Welcome to Passio Nutrition-AI iOS SDK! When integrated into your app the SDK provides you with food recognition and nutrition assistant technology. The SDK creates a video preview layer and outputs foods recognized by our computer vision technology in the video feed of your live camera along with nutrition data related to the recognized foods.

As the developer, you have complete control of when to turn on/off the SDK and to configure the outputs which includes:

• food names (e.g. banana, hamburger, fruit salad, quest chocolate bar)

• lists of alternatives for recognized foods (e.g., soy milk would be an alternative of a visually recognized class milk)

• barcodes detected on food packages

• packaged foods recognized by the text detected on food packages

• nutrition information detected on food packages via Passio's Nutrition Facts reader which returns information written in Nutrition Facts labels

• nutrition information associated with the foods

• food weight and volume for certain foods

By default the SDK does not record/store any photos or videos. Instead, as the end user hovers over a food item with his/her camera phone, the SDK recognizes and identifies food items in real time. This hovering action is only transitory/temporary while the end user is pointing the camera at a particular item and is not recorded or stored within the SDK. As a developer, you can configure the SDK to capture images or videos and store them in your app.

OpenFoodFacts Terms of use

Passio Nutrition-AI SDK added data from Open Food Facts (https://en.openfoodfacts.org/). Each food that contains data from Open Food Facts will have the value set in its food origin list. In case you choose to display the these food items you agree to abide by the terms of the Open Food Facts license agreement (https://opendatacommons.org/licenses/odbl/1-0) and their terms of use (https://world.openfoodfacts.org/terms-of-use) and you will have to add to the UI the following license copy:

"This record contains information from Open Food Facts (https://en.openfoodfacts.org), which is made available here under the Open Database License (https://opendatacommons.org/licenses/odbl/1-0)"

Follow these steps to include the SDK into your mobile application project.

`<key>>NSCameraUsageDescription</key><string>For real-time food recognition</string>`.

implementation files('libs/passiolib-release.aar')

dependencies {
    // TensorFlow
    implementation 'org.tensorflow:tensorflow-lite:2.8.0'
    implementation 'org.tensorflow:tensorflow-lite-metadata:0.4.0'

    // CameraX
    def camerax_version = "1.3.0-alpha12"
    implementation "androidx.camera:camera-core:$camerax_version"
    implementation "androidx.camera:camera-camera2:$camerax_version"
    implementation "androidx.camera:camera-lifecycle:$camerax_version"
    implementation 'androidx.camera:camera-view:1.3.0-alpha02'
    implementation 'androidx.camera:camera-extensions:1.3.0-alpha02'

    // Barcode and OCR
    implementation 'com.google.android.gms:play-services-mlkit-text-recognition:19.0.0'
    implementation 'com.google.android.gms:play-services-mlkit-barcode-scanning:18.3.0'
}

android {
    compileOptions {
        sourceCompatibility JavaVersion.VERSION_1_8
        targetCompatibility JavaVersion.VERSION_1_8
    }
}

//npm.pkg.github.com/:_authToken=GITHUB_ACCESS_TOKEN
@passiolife:registry=https://npm.pkg.github.com

npm install @passiolife/nutritionai-react-native-sdk-v3

yarn add @passiolife/nutritionai-react-native-sdk-v3

cd ios; pod install

platform :ios, '13.0'

dependencies {
    // Add this line below for Passio SDK library
    implementation files("$rootDir/../node_modules/@passiolife/nutritionai-react-native-sdk-v3/android/libs/passiolib-release.aar")
    ...
}

buildscript {
    ext {
        ...
        minSdkVersion = 26
        ...
    }
}

 $ flutter pub add nutrition_ai

dependencies:
  nutrition_ai: ^3.0.3

import 'package:nutrition_ai/nutrition_ai.dart';

allprojects {
   repositories {
      ...
      flatDir {
         dirs project(':nutrition_ai').file('libs')
      }
   }
}

allprojects {
   repositories {
      ...
      flatDir {
         implementation (name: 'passiolib-release', ext: 'aar')
      }
   }
}

The recognizeSpeechRemote function is primarily used to fetch food items using a voice command, although it can be used to extract food items from a recipe in a text form as well.

