In this paper, we present our submission to Task 2 of the BLP-2025 shared task on code generation from Bangla instructions. Our approach focused on enhancing instruction quality through translation and improving model performance with a two-stage ensemble strategy. We evaluated two proprietary and several open-source models under three instruction settings: original Bangla instructions, Bangla instructions translated into English using Facebook NLLB, and instructions rewritten in English with GPT-4.1. Experimental results showed that GPT-4.1-rewritten instructions consistently achieved the highest accuracy across models. For final predictions, we used a two-stage ensemble, achieving a pass@1 score of 80.0% on the hidden test set and securing 12th place on the official leaderboard. Additionally, we conducted a qualitative analysis of selected translations to illustrate how variations in instruction phrasing influenced model outputs.