AI-Powered Resume Analyzer

Table of Contents

1. Project Overview
2. Features
3. Tech Stack
4. How It Works
5. Project Structure
6. Installation and Setup
   • Prerequisites
   • Using Docker (Recommended for Production)
   • Running Locally (Without Docker)
7. Usage
8. Testing
9. License
10. Contributing
11. Troubleshooting

Project Overview

The AI-Powered Resume Analyzer is a web application designed to analyze user-uploaded resumes against job descriptions, providing feedback and a job match percentage. The application consists of a frontend built with React and Tailwind CSS, and a backend developed using Flask.

Features

• Resume Upload (PDF/Docx Support)
• Extract Skills & Experience using NLP
• Match Resume with a Job Description
• Provide Score & Suggestions
• Download Improved Resume Suggestions

Tech Stack

Frontend

• React.js: JavaScript library for building user interfaces.
• Tailwind CSS: Utility-first CSS framework for styling.
• Axios: Promise-based HTTP client for making API requests.

Backend

• Flask: Micro web framework for Python.
• Flask-CORS: Extension for handling Cross-Origin Resource Sharing (CORS).
• PyMuPDF: Library for PDF text extraction.
• python-docx: Library for reading and writing Microsoft Word files.
• NLTK: Natural Language Toolkit for text processing.
• OpenAI API: For analyzing resumes and providing feedback.

ML Processing

• OpenAI API for text analysis.
• NLTK or spaCy for text processing.

How It Works

1. User uploads a resume (PDF or Docx).
2. The backend extracts text using Python's PyMuPDF or python-docx.
3. The Machine Learning model (or OpenAI API) analyzes skills, keywords, and experience.
4. Compares the resume with a job description provided by the user.
5. Generates feedback (e.g., missing skills, keyword optimization).
6. Displays job match score and lets users download an improved version.

Project Structure

ResumeAnalyzer/
├── frontend/        # React.js application
│   ├── src/         # React source code
│   ├── public/      # Static files
│   └── Dockerfile   # Frontend Docker configuration
├── backend/         # Flask API server
│   ├── app.py       # Main Flask application
│   ├── requirements.txt # Python dependencies
│   └── Dockerfile   # Backend Docker configuration
├── docker-compose.yml  # Docker Compose configuration
└── README.md        # Project documentation

Installation and Setup

Prerequisites

• Node.js (v14 or later)
• Python (v3.8 or later)
• OpenAI API Key
• Docker and Docker Compose (optional, for containerized setup)

Using Docker (Recommended for Production)

1. Create a .env file in the project root with your OpenAI API key:

   OPENAI_API_KEY=your_actual_openai_api_key
   FLASK_ENV=production
   

2. Build and start the containers:

   docker-compose up -d
   

3. Access the application at http://localhost.

Running Locally (Without Docker)

Backend Setup

1. Navigate to the backend directory:

   cd ResumeAnalyzer/backend
   

2. Create a virtual environment:

   python -m venv venv
   

3. Activate the virtual environment:

   • Windows:

     venv\Scripts\activate
     

   • MacOS/Linux:

     source venv/bin/activate
     

4. Install required packages:

   pip install -r requirements.txt
   

5. Create a .env file in the backend directory:

   cp .env.example .env
   

6. Add your OpenAI API key to the .env file:

   OPENAI_API_KEY=your_actual_openai_api_key
   

7. Run the Flask development server:

   python app.py
   

   The backend will be available at http://localhost:5000.

Frontend Setup

1. Navigate to the frontend directory:

   cd ResumeAnalyzer/frontend
   

2. Install required packages:

   npm install
   

3. Start the development server:

   npm start
   

   The frontend will be available at http://localhost:3000.

Usage

1. Upload your resume (PDF or DOCX format).
2. Paste the job description you want to match against.
3. Click "Analyze Resume".
4. Review the analysis results including:
   • Job match score
   • Skills found in your resume
   • Missing skills from the job description
   • Suggestions for improvement

Testing

To run the backend tests, navigate to the backend directory and run:

python -m unittest test_app.py

License

This project is open source and available under the MIT License.

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for any suggestions or improvements.

Troubleshooting

• If you encounter CORS issues, make sure both frontend and backend are running.
• For OpenAI API errors, verify your API key is correct and has sufficient credits.
• If Docker containers fail to start, check port availability and environment variables.