feat: add pagination support for items API

[RohitChavan16]

What changed

• Added pagination support to the /api/items endpoint
• Added page and per_page query parameters
• Implemented backend pagination logic using offset calculation
• Added pagination metadata:
  • total_count
  • total_pages
  • current_page
  • has_more
• Added backward-compatible response fields:
  • total
  • page
  • limit
• Updated frontend to request paginated data
• Added frontend pagination state handling
• Fixed frontend infinite loading issue
• Improved compatibility between frontend and backend responses

---

Why

Previously, the /api/items endpoint returned all products at once, which caused:

• slow API responses for large datasets
• unnecessary bandwidth usage
• heavy frontend rendering load
• frontend loading issues

This change improves scalability and performance by loading only the required set of products per request.

---

How to test

1. Install dependencies

pip install -r requirements.txt

2. Start backend server

python -m uvicorn backend.main:app --reload

3. Open the application

http://127.0.0.1:8000

4. Test paginated API endpoint

http://127.0.0.1:8000/api/items?page=1&per_page=20

5. Verify

• products load correctly
• pagination metadata is returned
• frontend no longer remains stuck in loading state
• different pages return different product sets
• infinite scrolling/pagination works correctly

---

Screenshots (if UI change)

---

Checklist

• I have read the CONTRIBUTING.md
• My code follows PEP8 style (flake8 .)
• I have tested my changes locally
• [x ] I have added/updated tests where applicable
• I can explain every line of code I've written
• I have NOT used AI-generated code without understanding and attributing it

---

Related issue

Closes #23

---

AI assistance disclosure

• I did not use AI assistance for this PR
• I used AI assistance for:
  • understanding pagination integration
  • debugging backend import issues
  • fixing frontend infinite loading issues
  • improving frontend/backend compatibility

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Labels added: gssoc:approved | mentor:leonagoel | status:review-needed

PR Description Checklist:

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Hey @mohit Bareja! The Pipeline Smoke Test is failing due to a dependency conflict in requirements.txt — there are two conflicting pandas versions specified: pandas==2.0.3 and pandas>=2.1.0. Could you fix that by keeping only one consistent version? You can run it locally first to make sure it passes before pushing. Let me know if you need help!

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