The input to this function is just a string in free format, so the implementing side needs to do speech-to-text before using the API.

This function will be able to extract several pieces of data:

• meal action, is this food being added or removed from the logs

• meal time (breakfast, dinner, lunch or snack)

• date of log

• recognised name from the LLM

• portion and weight from the LLM

• nutritional data reference as PassioFoodDataInfo

Example of logging a morning breakfast:

UI Example

1. Create a screen where the user can record the voice logging command. Make sure to add the appropriate permissions.

2. The UI should enable the user to start/stop voice recording on a tap of a button. When the voice recording is done collect the string and use the SDK to extract food.

3. Once the SDK returns the results, show the list to the user with the option to deselect incorrect predictions.

The configuration process of the SDK has severable responsibilities:

• Validation of the license key

• Validating the currently present files

• Downloading or updating the files required by the SDK to do recognition

• Preparing the files to be used for inference

The SDK is configured using a PassioConfiguration object. This object is used to define the location of the files that the SDK requires.

SDK downloads the models

The default behaviour (or by setting the sdkDownloadsModels field to true) of the SDK is to download the required files. If the current files that are present on the device are older than the version of the SDK library, the SDK will download the newer version in the background, but still run the session with the older version of the files. Once the download is finished, the new files will be applied on the next session. Don't forget to add the import statement for the nutrition sdk.

Handling the configure status object

The result of the configuration process is a PassioStatus object, containing a PassioMode attribute along with the missingFiles, debugMessage, error object and the active models number. The attribute that needs to looked at is the mode.

PassioMode is an enum describing the current state of the Passio SDK configuration process:

• notReady -> The configuration process hasn't started yet.

• failedToConfigure -> There was an error during the configuration process.

• isBeingConfigured -> The SDK is still in the configuration process. Normally, you shouldn't receive this mode as a callback to the configure method. If you do please contact our support team.

• isDownloadingModels -> The files required by the SDK to work are not present and are currently being downloaded.

• isReadyForDetection -> The SDK is configured with the set of files defined by the activeModels attribute.

The missingFiles variable contains a list of all the files missing for the SDK to work optimally. When this list is not null, this doesn't mean that the SDK is not operational. The SDK may be initialized with older files, but it will advocate the download of the newest files through this list.

The debugMessage usually gives more verbose and human-readable information about the configuration process. It will always give an error message if the mode is FAILED_TO_CONFIGURE.

The activeModels will give the version of the files installed and ran by the SDK if the configuration process ran successfully i.e. if the mode is IS_READY_FOR_DETECTION.

Best practices

1. Make sure there is internet connection for the configuration process. The SDK needs to download the license file and the required files. If there is no internet connection the configuration status mode will be failedToConfigure.

2. The PassioStatus mode is notReady -> check debugMessage for the error, usually the wrong license key has been supplied or the files are missing.

3. The PassioStatus mode is isDownloadingModels -> register a PassioStatusListener through the setPassioStatusListener method as it will enable tracking of the download progress.

4. The PassioStatus mode is isReadyForDetection -> still check the missingFiles because the SDK might be running with an older version of the files and newer need to be downloaded.

The Passio SDK has the ability the recognise anything from simple ingredients like blueberries and almonds to complex cooked dishes like a beef burrito with salad and fries.

There are two types of food recognition, each of them with their own strengths and weaknesses.

The Remote image recognition approach is good when the accuracy of the results is top priority, and waiting for the response is not an issue. This use case is implemented by taking static images from the camera or the library of the device and sending them for recognition in an asynchronous fashion.

The Local model approach is good when speed is of the essence. This use case is implemented using continuous frame recognition from the camera. A callback is registered to capture the results as they are coming from the camera stream.

