implement_redis_kafka_rate_limit

[VaibhavA123]

Integration tests run locally against open infrastructure containers:

• Redis sliding-window pipeline transactions executed cleanly.
• Kafka producer successfully dispatched messages under 3ms.
• Background worker correctly trimmed user history lengths down to exactly 20 elements using ZSET logic.

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## Summary [required]

This PR introduces a high-performance Event-Driven Architecture (EDA) to resolve critical latency bottlenecks and database stress caused by handling high-frequency user interactions (like activity logging and real-time metric tracking) inside the core application thread. By integrating Redis for a sliding-window rate limiter and a low-latency recommendation engine cache alongside Apache Kafka for asynchronous stream ingestion, the system decouples heavy tracking operations from the HTTP request-response cycle. This ensures sub-millisecond read capabilities for user suggestions while shielding backend infrastructure from API abuse and DDoS vectors.

## Related Issue [required]

Closes # [Insert your Issue Number here, e.g., #12]

## Type of Change [required]

- [ ] Bug fix — resolves a broken behaviour
- [x] Feature — adds new functionality
- [ ] Data — adds new projects to `data/projects.json`
- [ ] Documentation — updates docs, README, or code comments only
- [ ] Style — CSS or visual changes only, no logic change
- [ ] Refactor — restructures code without changing behaviour
- [ ] Test — adds or updates tests

## What Was Changed [required]

| File | Change made |
|------|-------------|
| `utils/redis_client.py` | Created a thread-safe Redis connection pool wrapper for Flask middleware and routes. |
| `utils/kafka_producer.py` | Configured a global, lazy-initialized Kafka Producer utility to dispatch interaction event payloads asynchronously. |
| `app.py` | Attached a `before_request` hook applying a Redis-backed Sliding Window Counter rate limiter on all `/api` routes. |
| `routes/main_routes.py` | Created `/api/suggestions` endpoints: `POST` routes handle immediate "fire-and-forget" event pushes to Kafka, while `GET` routes fetch cached history profiles. |
| `worker/package.json` | Set up configuration metadata and package metrics for the isolated background engine context. |
| `worker/kafka_consumer.py` | Created a standalone Python background stream process to consume Kafka logs, compute analytics, and update capped Redis Sorted Sets (`ZSET`). |

## How to Test This PR [required]

1. Clone this branch: `git checkout feat/event-driven-architecture`
2. Spin up your local infrastructure components (Redis on port `6379`, Kafka on port `9092`):
   ```bash
   docker-compose up -d

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