Intelligent Nutrition Component Analysis and Recording Assistant
This workflow receives users' dietary records via Telegram, including text and voice messages. It utilizes AI technology to intelligently analyze the nutritional components of the ingredients and automatically stores the structured data in Google Sheets. It addresses the cumbersome issues of traditional dietary recording, supporting health management, exercise nutrition tracking, and medical rehabilitation, providing users who are concerned about dietary health with a convenient and efficient tool for recording and analysis.
Workflow Name
Intelligent Nutrition Component Analysis and Recording Assistant
Key Features and Highlights
This workflow receives users’ dietary records via Telegram (supporting both text and voice messages). It leverages OpenAI’s GPT-4 model to intelligently estimate and structurally analyze the ingredients and their nutritional components (such as calories, protein, carbohydrates, fats, and electrolytes) within the dietary content. The detailed nutritional data is then automatically stored in a Google Sheets spreadsheet, enabling users to conveniently track and manage their dietary health over time.
Core Problems Addressed
- Automatic recognition and transcription of voice dietary records, eliminating the hassle and inconvenience of traditional input methods.
- Intelligent estimation of food nutrition information using AI models, avoiding the complexity and errors of manual calculations.
- Automatic archiving of structured data to facilitate subsequent data organization, analysis, and nutritional intake evaluation.
Application Scenarios
- Health Management: Assists nutrition-conscious individuals in quickly recording daily nutrient intake.
- Sports and Fitness: Provides athletes and fitness enthusiasts with a scientific nutrition tracking tool.
- Medical Rehabilitation: Supports doctors and nutritionists in monitoring patient diets and formulating personalized dietary plans.
- Daily Life: Enables general users to easily log dietary details through chat or voice messages.
Main Process Steps
- Message Reception: Monitor and receive users’ dietary text or voice messages via a Telegram bot.
- Voice Processing: For voice messages, invoke OpenAI’s audio transcription API to convert speech to text.
- Input Preparation: Standardize the input by setting a unified
chatInputtext field based on the message type. - Nutritional Component Analysis: Use the OpenAI GPT-4 model to estimate the content of each nutrient based on the dietary description and output structured data in JSON format.
- Data Splitting and Processing: Split the nutritional data list into individual records and append the current date information.
- Storage and Archiving: Automatically append each processed nutritional data entry into a Google Sheets document.
- User Feedback: Automatically send a confirmation message to the user indicating successful saving of dietary data.
Involved Systems and Services
- Telegram: Channel for receiving and sending messages, supporting both text and voice input.
- OpenAI GPT-4: Core AI engine for natural language understanding and nutrition estimation.
- OpenAI Audio Transcription Service: Converts voice messages to text.
- Google Sheets: Data storage and archiving platform for easy review and analysis.
Target Users and Value
- Individual users focused on dietary health, especially those seeking to simplify the dietary recording process.
- Nutritionists, health consultants, and personal trainers for efficient management of client dietary data.
- Sports and fitness enthusiasts to scientifically monitor nutrient intake.
- Medical rehabilitation professionals assisting in patient diet management.
This workflow significantly enhances the convenience and intelligence of dietary recording by integrating AI technology with multi-platform services, providing users with a one-stop nutrition tracking solution.