AI-Powered Resume Analyzer & Career Resume Coach

An AI-powered web application that analyzes a candidate's PDF resume against their target career and generates a structured resume review.

The system combines Prompt Engineering, Google Gemini, Flask, PDF text extraction, ATS analysis, career matching, resume scoring, and structured AI outputs to provide practical resume improvement guidance.

This project was developed as part of the Vault of Codes Prompt Engineering Internship.

📌 Project Overview

The AI-Powered Resume Analyzer & Career Resume Coach allows students, freshers, and job seekers to upload a PDF resume and provide information about their target career.

The application extracts the resume content and processes it through a modular 8-prompt AI pipeline.

The final report includes:

Overall Resume Score
ATS Readiness Score
Career Match Score
Resume Strengths
Key Problems
Missing Information
Grammar & Consistency Issues
Content Improvement Suggestions
Priority Improvement Plan
Top Actions
Final Recommendation

The application is designed to provide useful career guidance without inventing skills, achievements, technologies, or experience that are not supported by the candidate's resume.

🌐 Live Demo

Try the AI Resume Analyzer & Career Coach

✨ Features

📄 Resume Processing

PDF resume upload
PDF-only validation
Maximum file size validation
Multi-page PDF support
Resume text extraction using PyMuPDF
Detection of unreadable or invalid PDFs
Character and page count extraction

👤 Student Information

Users can provide:

Full Name
Current Education
Degree / Course
Current Year / Stage

🎯 Career Goal Information

Users can specify:

Target Job Role
Target Industry
Career Goal
Experience Level
Preferred Job Type
Skills to Highlight
Current Career Purpose

🤖 AI Resume Analysis

The AI analyzes:

Resume structure
Resume quality
Career alignment
ATS readiness
Grammar and consistency
Missing information
Content improvement opportunities
Overall resume readiness

📊 Resume Scoring

The system provides:

Overall Resume Score
Resume Quality Score
ATS Readiness Score
Career Match Score
Content Completeness Score
Error & Consistency Score
Content Strength / Evidence Score

🌙 User Interface

Responsive interface
Professional report layout
Dark mode
Structured result cards
Priority-based recommendations
Resume analysis dashboard

🖼️ Application Screenshots

Homepage

The homepage introduces the AI Resume Analyzer and allows users to begin the resume review process.

Student Information

Users enter their current education and academic background before starting the analysis.

Career Goal

Users specify their target job role, industry, career goal, experience level, and preferred job type.

Analyze Resume

After entering the required information and uploading a PDF resume, the user can start the analysis.

AI Resume Analysis Report

The final report presents the overall resume score, ATS readiness, career match, and detailed AI analysis.

Career Goal Match

The application compares the candidate's existing resume with the selected target career.

Key Problems in Resume

Important resume problems are identified along with their priority, reason, and recommended action.

Resume Improvements

The application generates practical improvement recommendations while preserving the candidate's actual experience.

Updated Resume Suggestions

The system provides content improvement guidance based on the candidate's existing resume information.

Final Recommendation

A final career-focused recommendation summarizes the most important improvements the candidate should make.

Dark Mode

The application also supports a dark-mode interface.

🧠 Prompt Engineering Architecture

Instead of sending the entire task through one large AI prompt, this project uses a modular 8-prompt pipeline.

PDF Resume
    │
    ▼
Resume Text Extraction
    │
    ▼
Prompt 1 ── Resume Structure
    │
    ▼
Prompt 2 ── Quality Analysis
    │
    ▼
Prompt 3 ── Career Match
    │
    ▼
Prompt 4 ── ATS Analysis
    │
    ▼
Prompt 5 ── Error Detection
    │
    ▼
Prompt 6 ── Missing Information
    │
    ▼
Prompt 7 ── Content Improvement
    │
    ▼
Prompt 8 ── Final Report
    │
    ▼
AI Resume Review

🧩 8-Prompt Pipeline

Prompt 1 — Resume Structure

File

prompts/01-resume-structure.txt

Extracts and organizes information from the uploaded resume into structured JSON.

Typical information includes:

Candidate information
Professional summary
Education
Technical skills
Soft skills
Work experience
Internships
Projects
Certifications
Achievements
Languages
Other detected sections

Prompt 2 — Resume Quality Analysis

File

prompts/02-quality-analysis.txt

Evaluates the overall quality of the resume.

