feat(logging): implement unified request ID logging across frontend and backend

This commit introduces a unified request tracking system across both the Flask ML backend and the Next.js frontend. Each incoming API request is assigned a unique request ID (X-Request-ID) to enable consistent log correlation and structured debugging across services.

🔹 Backend (ML API - Flask):

• Added ml/utils/request_tracker.py:
  • Implements decorators (@with_request_tracking) for automatic logging.
  • Adds structured logging with log_with_context().
  • Generates req_<timestamp>_<random> format IDs.
• Updated app.py:
  • Applied logging to /, /health, and /predict-all routes.
  • All key logic paths (token check, weather fetch, prediction loop) now emit contextual logs.
  • Logs are JSON-formatted for compatibility with log aggregation tools.

🔹 Frontend (Next.js Webapp):

• Introduced src/utils/requestTracker.js and apiClient.js:
  • Automatically attaches X-Request-ID to all API calls.
  • Wraps fetch() with consistent error/logging handling.
  • Logs structured metadata (duration, endpoint, context, status).
• Updated /api/directions, /api/weather to:
  • Include token validation logging
  • Return X-Request-ID in responses
  • Use shared logging & error utilities
• Refactored Map component to use new apiClient.

🔧 CI/CD:

• Updated .gitlab-ci.yml:
  • Allow feature/request-id-logging to deploy to staging
  • Improved output readability and debugging logs

🧪 Added:

• scripts/test-request-tracking.sh: Tests request ID propagation and structured logs
• scripts/test-complete-system.sh: Verifies container health and request chain

Benefits:

• Enables end-to-end traceability for each user request
• Makes debugging across microservices easier and more reliable
• Lays foundation for future observability integrations (ELK, Datadog, etc.)