A modern web application that uses AI to extract structured data from unstructured resumes with high accuracy. Built with Next.js, TypeScript, and Google's Gemini AI.

• Intelligent extraction of skills, work experience, and education details
• Standardized data formatting and validation
• Clean and modern user interface
• Real-time processing with error handling
• Responsive design for all devices

1. AI-Powered Extraction: Uses Gemini AI to intelligently parse resume content
2. Structured Validation: Implements Zod schemas to ensure data consistency
3. Standardized Formatting: Normalizes dates, degree names, and skill levels
4. Error Handling: Gracefully handles edge cases and invalid inputs

1. Date Formats:

   • Standardizes various date formats (e.g., "Jan 2020", "01/2020", "January 2020")
   • Handles relative dates (e.g., "Present", "Current")
   • Validates date ranges for consistency

2. Degree Names:

   • Standardizes common variations (e.g., "BSc" → "Bachelor of Science")
   • Handles international degree formats
   • Validates degree names against known patterns

3. Skill Levels:

   • Standardizes proficiency levels (Beginner, Intermediate, Advanced, Expert)
   • Infers levels from context when not explicitly stated
   • Handles various skill level descriptions

4. Content Validation:

   • Minimum content length check
   • Keyword-based resume validation
   • Pattern matching for invalid content
   • Structure validation for extracted data

1. AI Model Selection:

   • Chose Gemini AI for its strong natural language understanding
   • Tradeoff: Requires API key and has usage costs
   • Alternative: Could use local models for privacy but with lower accuracy

2. Data Encoding:

   • Using base64 encoding for URL-safe data transfer
   • Tradeoff: Slightly larger URL size
   • Alternative: Could use local storage but less shareable

3. Validation Strategy:

   • Using Zod for runtime type checking
   • Tradeoff: Additional bundle size
   • Alternative: Manual validation but less maintainable

4. UI Framework:

   • Using Next.js with Tailwind CSS
   • Tradeoff: Larger initial bundle
   • Alternative: Could use lighter framework but less developer experience

• Node.js 18+ and npm
• Google Gemini API key

1. Clone the repository:

git clone <repository-url>
cd resume-analyzer

2. Install dependencies:

npm install

3. Create a .env file in the root directory and add your Gemini API key:

GEMINI_API_KEY=your_api_key_here

4. Start the development server:

npm run dev

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

1. Paste your resume content into the text area
2. Click "Parse Resume"
3. View the extracted information in a structured format
4. The parsed data includes:
   • Skills with proficiency levels
   • Work experience with company details
   • Education history with standardized degree names