vit-base-nsfw-detector

/* Instructions:
* - Place this script in an HTML file using the <script type="module"> tag.
* - Ensure the HTML file is served over a local or remote server (e.g., using Python's http.server, Node.js server, or similar).
* - Replace 'https://example.com/path/to/image.jpg' in the classifyImage function call with the URL of the image you want to classify.
*
* Example of how to include this script in HTML:
* <script type="module" src="path/to/this_script.js"></script>
*
* This setup ensures that the script can use imports and perform network requests without CORS issues.
*/
import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';

// Since we will download the model from HuggingFace Hub, we can skip the local model check
env.allowLocalModels = false;

// Load the image classification model
const classifier = await pipeline('image-classification', 'AdamCodd/vit-base-nsfw-detector');

// Function to fetch and classify an image from a URL
async function classifyImage(url) {
  try {
    const response = await fetch(url);
    if (!response.ok) throw new Error('Failed to load image');

    const blob = await response.blob();
    const image = new Image();
    const imagePromise = new Promise((resolve, reject) => {
      image.onload = () => resolve(image);
      image.onerror = reject;
      image.src = URL.createObjectURL(blob);
    });

    const img = await imagePromise; // Ensure the image is loaded
    const classificationResults = await classifier([img.src]); // Classify the image
    console.log('Predicted class: ', classificationResults[0].label);
  } catch (error) {
    console.error('Error classifying image:', error);
  }
}

// Example usage
classifyImage('https://example.com/path/to/image.jpg');
// Predicted class: sfw

/* Instructions:
* - Place this script in an HTML file using the <script type="module"> tag.
* - Ensure the HTML file is served over a local or remote server (e.g., using Python's http.server, Node.js server, or similar).
* - Replace 'https://example.com/path/to/image.jpg' in the classifyImage function call with the URL of the image you want to classify.
*
* Example of how to include this script in HTML:
* <script type="module" src="path/to/this_script.js"></script>
*
* This setup ensures that the script can use imports and perform network requests without CORS issues.
*/
import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';

// Since we will download the model from HuggingFace Hub, we can skip the local model check
env.allowLocalModels = false;

// Load the image classification model
const classifier = await pipeline('image-classification', 'AdamCodd/vit-base-nsfw-detector');

// Function to fetch and classify an image from a URL
async function classifyImage(url) {
  try {
    const response = await fetch(url);
    if (!response.ok) throw new Error('Failed to load image');

    const blob = await response.blob();
    const image = new Image();
    const imagePromise = new Promise((resolve, reject) => {
      image.onload = () => resolve(image);
      image.onerror = reject;
      image.src = URL.createObjectURL(blob);
    });

    const img = await imagePromise; // Ensure the image is loaded
    const classificationResults = await classifier([img.src]); // Classify the image
    console.log('Predicted class: ', classificationResults[0].label);
  } catch (error) {
    console.error('Error classifying image:', error);
  }
}

// Example usage
classifyImage('https://example.com/path/to/image.jpg');
// Predicted class: sfw

/* Instructions:
* - Place this script in an HTML file using the <script type="module"> tag.
* - Ensure the HTML file is served over a local or remote server (e.g., using Python's http.server, Node.js server, or similar).
* - Replace 'https://example.com/path/to/image.jpg' in the classifyImage function call with the URL of the image you want to classify.
*
* Example of how to include this script in HTML:
* <script type="module" src="path/to/this_script.js"></script>
*
* This setup ensures that the script can use imports and perform network requests without CORS issues.
*/
import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';

// Since we will download the model from HuggingFace Hub, we can skip the local model check
env.allowLocalModels = false;

// Load the image classification model
const classifier = await pipeline('image-classification', 'AdamCodd/vit-base-nsfw-detector');

// Function to fetch and classify an image from a URL
async function classifyImage(url) {
  try {
    const response = await fetch(url);
    if (!response.ok) throw new Error('Failed to load image');

    const blob = await response.blob();
    const image = new Image();
    const imagePromise = new Promise((resolve, reject) => {
      image.onload = () => resolve(image);
      image.onerror = reject;
      image.src = URL.createObjectURL(blob);
    });

    const img = await imagePromise; // Ensure the image is loaded
    const classificationResults = await classifier([img.src]); // Classify the image
    console.log('Predicted class: ', classificationResults[0].label);
  } catch (error) {
    console.error('Error classifying image:', error);
  }
}

// Example usage
classifyImage('https://example.com/path/to/image.jpg');
// Predicted class: sfw

/* Instructions:
* - Place this script in an HTML file using the <script type="module"> tag.
* - Ensure the HTML file is served over a local or remote server (e.g., using Python's http.server, Node.js server, or similar).
* - Replace 'https://example.com/path/to/image.jpg' in the classifyImage function call with the URL of the image you want to classify.
*
* Example of how to include this script in HTML:
* <script type="module" src="path/to/this_script.js"></script>
*
* This setup ensures that the script can use imports and perform network requests without CORS issues.
*/
import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';

// Since we will download the model from HuggingFace Hub, we can skip the local model check
env.allowLocalModels = false;

