Video URLs (e.g., YouTube links) are stored in the MySQL database alongside song metadata.
Used iframes, video players that were embedded directly into the frontend.
Users can watch recommended videos without leaving the application.
The system primarily supports English-language queries. Non-English queries may not be interpreted accurately.
The system relies on external platforms (e.g., YouTube) for video playback, which may introduce latency or compatibility issues.
Dependence on OpenAI's API for query interpretation introduces latency and potential rate limits.
Continuous updates to the database may lead to scalability challenges as the dataset grows.
Next.js (React framework for server-side rendering and static site generation).
Python (for query processing, database interaction, and response generation).
MySQL (for storing music metadata and video URLs).
OpenAI API (for natural language query processing), Langchain (for consistent output parsing).
iframes (for embedding video players).
This project successfully delivered a natural language-driven music recommendation system with embedded video playback. By leveraging advanced NLP techniques, a robust MySQL database, and seamless frontend-backend integration, we created a user-friendly application that meets the needs of modern music enthusiasts. The system's ability to interpret natural language queries, provide consistent responses, and dynamically update its database ensures a fresh and engaging user experience.
