📌 BreadButter - Fullstack Internship Assignment (Task 1)

This is the GitHub repository for Task 1 of the BreadButter Fullstack Internship Assignment.
I have successfully completed the core objective of building a fullstack matchmaking application with a REST API and a responsive React frontend.

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🚀 Tech Stack Used

🖥️ Frontend

• React (TypeScript)
• Axios – for making HTTP requests
• React Router – for routing (if applicable)

🛠️ Backend

• Node.js + Express
• CORS – to enable cross-origin requests
• Dotenv – for environment variable management
• FS – for reading local JSON files
• Nodemon – for hot-reloading during development

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⚙️ Setup Instructions

1. Fork the repo and clone it into your working directory.

2. Open two terminals:

   • In one, navigate to the client directory.
   • In the other, navigate to the server directory.

3. Run npm i in both terminals to install dependencies.

4. Create .env files in both client and server directories.

   • Client .env:

     VITE_CLIENT_API=<your_backend_url>

   • Server .env:

     PORT=<your_port_number>

5. Start the backend:

   npm run dev

   • Copy the backend's localhost URL and paste it into VITE_CLIENT_API in the client .env.

6. Start the frontend:

   npm run dev

7. You're done! 🎉 You can now use the local website to test talent-client matchmaking.

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📦 Data Used

This project primarily uses one dataset:

TalentProfiles.json

Contains creator data including:

• name: Creator’s full name
• city: Location of the creator
• skills: Array of skills offered
• style_tags: Tags describing the creator's style
• portfolio[].tags: Style tags used in specific portfolio projects

These fields are used to match against the client's input brief.

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📊 Scoring Logic

The backend assigns scores to each creator based on how well they match the client’s brief. The scoring rules are:

Criteria	Score
Location match	+3
Budget match (within range)	+2
Each matching skill	+2 per skill
Each matching style (in tags/portfolio)	+1 per style match

💡 Matching Process:

1. If the creator's city matches the client's selected location → +3 points.
2. If the client's budget falls within the creator’s expected budget range → +2 points.
3. For every skill in the client’s input that matches the creator’s skills array → +2 points per match.
4. For every style keyword that matches:
   • the creator's style_tags
   • or their portfolio[].tags → +1 point per match

The backend calculates scores for all creators using this logic and returns the top 3 matches along with a breakdown of why they were selected.

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Deployed Website

Click here to visit the Talent Matchmaker

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🧠 Improvements & Bonus Features (Optional)

Not implemented, but possible enhancements include:

• ✅ Thumbs up/down feedback per creator match (stored in match_history.json)
• ✅ Display past feedback for each creator
• ✅ Use fuzzy matching (e.g., sentence embeddings) for better style detection