing skills and keyword detection
• Strength identification
• Improvement suggestions
• ATS optimization tips
• FastAPI backend
• Clean HTML/CSS frontend

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Tech Stack

Frontend

• HTML5
• CSS3
• JavaScript

Backend

• Python 3.10+
• FastAPI
• Uvicorn
• Google Gemini API
• python-dotenv
• PyPDF2 / pdfplumber

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Project Structure

AI-resume-analyser/
│
├── backend/
│   ├── main.py
│   ├── requirements.txt
│   ├── .env
│   
│
├── frontend/
│   ├── index.html
│   ├── style.css
│ 
└── README.md

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Installation

1. Clone the Repository

git clone https://github.com/Manoj-kanur/AI-Resume-Analyzer.git
cd AI-Resume-Analyzer

2. Create a Virtual Environment

python -m venv venv
venv\Scripts\activate

3. Install Backend Dependencies

cd backend
pip install -r requirements.txt

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Environment Variables

Create a .env file inside the backend folder.

GEMINI_API_KEY=your_api_key_here

Example:

GEMINI_API_KEY=AIzaSyxxxxxxxxxxxxxxxxxxxx

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Gemini Configuration

In your backend code, configure Gemini like this:

import google.generativeai as genai

genai.configure(api_key=os.getenv("GEMINI_API_KEY"))

model = genai.GenerativeModel("gemini-2.0-flash")

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Running the Backend

cd backend
uvicorn main:app --reload

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Running the Frontend

Open the frontend/index.html file in your browser.

Or use a local server:

cd frontend
python -m http.server 5500

Then open:

http://localhost:5500

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API Endpoint

Sample Response

{
  "match_score": 84,
  "verdict": "Strong Match",
  "summary": "Your resume aligns well with the required skills and experience.",
  "strengths": [
    "Strong Python knowledge",
    "Experience with FastAPI",
    "Relevant AI projects"
  ],
  "missing_keywords": [
    "Docker",
    "AWS",
    "CI/CD"
  ],
  "improvements": [
    {
      "section": "Projects",
      "suggestion": "Add measurable outcomes and technologies used."
    }
  ],
  "ats_tips": [
    "Use standard section headings",
    "Include keywords from the job description"
  ]
}

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Future Enhancements

• Support DOCX and TXT resumes
• Add user authentication
• Save analysis history in MongoDB
• Generate downloadable PDF reports
• Add dashboard and charts
• Compare multiple resumes
• Add LinkedIn profile analysis

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Author

Developed K R Manoj as an AI-based resume analysis project using FastAPI and Gemini API.