Remote image recognition

This API sends an image as base64 format to an LLM on Passio's backend, and returns a list of recognised items. The default behaviour of this function is to resize the image to 512 pixels (longer dimension is resized, the other is calculated to keep aspect ratio). Using the PassioImageResolution enum, the image can be either resized to 512, 1080 or keep the original resolution.

The response, presented as a list ofPassioAdvisorFoodInfo objects, contains the name, portion and weight in grams recognised by the LLM, as well as a PassioFoodDataInfo reference from Passio's nutritional database. To fetch the for the PassioFoodDataInfo object, use the fetchFoodItemForDataInfo function.

UI Example

1. Create a screen where the user can snap one or multiple images using the camera of the device

2. Upon clicking next, the recognizeImageRemote is invoked on each of the images in the list

3. Wait for all of the responses to come, add each results list to a final list of results. When the last asynchronous function is executed, present the final list to the user.

Local neural network model

To set up the local model and the continuous scanning mode, the camera preview and the recognition session need to be defined.

Camera preview

Add the UI element that is responsible for rendering the camera frames:

Start food detection

The type of food detection is defined by the FoodDetectionConfiguration object. To start the Food Recognition process a FoodRecognitionListener also has to be defined. The listener serves as a callback for all the different food detection processes defined by the FoodDetectionConfiguration. When the app is done with food detection, it should clear out the listener to avoid any unwanted UI updates.

The FoodCandidates object that is returned in the recognition callbacks contains three lists:

• detectedCandidates detailing the result of VISUAL detection

• barcodeCandidates detailing the result of BARCODE detection

• packagedFoodCandidates detailing the result of PACKAGED detection

Only the corresponding candidate lists will be populated (e.g. if you define detection types VISUAL and BARCODE, you will never receive a packagedFoodCandidates list in this callback).

Visual detection

A DetectedCandidate represents the result from running Passio's neural network, specialized for detecting foods like apples, salads, burgers etc. The properties of a detected candidate are:

• name

• passioID (unique identifier used to query the nutritional databse)

• confidence (measure of how accurate is the candidate, ranges from 0 to 1)

• boundingBox (a rectangle detailing the bounds of the recognised item within the image dimensions)

• alternatives (list of alternative foods that are visually or contextually similar to the recognised food)

• croppedImage (the image that the recognition was ran on)

UI Example

1. Implement the camera screen using the steps above

2. Create a result view that can have two states: scanning and result

3. If the callback returns an empty list, show the scanning state. If it returns the result, display the name from the detectedCandidate.name

Example of an image that produces a DetectedCandidate:

SDK Key

To use the SDK please make sure you receive your SDK license key from Passio. The SDK WILL NOT WORK without a valid SDK key.

To obtain an SDK key visit

Minimum requirements

These are the minimum requirements for every supported platform

The SDK can detect barcodes located on the packaging of packaged foods. To implement barcode scanning, the is needed.

Specifically for barcode scanning, these steps are required:

1. When defining a FoodDetectionConfiguration object, enable the detectBarcode flag.

2. In the recognitionResults callback, check the property candidate.barcodeCandidates. If this list is empty, no barcodes have been recognised.

3. If there is a single or multiple barcode candidates, use their value property and invoke this function to fetch the .

UI Example

1. Set up the camera preview and detection session to recognise barcodes

2. Create a result view with two states: search and result

3. If the candidate.barcodeCandidates is empty, show the search state. Else, fetch the PassioFoodItem, and use it's name and icon to show the result

Example of an image that produces multiple BarcodeCandidates:

Search

The Passio Nutrition AI SDK search functionality gives access to over 2.2 million food records in our nutritional database. The search api takes in a text query and returns a list of PassioFoodDataInfo results as well as a list of suggested searches

The PassioFoodDataInfo is used a reference object to fetch the full nutritional data. It has all of the attributes needed for displaying on a typical search screen: the name of the food, name of the brand (if possible), iconId, and a nutritional preview detailing the macro nutrients for a specified serving size and weight.

