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Use Case

AI-generated personalized fitness routines

Generative AI models, like Large Language Models (LLMs) and image generators, can be employed to create personalized fitness routines. Algorithms like ChatGPT can be used to interact with users, collect important information like fitness goals, previous fitness experience, and available workout resources. The insights will be processed by the AI to generate tailor-made workout regimes for users, considering their unique needs. Furthermore, AI models empowered with image generation capabilities can provide visual illustrations for each exercise, greatly enhancing the comprehensibility and effectiveness of the workout routines. By analyzing user-provided input or progress photos, they can adaptively modify and optimize fitness plans over time. For example, an AI can suggest lower-intensity workouts to a user indicating fatigue or muscle pain. This enables continuous improvement and personalization for optimal fitness results.

How to build with Clevis

Text Input

Ask the user to input their fitness goals and any specific requirements.

Prompt ChatGPT

Prompt ChatGPT to generate a conversation with the user to collect more information about their fitness goals, preferences, and any limitations.

HTTP Request

Send an HTTP request to retrieve relevant exercise data from a fitness API based on the user's input and preferences.

Generate Image (DALL-E)

Generate visual representations of the recommended exercises using DALL-E.

Display Output

Display the personalized fitness routines, including exercise instructions and visual representations, to the user in a user-friendly format.

The AI application we are discussing is an example application, you can build using Clevis, a code-free AI development utility. One can easily realize similar use-case applications using Clevis. In our context, the application is named 'AI-generated personalized fitness routines'. It is designed to use AI to generate customized fitness routines based on a user's specific requirements and goals.

The flow of the process begins with the Text Input step where the user inputs their fitness aspirations and special prerequisites. Sequentially, in the ChatGPT phase, OpenAI's ChatGPT model is implemented to further communicate with the user and gather additional detailed information about their fitness objectives, preferences, and any constraints.

Next, during the Http Request step, the app sends an HTTP request and retrieves pertinent workout data from a fitness API corresponding to the user's input and preferences. Thereafter, in the DALL·E step, DALL-E is employed to produce visual renditions of the suggested exercises. Finally, in the Display Output step, the personalized fitness routines are displayed. These consist of instructions for the exercises and their visual representations, rendered in a format that is comfortable for the client to comprehend.

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