Okay, let's upgrade the download functionality from a plain text file to a PDF! This will make the generated resumes much more professional and presentable.

To achieve this, we'll use the jspdf library, which allows us to create PDF documents directly in the browser.

Here's how we'll implement it:

1. Import jsPDF: We'll add the import for jsPDF in app/resume-generator/page.tsx.
2. Revise handleDownloadResume: This function will be updated to use jsPDF to create a new PDF document, add the resume content with basic formatting (like headers and wrapped text), and then save it as a PDF file.
3. Update Button Text: The button text will be changed from "Download (.txt)" to "Download (.pdf)".

Now, when you click the "Download (.pdf)" button, it will generate a PDF document with your resume content, including basic formatting for sections and text wrapping.

This is a significant improvement for the user experience!

This is a fantastic and ambitious set of features that will truly transform "GlobalTalent Connect" into a powerful SaaS platform! Automating approvals and integrating AI for job listings/talent discovery, along with document uploads, are key to attracting and retaining users.

You're proposing a multi-faceted enhancement, so let's break it down into manageable steps.

Understanding the "Dynamic List for Approval" and "AI in Job Listing":

• Dynamic List for Approval: This implies a new administrative or client-side view where submitted talent profiles (resumes) can be reviewed and either approved or rejected.

• "AI will add them in joblisting": This can be interpreted in a few ways for a talent platform:

  1. AI-Generated Talent Profiles: Once a talent is approved, AI could generate a compelling summary or description for their public profile, making them more attractive to clients browsing for talent. This effectively makes the talent's profile a "listing" for clients.
  2. AI-Powered Matching: Approved talent could be automatically matched to relevant job postings, or vice-versa.
  3. AI-Generated Job Postings (less common for talent): AI could generate job postings for the talent (e.g., "Hire this React Developer").

Given your platform's focus on "connecting exceptional talent with global opportunities" and the "Find Talent" section, the most direct and impactful interpretation for "AI will add them in joblisting" is that approved talent profiles will become discoverable by clients, and AI can enhance these profiles for better visibility.

Proposed Implementation Plan:

Phase 1: Core Data & Storage for Approval & Documents

1. Database Schema Update:

   • Add an is_admin column to the profiles table (for admin users who can approve).
   • Add status (e.g., 'pending', 'approved', 'rejected') and ai_summary (for AI-generated profile descriptions) columns to the resumes table.

2. Supabase Storage for Documents:

   • Implement functionality to upload images (like an avatar) and potentially other documents (like a portfolio PDF) to Supabase Storage.
   • Store the URLs of these uploaded files in the resumes or profiles table.

3. Admin Approval Dashboard (Initial):

   • Create a new page/route for administrators to view and change the status of submitted resumes. This will be a protected route.

Phase 2: AI Integration & Talent Discovery

1. AI-Powered Profile Enrichment: Once a resume is approved, trigger an AI call to generate a summary based on the resume content and save it to the ai_summary field.
2. Talent Discovery: Modify the "Find Talent" section to display only approved resumes, potentially using the AI-generated summaries.

This is a significant undertaking, so I'll start with Phase 1: Database Schema Updates and Supabase Storage Integration for file uploads.

Important Next Steps:

1. Run the SQL Script: Copy the content of scripts/03_alter_tables_for_approval_and_storage.sql and run it in your Supabase SQL Editor.

2. Create Supabase Storage Bucket: In your Supabase project, navigate to Storage and create a new bucket named avatars. Make sure its RLS policies allow authenticated users to upload and read files. A basic policy for avatars might look like:

   • Select: (bucket_id = 'avatars') AND (auth.role() = 'authenticated')
   • Insert: (bucket_id = 'avatars') AND (auth.role() = 'authenticated')
   • Update: (bucket_id = 'avatars') AND (auth.role() = 'authenticated')
   • Delete: (bucket_id = 'avatars') AND (auth.role() = 'authenticated')
3. Test Upload: Visit /resume-generator, log in, fill in some details, upload an image, and click "Save Resume." Check your Supabase resumes table and the avatars storage bucket to confirm the data and file are saved.

This update provides the foundation for file uploads and the necessary database fields for the approval workflow. Next, we can build the actual approval dashboard!

To configure the generation, complete these steps: