This article provides a step-by-step guide on how to build a chat summarizer using the Cohere API and deploy it as a web application using Gradio. The article explains that text summarization is a useful tool for making sense of large volumes of text, and it has various applications in natural language processing. The article then introduces Cohere, a cloud-based natural language processing platform that offers chat summarization as one of its features. It explains how to use the Cohere API to generate chat summaries by analyzing the text of the conversation and identifying important information. The article also provides code examples for initializing the Cohere client, providing input for the summary, generating the summary, and outputting the summary. Additionally, the article discusses how to deploy the chat summarizer using Gradio, a user interface library for machine learning models. It explains how to import the necessary libraries, initialize the Cohere client, define the chat summarizer function, and create the Gradio interface. The article concludes by highlighting the importance of chat summarization in saving time and improving productivity in today’s digital world. It encourages readers to try out summarization features and provide feedback on their experience.