I'd be happy to help you create a CV formatter that can standardize resumes from different file formats. Let me ask a few questions to better understand your requirements:

CV Formatter Project

Let me create a solution that can take in .docx and .pdf CVs and output uniformly formatted .docx files.

Server-Side Implementation Details

For the actual document processing, you'll need to implement the server-side functionality. Here's what you would need to add to make this fully functional:

How to Use This Project

1. Upload CVs: Users can drag and drop or browse to select multiple .docx and .pdf files.

2. Configure Formatting: Users can customize:

   • Font family and size
   • Header and footer text
   • Page numbering
   • Standardization options for headings and spacing
   • Option to remove personal information
3. Process Files: After uploading, users can click "Format CVs" to process all files at once.
4. Download Results: Once processing is complete, users can download each formatted CV as a .docx file.

Implementation Notes

For a complete implementation, you would need to:

1. Install the necessary dependencies:

   • mammoth for parsing .docx files
   • pdf-parse for parsing PDF files
   • docx for generating .docx files

2. Set up proper file handling on the server:

   • Temporary storage for uploaded files
   • Secure file processing
   • Proper cleanup of temporary files

3. Implement the actual document processing logic:

   • Text extraction from different formats
   • Content structure preservation
   • Formatting application based on settings
   • Document generation with consistent styling

Would you like me to explain any specific part of this implementation in more detail?

I'll help you refine the CV formatter with these specific requirements:

Implementation Details for OpenAI GPT Integration

I've added the AI integration to parse and categorize CV content using the AI SDK with OpenAI. Here's how it works:

1. AI-Powered CV Parsing

The system now uses OpenAI's GPT-4o model to:

1. Extract structured information from the CV text:

   • First and last name (for the header)
   • Different sections (Education, Experience, Skills, etc.)
   • Key skills as a list
   • Years of experience
   • Education qualifications

2. Categorize and standardize the CV content:

   • Standardize section names (e.g., "Work History" → "Professional Experience")
   • Organize content within sections consistently
   • Standardize skill descriptions
   • Format dates consistently
   • Ensure consistent formatting for job titles and companies

2. Dynamic Header Generation

The system now:

• Uses [FirstName] and [LastName] as placeholders in the header text
• Extracts the actual first and last name from the CV using AI
• Replaces the placeholders with the extracted names in the final document

3. Complete Processing Flow

1. User uploads CV files (.docx or .pdf)
2. Files are sent to the server

3. For each file:

   • Extract text content
   • Send to OpenAI for parsing via the AI SDK
   • Get structured data back
   • Send to OpenAI again for categorization and standardization
   • Generate a formatted .docx with the structured content
   • Replace header placeholders with actual name
4. User downloads the formatted CVs

Technical Implementation

The implementation uses:

• The AI SDK with OpenAI integration (generateText function)
• Server-side processing with Next.js API routes
• Structured data flow between the frontend and backend

Next Steps

To fully implement this solution, you would need to:

1. Set up your OpenAI API key as an environment variable

2. Install the required dependencies:

   npm install ai @ai-sdk/openai mammoth pdf-parse docx

3. Add the OpenAI API key to your environment variables:

   OPENAI_API_KEY=your_api_key_here

Would you like me to explain any specific part of this implementation in more detail?