The Nutrition Data from Text API is a service that utilizes natural language processing (NLP) techniques to extract detailed nutrition information from textual inputs such as food descriptions, recipes, or menus.

The API employs advanced NLP algorithms to parse and analyze textual inputs, identifying key information related to food items, quantities, and ingredients. It then maps this information to a comprehensive nutrition database to generate accurate nutrition profiles.

The API is capable of processing a wide range of text inputs, including but not limited to food blogs, recipes, menu descriptions, ingredient lists, and dietary journals.

The API aims to provide highly accurate nutrition data by leveraging a robust nutrition database and sophisticated parsing algorithms. However, the accuracy may vary depending on the quality and specificity of the input text.

Yes, the API includes an intelligent feature for custom portioning. It automatically scales the extracted nutrition data based on specified quantities of individual food items or ingredients within the text.

The GET Facts endpoint returns a list of nutrition information extracted from the provided text. Each entry includes details such as calories, fat content, protein, sodium, potassium, cholesterol, carbohydrates, fiber, and sugar for each food item.

Key fields in the response data include "name" (food item), "calories," "fat_total_g," "fat_saturated_g," "protein_g," "sodium_mg," "potassium_mg," "cholesterol_mg," "carbohydrates_total_g," "fiber_g," and "sugar_g."

The response data is organized as a JSON array, where each object represents a food item with its corresponding nutrition information. Each object contains key-value pairs for various nutritional metrics.

The GET Facts endpoint accepts a "query" parameter, which allows users to specify the text input containing food items and their quantities. This enables the API to extract and scale nutrition data accordingly.

Users can customize their data requests by including specific quantities of ingredients in the "query" parameter. This allows the API to scale the nutrition data based on the specified amounts rather than defaulting to a 100g serving size.

The API sources its nutrition data from a comprehensive nutrition database that includes information from food manufacturers, nutritional studies, and established dietary guidelines to ensure accuracy and reliability.

Data accuracy is maintained through continuous updates to the nutrition database, validation against reputable sources, and the use of advanced NLP algorithms that enhance the extraction process from diverse text inputs.

Typical use cases include dietary tracking apps that log nutritional intake, recipe recommendation platforms that analyze nutritional profiles, and healthcare solutions that assist in managing dietary restrictions and monitoring nutrition.

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