It considers:

Section quality
Professional clarity
Resume structure
Existing strengths
Weak content
High-priority quality issues

Prompt 3 — Career Match Analysis

File

prompts/03-career-match.txt

Compares the resume against the user's selected career goal.

It evaluates:

Target role alignment
Relevant existing skills
Strong alignment points
Weakly demonstrated skills
Career alignment gaps
Recommended development areas

Prompt 4 — ATS Analysis

File

prompts/04-ats-analysis.txt

Evaluates resume readiness for Applicant Tracking Systems.

It reviews:

Section headings
Resume readability
Keyword coverage
Relevant keywords
Missing or weakly represented keywords
ATS strengths
ATS issues
Priority ATS improvements

Prompt 5 — Error Detection

File

prompts/05-error-detection.txt

Checks the resume for:

Grammar issues
Wording problems
Repetition
Terminology inconsistencies
Timeline inconsistencies
Formatting consistency
Information requiring candidate verification

Prompt 6 — Missing Information Analysis

File

prompts/06-missing-information.txt

Identifies important information that may be:

Missing
Incomplete
Weakly demonstrated
Candidate-verification dependent

The prompt also avoids treating optional sections as mandatory.

Prompt 7 — Content Improvement

File

prompts/07-content-improvement.txt

Generates practical content improvements while preserving the candidate's real experience.

It can provide:

Improved professional summary
Project description improvements
Internship improvements
Work experience improvements
Skills presentation improvements
Wording improvements
Future development areas

The prompt is designed not to fabricate unsupported candidate information.

Prompt 8 — Final Report Generation

File

prompts/08-final-report.txt

Combines the previous analyses into the final structured resume review.

The final report contains:

Scores
Executive summary
Resume strengths
Key problems
ATS review
Career goal review
Missing information
Errors and consistency
Content improvements
Priority action plan
Top actions
Final recommendation

📊 Resume Scoring Methodology

The overall resume score is based on six components.

Component	Weight
Resume Quality	25%
ATS Readiness	20%
Career Goal Match	20%
Content Completeness	15%
Error & Consistency Quality	10%
Content Strength / Evidence	10%
Total	100%

Formula

Overall Resume Score =

(Resume Quality Score × 0.25)
+
(ATS Readiness Score × 0.20)
+
(Career Match Score × 0.20)
+
(Content Completeness Score × 0.15)
+
(Error & Consistency Score × 0.10)
+
(Content Strength / Evidence Score × 0.10)

The final result is rounded to the nearest whole number.

The overall score is therefore derived from the component scores instead of being an arbitrary standalone value.

📈 Score Interpretation

Score	Interpretation
85–100	Strong Resume
70–84	Good Resume with Targeted Improvements
50–69	Moderate Resume Requiring Meaningful Improvement
30–49	Weak Resume Requiring Major Improvement
0–29	Very Weak or Highly Incomplete Resume

🎓 Fresher Fairness

The scoring methodology does not automatically reduce a candidate's score simply because they are a student or fresher.

For students and freshers, the analyzer can consider evidence such as:

Education
Academic projects
Personal projects
Internships
Certifications
Coursework
Technical skills
Practical implementation
Achievements

Professional employment history is not treated as mandatory when the candidate's experience level does not reasonably require it.