The search will return 50 items that best match the input term, as well as a list of suggested searches that are semantically or contextually close to the input term.

Example: searching for term "coffee"

• search results: "Coffee, Brewed", "Coffee, Breakfast Blend", Coffee - Brewed From Grounds", "Coffee, Brewed, Decaffeinated", "Coffee, Instant, Reconstituted Decaffeinated", ...

• suggested searches: "coffee with whole milk", "coffee with creamer", "iced coffee"

UI Example

1. Create an input text field to collect the user query

2. Create a horizontal list scroll to display the suggestions

3. Create a vertical list to display the results of the search

4. If the user clicks on a suggestion, pass that string to the search API and reload the UI with the new results

Food Icons

The SDK gives the ability to fetch food icons for a specific passioId. There are two functions that can be used: lookupIconsFor and fetchIconsFor.

1. lookupIconsFor does a local search of the icons without doing any networking calls. It returns two icons. One is a placeholder icon depending on the type of food associated with the passioId. The second one is cached icon from a previous fetchIconsFor call, it it exists.

2. fetchIconsFor does a networking call to fetch an icon associated with the passioId, but it will check the local cache and return a previously fetched icon.

Example of how to use both of these functions to first display the placeholder icon, and then fetch the remote icon:

RefCode

There are several functions that are used to retrieve the PassioFoodItem object, depending on the origin of the data. If the food item is being scanned using an image, the food item is fetched using either fetchFoodItemForPassioID or fetchFoodItemForProductCode. If search is being used to access to food item, the appropriate call is the fetchFoodItemForDataInfo.

But if there is a use case that requires to save just one attribute to a database, that will later on be used to retrieve the full PassioFoodItem object, then the refCode attribute is the answer. The refCode in combination with the fetchFoodItemForRefCode will retrieve the original food item object from Passio's nutritional database.

Using v2 PassioID in the v3 SDK

If the implementing app is transitioning from generation 2 to generation 3 of the SDK, and has already stored generation 2 PassioIDs, invoking the fetchFoodItemLegacy will retrieve the PassioFoodItem that was previously structured as PassioIDAttributes.

The Nutrition Advisor is an intelligent chatbot designed to offer personalised guidance on nutrition, diets, and healthy eating habits. Whether you're looking to explore a new diet, understand the nutritional value of certain foods, or find delicious and balanced recipes, the Nutrition Advisor is here to assist. Users can interact with the Nutrition Advisor through text messages, engaging in a conversational experience where they can ask questions about various diets like keto, vegan, Mediterranean, and more. The chatbot provides detailed insights into the benefits, potential drawbacks, and suitability of these diets based on individual goals and preferences.

Conversation flow

Use these steps to initialise the conversation and enable users to send messages:

1. To start a conversation initConversation has to be invoked. The response will either indicate a success state, which means message can be sent to the advisor. In case of an error, an error message will detail the cause.

2. Text messages are sent using the sendMessage API. The reply (if successful) will contain a PassioAdvisorResponse response. This object consist of:

• threadId - identifies the conversation

• messageId - identifies the specific message

• markupContent - text response from the advisor denoted with markup tags. This can be used to style the text when rendering

• rawConent - text response without the markup tags

• tools - list of actions that can be executed by the advisor. Only should be used by the SDK

• extracedIngredients - will contain a list of foods if the request to the advisor was an image recognition request or extract ingredients from text message request

1. Image are sent to recognition using sendImage. As mentioned above, in the PassioAdvisorResponse object, the extractedIngredients property will hold the recognised foods.

