Cover Letter Generator

A modern web application that generates personalized cover letters using AI based on job descriptions and user information. Built with React frontend and Python backend, featuring Google Gemini AI integration.

Features

• AI-Powered Cover Letter Generation: Uses Google Gemini AI to create personalized cover letters
• Personal Information Management: Collect and store personal details for consistent formatting
• PDF Generation: Automatically generate professional PDF cover letters
• Modern Web Interface: Clean, responsive React frontend
• Docker Support: Containerized deployment ready
• Cloud Deployment: Configured for Google Cloud Run deployment

Project Architecture

The application follows a microservices architecture with the following components:

• Frontend (frontend/): React application with modern UI
• Backend (backend/): Flask server handling API requests and serving the frontend
• AI Service (api_service/): Google Gemini AI integration for cover letter generation
• PDF Service (pdf_service/): PDF generation using ReportLab
• Static Assets (static/): Resume storage and static files

Prerequisites

• Python 3.9+
• Node.js 18+ and npm
• Google Gemini API key
• Virtual environment (recommended)

Setup Instructions

1. Clone the Repository

git clone https://github.com/yourusername/Cover-Letter-Generator.git
cd Cover-Letter-Generator

2. Set Up Python Environment

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install backend dependencies
pip install -r backend/requirements.txt

3. Set Up Frontend

cd frontend
npm install
cd ..

4. Configure Environment Variables

Create a .env file in the root directory:

# Required: Your Google Gemini API key
GEMINI_API_KEY=your_gemini_api_key_here

# Optional: Frontend API URL (defaults to production URL)
REACT_APP_API_URL=http://localhost:5000

5. Add Your Resume

Place your resume PDF file at static/resume.pdf (the application will use this as a reference for generating cover letters).

Running the Application

Option 1: Quick Start (Recommended)

Use the provided script to start all services:

chmod +x run.sh
./run.sh

This will start:

• Frontend on http://localhost:3002
• Backend on http://localhost:5000

Option 2: Manual Start

Start Backend

# Activate virtual environment
source venv/bin/activate

# Start Flask backend
python backend/app.py

Start Frontend (in a new terminal)

cd frontend
PORT=3002 npm start

Option 3: Docker Deployment

# Build the Docker image
docker build -t cover-letter-generator .

# Run the container
docker run -p 8080:8080 -e GEMINI_API_KEY=your_key_here cover-letter-generator

Usage

1. Open the Application: Navigate to http://localhost:3002
2. Fill Personal Information: Enter your name, email, phone, and other details
3. Add Job Details:
   • Enter the company name
   • Paste the job description
   • Add any custom instructions (optional)
4. Generate Cover Letter: Click "Analyze & Generate" to create your personalized cover letter
5. Download PDF: Review the generated content and download the professional PDF

API Endpoints

• POST /api/analyze: Generate cover letter content using AI
• POST /api/generate-pdf: Create PDF from cover letter content
• GET /api/download/<filename>: Download generated PDF files

Technology Stack

Backend

• Flask: Web framework
• Google Generative AI: AI-powered content generation
• ReportLab: PDF generation
• PyPDF: PDF processing
• Flask-CORS: Cross-origin resource sharing

Frontend

• React 18: Modern UI framework
• React Scripts: Build and development tools

Infrastructure

• Docker: Containerization
• Gunicorn: Production WSGI server
• Google Cloud Run: Cloud deployment platform

Development

Project Structure

Cover-Letter-Generator/
├── backend/                 # Flask backend server
│   ├── app.py              # Main Flask application
│   └── requirements.txt    # Python dependencies
├── frontend/               # React frontend
│   ├── src/               # Source code
│   ├── public/            # Public assets
│   └── package.json       # Node.js dependencies
├── api_service/           # AI service integration
│   ├── ai_service.py      # Gemini AI integration
│   └── system_instruction.txt  # AI prompt instructions
├── pdf_service/           # PDF generation service
│   └── pdf_generator.py   # PDF creation logic
├── static/                # Static files and resume storage
├── terraform/             # Infrastructure as code (if applicable)
├── Dockerfile             # Docker configuration
├── run.sh                 # Development startup script
└── README.md              # This file

Environment Variables

• GEMINI_API_KEY: Required Google Gemini API key
• REACT_APP_API_URL: Frontend API endpoint (defaults to production)

Deployment

The application is configured for deployment on Google Cloud Run. The Dockerfile includes:

• Multi-stage build for optimized production image
• Gunicorn WSGI server for production
• Static file serving for the React frontend

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

License

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