🛠️ Technology Stack

Backend

Python
Flask
Google Gemini API
Google GenAI Python SDK
PyMuPDF
python-dotenv

Frontend

HTML5
CSS3
JavaScript

AI

Google Gemini
Prompt Engineering
Structured JSON outputs
Multi-stage AI pipeline

Testing

Pytest

Deployment

Gunicorn
Flask-compatible hosting

📁 Project Structure

AI-Resume-Analyzer/
│
├── prompts/
│   ├── 01-resume-structure.txt
│   ├── 02-quality-analysis.txt
│   ├── 03-career-match.txt
│   ├── 04-ats-analysis.txt
│   ├── 05-error-detection.txt
│   ├── 06-missing-information.txt
│   ├── 07-content-improvement.txt
│   └── 08-final-report.txt
│
├── screenshots/
│   ├── analyse_button.png
│   ├── Analysis_report.png
│   ├── career_goal_match.png
│   ├── career_goal.png
│   ├── Dark_mode.png
│   ├── Final_recommendation.png
│   ├── Homepage.png
│   ├── improvements.png
│   ├── Key_problem_in_resume.png
│   ├── student_information.png
│   └── updated_resume.png
│
├── static/
│   ├── script.js
│   └── style.css
│
├── templates/
│   └── index.html
│
├── tests/
│   ├── conftest.py
│   ├── test_final_report.py
│   ├── test_mock_pipeline.py
│   ├── test_pdf_processing.py
│   ├── test_profile_validation.py
│   └── test_scoring.py
│
├── uploads/
│   └── .gitkeep
│
├── .env
├── .gitignore
├── app.py
├── requirements.txt
└── README.md

.env and uploaded resume files are excluded from Git using .gitignore.

⚙️ Installation and Setup

1. Clone the Repository

git clone YOUR_GITHUB_REPOSITORY_URL

Move into the project directory:

cd AI-Resume-Analyzer

2. Create a Virtual Environment

python -m venv venv

Windows PowerShell

.\venv\Scripts\Activate.ps1

If PowerShell prevents activation:

Set-ExecutionPolicy -Scope Process -ExecutionPolicy RemoteSigned
.\venv\Scripts\Activate.ps1

3. Install Dependencies

pip install -r requirements.txt

🔐 Environment Variables

Create a .env file in the root directory.

For full AI mode:

GEMINI_API_KEY=your_gemini_api_key
GEMINI_MODEL=your_gemini_model

USE_AI=true
AI_PROMPT_LIMIT=8

Replace the placeholders with your actual Gemini configuration.

Never upload your real Gemini API key to GitHub.

The .env file is ignored through .gitignore.

🤖 AI Mode

To execute all eight Gemini prompts:

USE_AI=true
AI_PROMPT_LIMIT=8

When the application starts, the terminal should show:

========================================
AI Resume Analyzer
USE_AI: True
AI_PROMPT_LIMIT: 8
Gemini enabled for prompts 1 to 8.
========================================

During a successful full analysis:

========== RUNNING PROMPT 1 ==========
Prompt 1 completed successfully.

========== RUNNING PROMPT 2 ==========
Prompt 2 completed successfully.

========== RUNNING PROMPT 3 ==========
Prompt 3 completed successfully.

========== RUNNING PROMPT 4 ==========
Prompt 4 completed successfully.

========== RUNNING PROMPT 5 ==========
Prompt 5 completed successfully.

========== RUNNING PROMPT 6 ==========
Prompt 6 completed successfully.

========== RUNNING PROMPT 7 ==========
Prompt 7 completed successfully.

========== RUNNING PROMPT 8 ==========
Prompt 8 completed successfully.

🧪 Development / Mock Mode

The application also contains a mock mode for development and testing without consuming Gemini API quota.

Set:

USE_AI=false
AI_PROMPT_LIMIT=0

The terminal will show:

AI Resume Analyzer
USE_AI: False
AI_PROMPT_LIMIT: 0
Gemini AI calls are DISABLED.

Mock mode can be used to test:

Frontend
PDF processing
Form validation
Scoring logic
Report layout
Application flow
Automated tests

Mock results are clearly identified as development values and should not be presented as real Gemini analysis.

▶️ Run the Application

Start Flask:

python app.py

The development server will run at:

http://127.0.0.1:5000

Open this URL in your browser.

📄 Resume Upload Requirements

The application currently accepts:

File Type: PDF
Maximum Size: 5 MB

The uploaded resume should contain readable text.

Image-only/scanned PDFs may not provide sufficient extractable text.

🧪 Automated Testing

The project uses Pytest for local automated testing.

Tests cover:

Career profile validation
Missing required field detection
PDF extension validation
PDF text extraction
Invalid PDF handling
Weighted resume score calculation
Resume score-level classification
Mock pipeline execution
Final report structure

Run all tests with:

pytest tests -v

Latest local test result:

========================
9 passed, 5 warnings
========================

The five warnings observed during testing were PyMuPDF/SWIG dependency-level deprecation warnings and did not cause test failures.

