Resume Analyzer Pro

A modern, AI-powered resume analysis application built with React frontend and Flask backend. Get intelligent insights and recommendations to optimize your resume for better job opportunities.

Features

• AI-Powered Analysis: Uses OpenAI GPT-4 for intelligent resume analysis
• PDF Processing: Advanced PDF text extraction using PyMuPDF
• Modern UI: Beautiful React frontend with Tailwind CSS and Framer Motion
• Drag & Drop: Easy file upload with drag and drop functionality
• Responsive Design: Works perfectly on desktop, tablet, and mobile
• Real-time Feedback: Get instant analysis and recommendations
• LinkedIn Integration: Pull LinkedIn profiles (coming soon)

Tech Stack

Frontend

• React 18 - Modern React with hooks
• Tailwind CSS - Utility-first CSS framework
• Framer Motion - Smooth animations and transitions
• React Router - Client-side routing
• Axios - HTTP client for API calls
• React Dropzone - File upload with drag and drop
• Lucide React - Beautiful icons

Backend

• Flask - Python web framework
• OpenAI GPT-4 - AI-powered analysis
• PyMuPDF - PDF text extraction
• Python-dotenv - Environment variable management
• gunicorn - Python development web framework

Installation

Prerequisites

• Node.js (v16 or higher)
• Python (v3.8 or higher)
• npm or yarn

Backend Setup

1. Clone the repository and navigate to it

   git clone <repository-url>
   cd group_project_python

   repository-url is https://github.com/ChrisAnz19/group_project_python.git

2. Create a virtual environment

   Choose either conda or python to create your virutal environment.

   conda create -n group_project_python python=3.11 # create new environment in cond
   conda activate group_project_python # activate the environment in conda

   python -m venv group_project_python # create virtual environment in python
   source group_project_python/bin/activate  # activate virutal envrionment in python (On Windows: group_project_python\Scripts\activate)

3. Install Python dependencies

   pip install -r requirements.txt

4. Set up environment variables Create a .env file in the root directory:

   OPENAI_API_KEY=your_openai_api_key_here

   See section on Configuration for further details on how to set up the file.

5. Run the Flask backend

   FLASK_APP=web_app FLASK_RUN_PORT=5001 flask run --host=localhost   

   The backend will run on http://localhost:5001

Frontend Setup

1. Install Node.js dependencies

   npm install

2. Start the React development server

   npm start

   The frontend will run on http://localhost:3000

Usage

1. Open the application in your browser at http://localhost:3000

2. Choose your input method:

   • Upload PDF: Drag and drop or click to upload a PDF resume
   • Paste Text: Copy and paste your resume text directly

3. Get analysis: The AI will analyze your resume and provide recommendations for:

   • Education section
   • Experience section
   • Skills section
   • General formatting and optimization

4. Review results: View detailed feedback and actionable recommendations

Testing

While at the root directory, use the following command to run the tests:

pytest # find all Python tests and run them
npm test -- --watchAll=false # find and run all JS tests

Configuration

Environment Variables

Create a .env file in the root directory with the following variables:

# OpenAI API Key for GPT-4 analysis
OPENAI_API_KEY=sk-your-openai-api-key

# Flask configuration
FLASK_ENV=development
FLASK_DEBUG=True

API Endpoints

The Flask backend provides the following endpoints:

• POST /result - Analyze resume text
• POST /result_pdf - Analyze uploaded PDF file
• GET / - Home page
• GET /about - About page
• GET /trending-jobs - Trending Jobs page
• GET /leadership - Leadership page

Customization

Styling

The application uses Tailwind CSS for styling. You can customize the design by:

1. Modifying tailwind.config.js for theme customization
2. Updating src/index.css for custom styles
3. Changing component styles in individual React components

Deployment

Frontend Deployment

1. Build the production version:

   npm run build

2. Deploy the build folder to your hosting service (Netlify, Vercel, etc.)

Backend Deployment

1. Deploy the Gunicorn

OPENAI_API_KEY=your_openai_api_key_here gunicorn --bind 0.0.0.0:5001 "web_app:app" # binds to all 5001 ports 

2. Update the proxy configuration in package.json to point to your deployed backend URL

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.