NutritionAdvisor.instance.initConversation { result ->
    when (result) {
        is PassioResult.Success -> /* the advisor can start acceping messages */
        is PassioResult.Error -> /* handle conversation init error */
    }
}

NutritionAdvisor.instance.sendMessage("Hi! I would like a recipe for a healthy burger") { result ->
    when (result) {
        is PassioResult.Error -> {
            val errorMessage = result.message
            // Handle error
        }
        is PassioResult.Success -> {
            val text = result.value.rawContent
            // Show text in a chat bubble
        }
    }
}

val bitmap = loadBitmap()
NutritionAdvisor.instance.sendImage(bitmap) { result ->
    when (result) {
        is PassioResult.Error -> {
            val errorMessage = result.message
            // Handle error
        }
        is PassioResult.Success -> {
            val foods = result.value.extractedIngredients
            // Show list of foods
        }
    }
}

import { NutritionAdvisor } from '@passiolife/nutritionai-react-native-sdk-v3'

const [loadingState, setLoadingState] = useState<SDKStatus>('Init')
  useEffect(() => {
    async function configure() {
      try {
            NutritionAdvisor.initConversation()
              .then((item) => {
                setLoadingState(
                  item?.status === 'Success' ? 'Success' : 'Error'
                )
              })
              .catch((error) => {
                setLoadingState(error)
              })
      } catch (err) {
        console.error(`PassioSDK Error ${err}`)
        setLoadingState('Error')
      }
    }
    configure()
  }, [])


 const response = await NutritionAdvisor.sendMessage(message)
 switch (response?.status) {
          case 'Success':
            // get your response here {response.response}
            return
          case 'Error':
          // get your erorr response here {response.message}
            return
        }

const response = await NutritionAdvisor.sendImage(imageURI)
 switch (response?.status) {
          case 'Success':
            // get your response here {response.response}
            return
          case 'Error':
          // get your erorr response here {response.message}
            return
        }

final result = await NutritionAdvisor.instance.initConversation();
switch (result) {
  case Success():
    // Initialization succeeded, the advisor can start accepting messages
    break;
  case Error():
    // Handle the init error here
    break;
}

final result = await NutritionAdvisor.instance.sendMessage('Hi! I would like a recipe for a healthy burger');
switch (result) {
  case Success():
    // Parse the response from result.value
    break;
  case Error():
    // Handle the error that occurred while sending the message
    break;
}

Uint8List bytes = loadImage();
final result = await NutritionAdvisor.instance.sendImage(bytes);
switch (result) {
  case Success():
    // The food items are found in imageResult.value.extractedIngredients
    break;
  case Error():
    // Handle the error that occurred while sending the message
    break;
}

Extract ingredients

In the case that a PassioAdvisorResponse contains a tool called searchMatchIngredient, the Advisor has the ability to parse the text response and return the list of foods. If a message that has this tool is passed to the fetchIngredients API, the returned response will contain foods in the extracedIngredients field.

This feature is particularly useful when trying to log foods when directly communicating with the chatbot. A common use case is when user ask for a specific recipe. They can then initiate the extraction of foods from the message, and see their nutrition value and finally log the entire meal in one action.

UI Example

1. Create a screen where the user can type messages and send images to the NutritionAdvisor. Upon initiating the screen, set up the conversation by calling initConversation.

2. When the user types a message in the input text field and taps "Send", invoke the sendMessage function. Because this is an asynchronous function, add an animation to indicate this behaviour.

3. If the response from the advisor contains a tool called searchMatchIngredient show an action button "Find foods" to initiate extraction of the foods. Invoke the fetchIngredients function using the response from the Advisor.

4. Create a view where the user can see the extracted foods, their serving size and have the ability to log them.

1. Add the ability for the user to select an image from the library of the phone. Once the image is obtained, invoke the sendImage to get the list of foods. If foods are recognised, the response will contain a list of foods, use the same result view to show them.

The main object containing the nutritional information is called PassioFoodItem. This object is fetched from Passio's backend using one of several fetchFoodItemFor... functions.

