ResumeCraft 🚀

AI-powered resume analysis and optimization platform

✨ Features

🎯 Core Features

• AI-Powered Analysis: GPT-4 driven comprehensive resume feedback
• Job Readiness Score: 0-100 scoring with detailed breakdown
• Export Capabilities: PDF and text export of analysis results
• Progress Tracking: Visual charts showing improvement over time
• Guest & Registered Modes: Works without signup, enhanced with account

🔐 Authentication & User Management

• Secure user registration and login
• Password reset functionality
• Guest mode for immediate analysis
• User dashboard with analytics

📊 Analytics & Insights

• Strengths Identification: What's working in your resume
• Improvement Areas: Specific weaknesses to address
• ATS Optimization: Tips for applicant tracking systems
• Trend Analysis: Track progress over multiple uploads

💼 Professional Features

• PDF Export: Professional formatted analysis reports
• Progress Charts: Visual representation of improvement
• Usage Analytics: Track monthly and total analyses
• Subscription Ready: Built for tiered pricing (free/pro/premium)

Tech Stack

• Frontend: Next.js 15 with App Router, TypeScript, Tailwind CSS
• Backend: Next.js API routes
• Database & Storage: Supabase
• AI: OpenAI GPT-4 for resume analysis
• File Processing: PDF, Word (.docx/.doc), and text file support
• UI Components: Lucide React icons, React Dropzone

Getting Started

Prerequisites

• Node.js 18+
• npm or yarn
• OpenAI API key
• Supabase project (optional, for file storage)

Environment Setup

1. Copy the environment variables:

cp .env.example .env.local

2. Fill in your environment variables in .env.local:

# OpenAI Configuration (Required)
OPENAI_API_KEY=your_openai_api_key

# Supabase Configuration (Optional)
NEXT_PUBLIC_SUPABASE_URL=your_supabase_project_url
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_supabase_anon_key
SUPABASE_SERVICE_ROLE_KEY=your_supabase_service_role_key

Installation

1. Install dependencies:

npm install

2. Run the development server:

npm run dev

3. Open http://localhost:3000 in your browser.

Usage

1. Upload Resume: Drag and drop or click to select a PDF, Word (.docx/.doc), or text file (max 10MB)
2. Wait for Analysis: The AI will process your resume through multiple stages
3. Review Feedback: Get comprehensive feedback including:
   • Strengths and weaknesses
   • Specific improvement suggestions
   • ATS optimization tips
   • Job-readiness score
   • Improved bullet points
4. Apply Changes: Implement the suggestions to improve your resume
5. Re-analyze: Upload your updated resume to track improvements

Project Structure

src/
├── app/
│   ├── api/
│   │   └── analyze/
│   │       └── route.ts          # Resume analysis API endpoint
│   ├── globals.css               # Global styles
│   ├── layout.tsx               # Root layout
│   └── page.tsx                 # Main application page
├── components/
│   ├── FeedbackPanel.tsx        # Analysis results display
│   ├── Loading.tsx              # Loading states component
│   └── ResumeUploader.tsx       # File upload component
├── lib/
│   ├── openai.ts               # OpenAI configuration
│   └── supabase.ts             # Supabase configuration
├── types/
│   └── index.ts                # TypeScript type definitions
└── utils/
    ├── fileUpload.ts           # Supabase file upload utilities
    ├── generatePrompt.ts       # OpenAI prompt generation
    └── parsePDF.ts             # PDF text extraction utilities

API Reference

POST /api/analyze

Analyzes an uploaded resume file and returns AI-powered feedback.

Request:

• Method: POST
• Content-Type: multipart/form-data
• Body: FormData with 'file' field containing PDF, Word (.docx/.doc), or text file

Response:

{
  "success": true,
  "analysis": {
    "strengths": ["strength 1", "strength 2", "strength 3"],
    "weaknesses": ["weakness 1", "weakness 2", "weakness 3"],
    "suggestions": ["suggestion 1", "suggestion 2", "suggestion 3"],
    "atsOptimization": ["tip 1", "tip 2", "tip 3"],
    "jobReadinessScore": 85,
    "improvedBulletPoints": ["improved point 1", "improved point 2"]
  }
}

Deployment

Vercel (Recommended)

1. Push your code to GitHub
2. Connect your repository to Vercel
3. Add environment variables in Vercel dashboard
4. Deploy

Other Platforms

The application can be deployed to any platform that supports Next.js applications.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Support

For support, please open an issue on GitHub or contact the development team.