We present TextLabel, an open-source web application prototype built on the SnorkelAI framework, designed to streamline and accelerate distributed text data labeling using weak supervision through label functions. By providing a click-and-select interface with predefined and extendable components, TextLabel enables users to construct and evaluate complete text classification pipelines without the need for extensive coding, except for creating custom labeling functions. This approach streamlines the end-to-end workflow of text annotation, model development, and evaluation, making it an efficient and accessible prototype for diverse text classification tasks. This paper provides a detailed account of the annotation process, the guiding design principles, and the implementation strategies, all of which were refined through early field test results and user feedback. With its user-friendly interface and open accessibility, TextLabel addresses the growing demand for tools that enhance the efficiency of data annotation, catering to a broad audience of practitioners and researchers. (GitHub: github.com/marie2501/text-label-application)