PassioFoodItem

The PassioFoodItem is a top level object that can represent a single food like a banana, or a complex recipe like caesar salad.

public struct PassioFoodItem: Codable {
    public let id: String
    public let name: String
    public let details: String
    public let iconId: String
    public let licenseCopy: String
    public let amount: PassioNutritionAISDK.PassioFoodAmount
    public let ingredients: [PassioNutritionAISDK.PassioIngredient]
    public let refCode: String?
}

data class PassioFoodItem(
    val id: String,
    val refCode: String,
    val name: String,
    val details: String,
    val iconId: String,
    val amount: PassioFoodAmount,
    val ingredients: List<PassioIngredient>,
)

export interface PassioFoodItem {
  refCode?: RefCode
  name: string
  details?: strin
  iconId: PassioID | string
  amount: PassioFoodAmount
  ingredients?: PassioIngredient[]
  weight: UnitMass
  isOpenFood?: boolean
  openFoodLicense?: string
  id: string
}

class PassioFoodItem {
  final PassioFoodAmount amount;
  final String details;
  final String iconId;
  final String id;
  final List<PassioIngredient> ingredients;
  final String name;
  final String refCode;
  final PassioID? scannedId;
 }

• The name attribute is used to display the name of the food, while the details contains additional information like the brand of the food or the name of the food which was used to populate the nutritional data

• iconId is used with the SDK function fetchIconFor to fetch a small image of the food

• refCode is a parameter that can be used later on to retrieve the full food item again

• amount details the available serving size as well as the currently selected serving quantity and unit, used when calculating nutrients

There are three functions that can be used to fetch nutrient information for the PassioFoodItem:

PassioNutritionAI.shared.fetchFoodItemFor(passioID: "VEG0025") { (foodItem) in
        
  if let foodItem {
  
      // Getting nutrients for the referent 100 grams
      let nutrientsRef = foodItem.nutrientsReference()
            
      // Getting nutrients for a specific weight
      let nutrients250g = foodItem.nutrients(weight: Measurement<UnitMass>(value: 250.0, unit: .grams))
      
      // Getting nutrients using the amount->selectedQuantity and
      // amount->selectedUnit
      let nutrientsServing = foodItem.nutrientsSelectedSize()
            
      // Weight of the ingredients
      let weight = foodItem.weight()
  }
}

 
   const passioFoodItem = await PassioSDK.fetchFoodItemForPassioID("VEG0025")
  
   // Getting nutrients for the referent 100 grams
   const nutrients =  PassioSDK.getNutrientsReferenceOfPassioFoodItem(passioFoodItem)
   
   // Getting nutrients for a specific weight
   const nutrients =  PassioSDK.getNutrientsOfPassioFoodItem(passioFoodItem,{unit:'g',value:250})
  
   // Getting nutrients using the amount->selectedQuantity and
   // amount->selectedUnit
   const nutrients =  PassioSDK.getNutrientsSelectedSizeOfPassioFoodItem(passioFoodItem)
 
   // Weight of the ingredients
   const val weight = passioFoodItem.weight()

    final foodItem = await NutritionAI.instance.fetchFoodItemForPassioID('VEG0025');
    // Getting nutrients for the referent 100 grams
    final nutrientsRef = foodItem!.nutrientsReference();
    // Getting nutrients for a specific weight
    final nutrients250g =
        foodItem.nutrients(UnitMass(250.0, UnitMassType.grams));
    // Getting nutrients using the amount->selectedQuantity and
    // amount->selectedUnit
    final nutrientsServing = foodItem.nutrientsSelectedSize();
    // Weight of the ingredients
    final weight = foodItem.weight();

The weight function is used to fetch the sum mass of all of the ingredients.

PassioFoodAmount

This class holds the information about possible serving sizes and serving units, as well as the currently selected unit and quantity. Example of values for food item "apples"

• serving units: small (2-3/4" dia), medium, large (3-1/4" dia), cup, oz, gram, ...

• serving sizes: 1 small (2-3/4" dia), 2 cups, 100 grams, ...