✅ Tested Components

Component	Status
Student Information Form	✅ Tested
Career Goal Form	✅ Tested
PDF Validation	✅ Tested
PDF Text Extraction	✅ Tested
Prompt 1	✅ Tested
Prompt 2	✅ Tested
Prompt 3	✅ Tested
Prompt 4	✅ Tested
Prompt 5	✅ Tested
Prompt 6	✅ Tested
Prompt 7	✅ Tested
Prompt 8	✅ Tested
Weighted Scoring	✅ Tested
Final Report	✅ Tested
Mock Mode	✅ Tested
Pytest Suite	✅ 9 Passed

🤖 Live Gemini Pipeline Test

A complete end-to-end Gemini test was performed using all eight prompts.

The terminal confirmed:

Prompt 1 completed successfully.
Prompt 2 completed successfully.
Prompt 3 completed successfully.
Prompt 4 completed successfully.
Prompt 5 completed successfully.
Prompt 6 completed successfully.
Prompt 7 completed successfully.
Prompt 8 completed successfully.

Example result from the test resume:

Overall Score: 78
ATS Score: 88
Career Match: 90

The test demonstrated that the pipeline could generate:

Resume strengths
Career alignment analysis
ATS analysis
Keyword analysis
Missing information
Timeline inconsistencies
Grammar and consistency issues
Content improvement suggestions
Priority recommendations
Final career-focused recommendation

API calls can be limited during development using AI_PROMPT_LIMIT to help manage API quota.

🔒 Privacy and Security

Resumes can contain sensitive personal information.

The application therefore follows several privacy-oriented practices:

PDF files are validated before analysis.
File size is limited to 5 MB.
Resume text is extracted for analysis.
Uploaded resumes are not intentionally permanently stored by the current application flow.
Uploaded files are excluded from Git tracking.
API keys are stored through environment variables.
.env is excluded from Git.
AI instructions discourage fabrication of candidate information.

The uploads/ directory contains .gitkeep only so that the directory structure can remain in Git.

🛡️ AI Accuracy and Responsible Recommendations

The analyzer is designed to avoid inventing candidate information.

The AI should not fabricate:

Skills
Technologies
Employers
Work experience
Internships
Certifications
Achievements
Project details
Responsibilities
Numerical metrics

When recommending a skill the candidate does not currently demonstrate, it should be treated as a future development recommendation, not as an existing skill.

Users should verify AI-generated recommendations before modifying their resume.

⚠️ ATS Disclaimer

The ATS score is an advisory estimate.

Different employers and Applicant Tracking Systems use different:

Parsing methods
Ranking algorithms
Keywords
Job requirements
Resume filters

Therefore, an ATS score generated by this project does not guarantee that a resume will pass a real employer's ATS.

📦 Requirements

The project uses the following Python dependencies:

Flask==3.1.3
python-dotenv==1.2.3
google-genai==2.18.1
PyMuPDF==1.26.4
gunicorn==26.1.0
pytest==9.1.1

Install them using:

pip install -r requirements.txt

🗂️ .gitignore

Sensitive and unnecessary development files should not be committed.

Recommended configuration:

.env
venv/
.venv/
__pycache__/
*.pyc

.pytest_cache/

uploads/*
!uploads/.gitkeep

.vscode/
.DS_Store
Thumbs.db

🚀 Deployment

The project includes gunicorn for production-compatible Flask deployment.

A typical production start command is:

gunicorn app:app

Environment variables must be configured on the deployment platform instead of uploading the local .env file.

Required AI environment variables include:

GEMINI_API_KEY
GEMINI_MODEL
USE_AI
AI_PROMPT_LIMIT

🔮 Future Improvements

Potential future enhancements include:

Job Description vs Resume comparison
Downloadable PDF analysis report
Resume builder
Resume version comparison
LinkedIn profile analysis
Job-specific keyword recommendations
Resume improvement checklist
Multiple resume comparison
Career roadmap generation
Additional AI provider support
Improved analytics dashboard

📚 Key Learning Outcomes

This project demonstrates practical knowledge of:

Prompt Engineering
Multi-prompt AI workflows
Google Gemini API integration
Structured JSON generation
Flask backend development
HTML, CSS, and JavaScript
PDF document processing
ATS-oriented resume analysis
Career goal matching
AI safety constraints
Environment variable management
API quota-aware development
Automated testing with Pytest
Git and GitHub project management

📌 Important Notes

Never commit .env to GitHub.
Never expose the Gemini API key in screenshots or source code.
Uploaded resumes should not be committed to the repository.
AI-generated recommendations should be treated as guidance.
Candidates should only add skills and achievements they genuinely possess.
ATS scores do not guarantee hiring outcomes.
Mock-mode scores are for development and interface testing only.

