AI Resume Roaster & Analyzer

Application Architecture Diagram

A powerful tool to analyze, score, and improve your resume using AI. Get brutally honest feedback and professional suggestions to make your resume stand out to recruiters.

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🚀 Features

• ATS-Friendly Scoring: Get an instant score based on key metrics like section completeness, action verbs, and resume length.
• AI-Powered Roast: Receive a sarcastic but insightful critique of your resume's weaknesses and language.
• Professional Rewrite Suggestions: Get AI-generated improvements with strong action verbs and measurable impact.
• Formatting Feedback: Analyzes your resume for font consistency and alignment issues.
• Secure & Private: Your resume is processed and then discarded. Nothing is stored permanently without your consent.
• Supports PDF & DOCX: Upload your resume in the most common formats.

🛠️ Tech Stack

• Frontend: React, Vite, Tailwind CSS
• Backend Server: Node.js, Express
• AI Parser Service: Python, FastAPI, Google Gemini
• Database: MongoDB (for future use, not currently implemented for storage)
• Containerization: Docker, Docker Compose

⚙️ Getting Started

You can run this project either locally on your machine or using Docker.

Prerequisites

• Node.js (v18 or later)
• Python (v3.9 or later)
• A Google Gemini API Key. You can get one from Google AI Studio.
• Docker and Docker Compose (for the Docker-based setup)

Configuration

Create a .env file in the root directory of the project. This file will hold the necessary environment variables.

# .env

# Your Google Gemini API Key
GEMINI_API_KEY=YOUR_GEMINI_API_KEY_HERE

# MongoDB connection string (used by the server)
MONGO_URI=mongodb://mongo:27017/resumes

1. Local Installation (Without Docker)

This method is recommended if you want to run each service manually for development.

1. Clone the repository:

   git clone https://github.com/your-username/Resume-Roaster.git
   cd Resume-Roaster

2. Setup the Backend Server:

   • Navigate to the server directory: cd server
   • Install dependencies: npm install
   • Start the server: npm start
   • The server will be running on http://localhost:5000.

3. Setup the Parser Service:

   • Navigate to the parser directory: cd ../parser
   • Create a Python virtual environment:

     python -m venv venv
     source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

   • Install dependencies: pip install -r requirements.txt
   • Start the parser service: uvicorn main:app --reload --port 8000
   • The parser will be running on http://localhost:8000.

4. Setup the Frontend:

   • Navigate to the client directory: cd ../client
   • Install dependencies: npm install
   • Start the development server: npm run dev
   • The application will be available at http://localhost:5173.

2. Docker Installation

This is the recommended method for a stable, containerized setup.

1. Clone the repository:

   git clone https://github.com/your-username/Resume-Roaster.git
   cd Resume-Roaster

2. Ensure Docker is running on your machine.
3. Build and run the containers:

   docker compose up --build -d

4. Access the application:
   • The application will be available at http://localhost:5173.
   • The services are running on their respective ports as defined in docker-compose.yml.

Usage

1. Open the application in your browser.
2. Click the "Select your resume" button and choose a .pdf or .docx file.
3. Click "Analyze Resume".
4. Wait a few moments for the analysis to complete.
5. View your ATS score, the AI roast, improvement suggestions, and formatting feedback.