Pneumonia was a severe lung infection that viruses or bacteria could cause and could range in severity. Streptococcus pneumoniae was a common bacterium that could cause lifethreatening pneumonia if left untreated. This infection caused inflammation in the lungs, making it difficult to breathe and reducing the amount of oxygen the body received. Pneumonia could affect one or both lungs, and it occurs when fluid or pusfilled the air sacs (alveoli) in the lungs. In remote areas, an automated system for detecting pneumonia would have been very helpful in treating patients quickly. Convolutional Neural Networks (CNNs) were a popular deep learning algorithm used to classify diseases by analyzing medical images, and they had successfully detected pneumonia. Pretrained CNN models that had learned features from large datasets could benefit image classification tasks. By analyzing chest X-ray images, a deep learning algorithm could identify whether a patient had pneumonia, with an accuracy rate of almost 90%. Users could upload an X-ray image to the Streamlit web application and view the results