Predicting the full appearance of a partially visible object, known as amodal content completion, remains a challenging task. Many existing approaches struggle to recover missing regions without leaving artifacts from the occluding objects or generating out of context content. To address this limitation, we introduce a two-stage framework that incorporates a weighted mask together with multiple layers of enhanced gated convolution. This design allows the model to focus more on features extracted from the visible region of the occluded object and to propagate its effect through the network. We also integrate weighted global contextual information with these extracted features, which the model uses to refine the initial output produced by the model's first stage. We evaluate our approach on the COCOA dataset and two of its subsets. The experimental results demonstrate that the weighted mask effectively helps the model prioritize the visible areas of partially occluded objects, leading to reconstructions with richer semantic details and more contextually accurate content than the baseline models, particularly for objects with uniform textures such as animals.