In this work, we propose an effective context-aware framework for human-robot interaction (HRI) that uses large language models (LLM) and state-of-the-art object detection techniques. Our approach integrates ChatGPT as the LLM for interpreting task instructions and generating context-aware responses, while YOLOv8 is employed for robust object detection in dynamic environments. The proposed system interprets spoken task requirements, extracts contextual information, and engages with a dual-arm service robotic system for real-time task execution. We present a comprehensive evaluation of our method, demonstrating significant improvements in task understanding, response accuracy, and adaptability in various interactive scenarios. Experimental results validate the efficacy of our approach in enhancing HRI.