**Endpoint:** `/ping` **Description:** This endpoint is designed to check the health and responsiveness of the API server. When a `GET` request is made to `/ping`, the server will respond with a simple acknowledgment message, indicating that the service is operational. The response typically includes a status code of 200 OK and a JSON object containing a message such as `{"status":"pong"}`. This is useful for monitoring purposes and to confirm connectivity with the API.
{"status":"OK"}
curl --location --request GET 'https://zylalabs.com/api/6606/gcpaws+vision+api/9691/ping' --header 'Authorization: Bearer YOUR_API_KEY'
Header | Description |
---|---|
Authorization
|
[Required] Should be Bearer access_key . See "Your API Access Key" above when you are subscribed. |
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The Safe Search Detection endpoint returns a JSON object containing likelihood ratings for explicit content categories, including adult, spoof, medical, violence, and racy. Each category indicates the likelihood of the content being present, with values such as "LIKELY," "POSSIBLE," or "VERY_LIKELY."
The key fields in the response data include "adult," "spoof," "medical," "violence," and "racy." Each field provides a likelihood rating that helps in content moderation decisions.
The response data is organized in a JSON format, encapsulated within a "responses" array. Each response contains a "safeSearchAnnotation" object that holds the likelihood ratings for various content categories.
The Ping endpoint provides a simple acknowledgment of the API's operational status. It returns a JSON object with a "status" field, typically indicating "OK," confirming that the service is responsive.
The Safe Search Detection endpoint accepts an image file as input, which can be provided in various formats such as JPEG or PNG. Users can customize their requests by submitting different images for analysis.
Users can utilize the returned likelihood ratings to implement content moderation strategies, filtering out images based on the explicit content categories. For example, images rated "VERY_LIKELY" for adult content can be flagged for review.
Typical use cases include content moderation for social media platforms, image tagging for adult content filtering, and compliance with community guidelines in applications that handle user-generated content.
Data accuracy is maintained through the use of advanced pre-trained machine learning models from Google Cloud Vision and AWS Rekognition, which are continuously updated and refined based on extensive training datasets and user feedback.
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