fix: make ML dependencies optional for graceful startup (Fixes #212)

[CodeSparks45]

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Linked issue

Closes #212

What this PR does

This PR introduces a graceful fallback mechanism for Machine Learning dependencies (transformers, torch, etc.) during application startup. Instead of crashing with a ModuleNotFoundError when the application is launched via the standard requirements.txt, the ML components are now wrapped in a robust try/except block, allowing the core API to function seamlessly.

Type of change

• Bug fix
• New feature
• ML model / training pipeline
• Refactor (no behaviour change)
• Documentation
• Tests only

ML tier (if applicable)

• Tier 1 — Triage
• Tier 2 — Predictive
• Tier 3 — Autonomous
• Not ML-related

Stack affected

• Backend
• Frontend
• Both

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Changes

Backend

• Modified backend/app/main.py to wrap app.ml imports in an ImportError exception handler.
• Implemented dummy mock functions to intercept downstream ML calls and safely return unmodified data when running in lightweight mode.
• Added a logger.warning to inform developers when the application is operating with ML features disabled.

Frontend

New dependencies

Database / schema changes

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Testing

How did you test this?

Initialized a fresh virtual environment and installed only backend/requirements.txt (simulating the bug environment). Started the FastAPI server and verified that the ModuleNotFoundError is suppressed, the application starts successfully, and the correct warning is printed to the console.

Checklist

• Tested locally end-to-end (upload ZIP or GitHub URL → scan → findings returned correctly)
• New ML model falls back gracefully when model file is absent
• No new console.error or unhandled Python exceptions introduced
• Added or updated tests where applicable
• requirements.txt / package.json updated if new dependencies added
• New model files (.pkl, .pt, etc.) are gitignored, not committed

---

Anything reviewers should focus on

The mock functions (DummyPredictor, deduplicate, etc.) are intentionally designed to preserve the data contracts of the original functions to ensure downstream pipeline stability.

Screenshots (if UI changed)

[github-actions]

🎉 Thank you @CodeSparks45 for submitting a Pull Request!

We're excited to review your contribution.

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[github-actions]

✅ PR template check passed!

@arpit2006 this PR is ready for your review. 🚀

[arpit2006]

@CodeSparks45 , Resolve CI and Quality Gate issue.

[github-actions]

✅ PR template check passed!

@arpit2006 this PR is ready for your review. 🚀

[arpit2006]

@CodeSparks45 !

Thanks for the contribution!

It looks like the required GitHub Actions checks are currently failing, so I won't be able to complete the review or approve the PR just yet.

Could you please investigate and resolve the failing CI checks (particularly the Backend Python 3.10 workflow)? Once the CI is passing, I'll review the changes in detail and proceed with approval if everything looks good.

[CodeSparks45]

@arpit2006 Ok Sir!

[github-actions]

✅ PR template check passed!

@arpit2006 this PR is ready for your review. 🚀

[github-actions]

✅ PR template check passed!

@arpit2006 this PR is ready for your review. 🚀

[arpit2006]

Quality Gate Failed. Please take a look or raise a new PR with related issue fix.