Refactor & Optimize Chatbot Code for Improved Performance and Maintainability

The current chatbot implementation uses a brute-force approach for processing and responding to user inputs. While functional, this method:

* Increases response time as the number of rules grows.
* Makes the code harder to maintain and extend.
* Limits scalability and adaptability for future improvements.

**Proposed Solution:**

* Refactor the chatbot logic using a more **modular and efficient architecture**.
* Implement **optimized NLP handling** (e.g., tokenization, preprocessing) for faster processing.
* Use **data structures** like dictionaries, regex patterns, or keyword maps to replace repetitive conditional checks.
* Follow **Agile methodology** for iterative improvements, ensuring performance gains in each sprint.

**Expected Benefits:**

* Faster response times.
* Cleaner, more maintainable code.
* Easier scalability for adding new fitness-related features (workouts, nutrition, sleep tips).
* Better user experience with more accurate and timely responses.

@Akki-jaiswal I am a **GSSoC-2025 Contributor** and would like to take up this task to improve the chatbot’s performance and overall efficiency.