🎓 Internship Project

Vault of Codes – Prompt Engineering Internship

Project

AI-Powered Resume Analyzer & Career Resume Coach

The project demonstrates the use of prompt engineering to transform unstructured resume information into structured, career-focused recommendations through a modular AI analysis pipeline.

👩‍💻 Author

Kanneboina Maheshwari

Computer Science and Engineering

GitHub: KanneboinaMaheshwari29

GitHub Repo : https://github.com/KanneboinaMaheshwari29/AI-Resume-Analyzer-and-Career-Coach

📜 Disclaimer

This application is an educational and career-support project.

Resume recommendations, career-match scores, ATS scores, and other AI-generated assessments are advisory only. Recruiter preferences, ATS systems, job requirements, and hiring decisions vary between employers.

Users are responsible for reviewing the generated recommendations and ensuring that their final resume contains only accurate and truthful information.

⚠️ Gemini API Quota Note

This project uses the Google Gemini API for AI-powered resume analysis through an 8-prompt pipeline.

Gemini API usage is subject to rate limits and quota restrictions. If the available quota is exhausted, live AI analysis may temporarily fail or become unavailable.

To support development and testing without consuming API quota, the application includes a Mock/Development Mode that:

Does not make Gemini API requests.
Uses predefined test responses.
Allows the UI, scoring logic, PDF processing, and report rendering to be tested safely.
Helps prevent unnecessary API usage during development.

The complete Gemini pipeline has also been successfully tested with all 8 prompts, producing real ATS, career-match, resume-quality, and final-report results.

Mock results are intended only for development and testing. Real resume analysis requires Gemini API access and available API quota.

Name		Name	Last commit message	Last commit date
prompts		prompts
screenshots		screenshots
static		static
templates		templates
tests		tests
uploads		uploads
.gitignore		.gitignore
README.md		README.md
app.py		app.py
requirements.txt		requirements.txt

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AI-Powered Resume Analyzer & Career Resume Coach

📌 Project Overview

🌐 Live Demo

✨ Features

📄 Resume Processing

👤 Student Information

🎯 Career Goal Information

🤖 AI Resume Analysis

📊 Resume Scoring

🌙 User Interface

🖼️ Application Screenshots

Homepage

Student Information

Career Goal

Analyze Resume

AI Resume Analysis Report

Career Goal Match

Key Problems in Resume

Resume Improvements

Updated Resume Suggestions

Final Recommendation

Dark Mode

🧠 Prompt Engineering Architecture

🧩 8-Prompt Pipeline

Prompt 1 — Resume Structure

Prompt 2 — Resume Quality Analysis

Prompt 3 — Career Match Analysis

Prompt 4 — ATS Analysis

Prompt 5 — Error Detection

Prompt 6 — Missing Information Analysis

Prompt 7 — Content Improvement

Prompt 8 — Final Report Generation

📊 Resume Scoring Methodology

Formula

📈 Score Interpretation

🎓 Fresher Fairness

🛠️ Technology Stack

Backend

Frontend

AI

Testing

Deployment

📁 Project Structure

⚙️ Installation and Setup

1. Clone the Repository

2. Create a Virtual Environment

Windows PowerShell

3. Install Dependencies

🔐 Environment Variables

🤖 AI Mode

🧪 Development / Mock Mode

▶️ Run the Application

📄 Resume Upload Requirements

🧪 Automated Testing

✅ Tested Components

🤖 Live Gemini Pipeline Test

🔒 Privacy and Security

🛡️ AI Accuracy and Responsible Recommendations

⚠️ ATS Disclaimer

📦 Requirements

🗂️ .gitignore

🚀 Deployment

🔮 Future Improvements

📚 Key Learning Outcomes

📌 Important Notes

🎓 Internship Project

Project

👩‍💻 Author

GitHub Repo : https://github.com/KanneboinaMaheshwari29/AI-Resume-Analyzer-and-Career-Coach

📜 Disclaimer

⚠️ Gemini API Quota Note

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