• selected quantity: 1

• selected unit: medium

Serving units holds the list of all of the units that the system has the correct weight for. Serving sizes holds the list of predefines matches of a quantity and a serving unit. The selected quantity and unit are used to calculate nutrients on the PassioFoodItem level. While these values can be changed, the initial values are set by the SDK as a default portion.

PassioIngredient

Every PassioFoodItem has a list of ingredients. A food ingredient holds the nutritional information along with the portion size of each ingredient.

public struct PassioIngredient: Codable {
    public let id: String
    public let name: String
    public let iconId: String
    public let amount: PassioFoodAmount
    public let referenceNutrients: PassioNutrients
    public let metadata: PassioFoodMetadata
    public let refCode: String?
}

data class PassioIngredient(
    val id: String,
    val refCode: String,
    val name: String,
    val iconId: String,
    val amount: PassioFoodAmount,
    val referenceNutrients: PassioNutrients,
    val metadata: PassioFoodMetadata,
)

export interface PassioIngredient {
  name: string
  id: PassioID
  iconId: PassioID | string
  weight?: UnitMass
  referenceNutrients?: PassioNutrients
  nutrients?: PassioNutrients
  metadata?: PassioFoodMetadata
  amount?: PassioFoodAmount
}

class PassioIngredient {
  final PassioFoodAmount amount;
  final String iconId;
  final String id;
  final PassioFoodMetadata metadata;
  final String name;
  final String refCode;
  final PassioNutrients referenceNutrients;
}

Similarly to #passiofooditem, the ingredient also has a name, iconId, refCode, and amount.

But, the most important part of the ingredient class is the PassioNutrients and it's methods. The referenceNutrients attribute holds the referent (in most cases 100 grams) macronutrients and micronutrients, and are used to calculate the resulting nutrient values when changing serving sizes of the whole PassioFoodItem.

Also, an ingredient has PassioMetadata field, storing the information on the origin of the nutritional data, barcode value, possible ingredients (if the PassioFoodItem is a packaged food), and a list of tags.

Fetching nutrients

PassioNutritionAI.shared.fetchFoodItemFor(passioID: "VEG0025") { (foodItem) in
  if let foodItem {
     let nutrients = foodItem.nutrientsReference()
     // Calories for 100 grams, values is in kCal
     let calories = nutrients.calories()?.value
     // Carbs for 100 grams, value is in grams
     let carbs = nutrients.carbs()?.value
     // Potassium for 100 grams, value is in milligrams
     let potassiumMilli = nutrients.potassium()?.value
     // Potassium for 100 grams, value is in grams
     let potassiumGrams = nutrients.potassium()?.gramsValue
  }
}

PassioSDK.instance.fetchFoodItemForPassioID("VEG0025") { foodItem ->
    val nutrients = foodItem!!.nutrientsReference()
    // Calories for 100 grams, values is in kCal
    val calories = nutrients.calories()?.value
    // Carbs for 100 grams, value is in grams
    val carbs = nutrients.carbs()?.value
    // Potassium for 100 grams, value is in milligrams
    val potassiumMilli = nutrients.potassium()?.value
    // Potassium for 100 grams, value is in grams
    val potassiumGrams = nutrients.potassium()?.gramsValue()
}

const passioFoodItem = await PassioSDKBridge.fetchFoodItemForPassioID("VEG0025")

   const nutrients =  PassioSDK.getNutrientsReferenceOfPassioFoodItem(passioFoodItem)
    // Calories for 100 grams, values is in kCal
     let calories = passioNutrients.calories?.value
     // Carbs for 100 grams, value is in grams
     let carbs = passioNutrients.carbs?.value
     // Potassium for 100 grams, value is in milligrams
     let potassiumMilli = passioNutrients.potassium?.value

    final foodItem = await NutritionAI.instance.fetchFoodItemForPassioID('VEG0025');
    final nutrients = foodItem!.nutrientsReference();
    // Calories for 100 grams, values is in kCal
    final calories = nutrients.calories?.value;
    // Carbs for 100 grams, value is in grams
    final carbs = nutrients.carbs?.value;
    // Potassium for 100 grams, value is in milligrams
    final potassiumMilli = nutrients.potassium?.value;
    // Potassium for 100 grams, value is in grams
    final potassiumGrams = nutrients.potassium?.gramsValue();

Single food item vs recipe

The PassioFoodItem class encompasses both single food items like an avocado as well as items with multiple ingredients such as homemade caprese salad. The difference between these two items is how is the PassioFoodAmount calculated from the top-level PassioFoodItem.

