> For the complete documentation index, see [llms.txt](https://docs.january.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.january.ai/mobile-sdks/readme/photo-scanning.md).

# Photo scanning

Use `client.photoScanning` to analyze a meal image and submit natural-language corrections.

{% hint style="warning" %}
Meal images and inferred nutrition may be sensitive user data. Obtain appropriate consent, minimize retention, and never write image contents or results to application logs.
{% endhint %}

## Scan a public image URL

```swift
let scan = try await client.photoScanning.scan(
    .init(image: publicImageURL, endUserID: userID)
)
```

## Scan image data

Encode image bytes as a data URI:

```swift
let encoded = imageData.base64EncodedString()
let dataURI = "data:image/jpeg;base64,\(encoded)"

let scan = try await client.photoScanning.scan(
    .init(image: dataURI, endUserID: userID)
)
```

The response can contain a meal name, detected foods, total nutrients, confidence values, and glucose impact.

## Correct a result

```swift
guard
    let mealName = scan.mealName,
    let detections = scan.detections
else { return }

let corrected = try await client.photoScanning.correct(
    .init(
        mealName: mealName,
        detections: detections,
        userInput: "Remove the fries and rename this grilled chicken sandwich.",
        endUserID: userID
    )
)
```