There are three essential components in the model: 1. Co:here Classify model that has been trained on almost 500,000 real training data (sentence, emotion) and is capable of using NLP to truncate and classify large prompts into a range of emotions. 2. Co:here Embedding + Sklearn model that transforms the user prompt to a list of floats (embeds) and performs semantic search in a massive data base of real, labeled, embedded sentences to find the closest matching example. 3. Co:here Generate model that receives user input, classified emotions, previous calls, and some hard coded psychological information to generate an accurate therapist response. The model has memory of past inputs and thus can create a coherent conversation. These components work together in the back end, while the front end is a webpage that user can access to send information and read model's responses. Ultimately, the project aims to create an ongoing conversation with Co:DY and user, providing emotional support, helping users work through problems, and offering tailored advice.