Homemade caprese salad:

• weight(): 176.5 grams

• amount->selectedQuantity = 1

• amount->selectedUnit = "serving"

• ingredients: 1. fresh sliced cheese, 84 grams, amount->selectedQuantity = 3, amount-> selectedUnit = "oz" 2. balsamic vinegar, 16 grams, amount->selectedQuantity = 1, amount-> selectedUnit = "tbsp" 3. fresh basil, 3 grams, amount->selectedQuantity = 6, amount-> selectedUnit = "leaves" 4. tomatoes, 60 grams, amount->selectedQuantity = 3, amount-> selectedUnit = "medium slice" 5. olive oil, 13.5 grams, amount->selectedQuantity = 1, amount-> selectedUnit = "tbsp"

Invoking the function foodItem.nutrientsServingSize() will fetch the nutrients for the whole recipe, calculated by the weight of the serving size "1 serving".

Avocado:

• weight(): 201 grams

• amount->selectedQuantity = 1

• amount->selectedUnit = "avocado"

• ingredients: 1. avodaco, raw, 201 grmas, amount->selectedQuantity = 1, amount->selectedUnit = "avocado"

A single food item will always have only one ingredient. Also, the amount object of the PassioFoodItem will be the same as the amount of the first ingredient. The ingredient is the item in the database used for nutritional data. For example, If the SDK recognises "milk" during the visual detection process, the default food item for "milk" is "milk, whole, 3.25% milkfat, without added vitamin a and vitamin d".

UI Example

1. Fetch the PassioFoodItem

2. Use the name, details and iconId to create the food header view

3. Use the nutrientsSelectedSize->calories, carbs, protein and fat to show the marcos

4. Use the amount class to create the serving size view

5. Give the user the option to log the food

Suggestions

The SDK provides the ability to fetch a predefined set of suggested foods for logging, depending on the current time of day

enum class PassioMealTime(val mealName: String) {
    BREAKFAST("breakfast"),
    LUNCH("lunch"),
    DINNER("dinner"),
    SNACK("snack")
}

fun fetchSuggestions(
    mealTime: PassioMealTime,
    callback: (results: List<PassioFoodDataInfo>) -> Unit
)

Example of fetching suggestions for snack meal time:

banana, coffee, black tea, coke, cheddar cheese, chocolate chip cookies, potato chips, blueberry muffin, plain yogurt, strawberries, ...

Meal Plans

Meal Plans provide users suggestions which foods to consume depending on their dietary preference and time of day. The Meal Plan api is divided into two functions:

• fetchMealPlans that is used to retrieve all of the possible meal plans. These meal plans usually correlate with a specific diet like "Keto Diet".

• fetchMealPlanForDay is an api that is used to fetch specific foods recommended for a certain day of a target meal plan.

fun fetchMealPlans(callback: (result: List<PassioMealPlan>) -> Unit)

fun fetchMealPlanForDay(
    mealPlanLabel: String,
    day: Int,
    callback: (result: List<PassioMealPlanItem>) -> Unit
)

data class PassioMealPlan(
    val mealPlanTitle: String,
    val mealPlanLabel: String,
    val carbTarget: Int,
    val proteinTarget: Int,
    val fatTarget: Int
)

data class PassioMealPlanItem(
    val dayNumber: Int,
    val dayTitle: String,
    val mealTime: PassioMealTime,
    val meal: PassioFoodDataInfo
)