// Load the image classification model
const classifier = await pipeline('image-classification', 'AdamCodd/vit-base-nsfw-detector');

// Function to fetch and classify an image from a URL
async function classifyImage(url) {
  try {
    const response = await fetch(url);
    if (!response.ok) throw new Error('Failed to load image');

    const blob = await response.blob();
    const image = new Image();
    const imagePromise = new Promise((resolve, reject) => {
      image.onload = () => resolve(image);
      image.onerror = reject;
      image.src = URL.createObjectURL(blob);
    });

    const img = await imagePromise; // Ensure the image is loaded
    const classificationResults = await classifier([img.src]); // Classify the image
    console.log('Predicted class: ', classificationResults[0].label);
  } catch (error) {
    console.error('Error classifying image:', error);
  }
}

// Example usage
classifyImage('https://example.com/path/to/image.jpg');
// Predicted class: sfw

/* Instructions:
* - Place this script in an HTML file using the <script type="module"> tag.
* - Ensure the HTML file is served over a local or remote server (e.g., using Python's http.server, Node.js server, or similar).
* - Replace 'https://example.com/path/to/image.jpg' in the classifyImage function call with the URL of the image you want to classify.
*
* Example of how to include this script in HTML:
* <script type="module" src="path/to/this_script.js"></script>
*
* This setup ensures that the script can use imports and perform network requests without CORS issues.
*/
import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';

// Since we will download the model from HuggingFace Hub, we can skip the local model check
env.allowLocalModels = false;

// Load the image classification model
const classifier = await pipeline('image-classification', 'AdamCodd/vit-base-nsfw-detector');

// Function to fetch and classify an image from a URL
async function classifyImage(url) {
  try {
    const response = await fetch(url);
    if (!response.ok) throw new Error('Failed to load image');

    const blob = await response.blob();
    const image = new Image();
    const imagePromise = new Promise((resolve, reject) => {
      image.onload = () => resolve(image);
      image.onerror = reject;
      image.src = URL.createObjectURL(blob);
    });

    const img = await imagePromise; // Ensure the image is loaded
    const classificationResults = await classifier([img.src]); // Classify the image
    console.log('Predicted class: ', classificationResults[0].label);
  } catch (error) {
    console.error('Error classifying image:', error);
  }
}

// Example usage
classifyImage('https://example.com/path/to/image.jpg');
// Predicted class: sfw

/* Instructions:
* - Place this script in an HTML file using the <script type="module"> tag.
* - Ensure the HTML file is served over a local or remote server (e.g., using Python's http.server, Node.js server, or similar).
* - Replace 'https://example.com/path/to/image.jpg' in the classifyImage function call with the URL of the image you want to classify.
*
* Example of how to include this script in HTML:
* <script type="module" src="path/to/this_script.js"></script>
*
* This setup ensures that the script can use imports and perform network requests without CORS issues.
*/
import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';

// Since we will download the model from HuggingFace Hub, we can skip the local model check
env.allowLocalModels = false;

// Load the image classification model
const classifier = await pipeline('image-classification', 'AdamCodd/vit-base-nsfw-detector');

// Function to fetch and classify an image from a URL
async function classifyImage(url) {
  try {
    const response = await fetch(url);
    if (!response.ok) throw new Error('Failed to load image');

    const blob = await response.blob();
    const image = new Image();
    const imagePromise = new Promise((resolve, reject) => {
      image.onload = () => resolve(image);
      image.onerror = reject;
      image.src = URL.createObjectURL(blob);
    });

    const img = await imagePromise; // Ensure the image is loaded
    const classificationResults = await classifier([img.src]); // Classify the image
    console.log('Predicted class: ', classificationResults[0].label);
  } catch (error) {
    console.error('Error classifying image:', error);
  }
}

// Example usage
classifyImage('https://example.com/path/to/image.jpg');
// Predicted class: sfw

/* Instructions:
* - Place this script in an HTML file using the <script type="module"> tag.
* - Ensure the HTML file is served over a local or remote server (e.g., using Python's http.server, Node.js server, or similar).
* - Replace 'https://example.com/path/to/image.jpg' in the classifyImage function call with the URL of the image you want to classify.
*
* Example of how to include this script in HTML:
* <script type="module" src="path/to/this_script.js"></script>
*
* This setup ensures that the script can use imports and perform network requests without CORS issues.
*/
import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';

// Since we will download the model from HuggingFace Hub, we can skip the local model check
env.allowLocalModels = false;

// Load the image classification model
const classifier = await pipeline('image-classification', 'AdamCodd/vit-base-nsfw-detector');

// Function to fetch and classify an image from a URL
async function classifyImage(url) {
  try {
    const response = await fetch(url);
    if (!response.ok) throw new Error('Failed to load image');

    const blob = await response.blob();
    const image = new Image();
    const imagePromise = new Promise((resolve, reject) => {
      image.onload = () => resolve(image);
      image.onerror = reject;
      image.src = URL.createObjectURL(blob);
    });

    const img = await imagePromise; // Ensure the image is loaded
    const classificationResults = await classifier([img.src]); // Classify the image
    console.log('Predicted class: ', classificationResults[0].label);
  } catch (error) {
    console.error('Error classifying image:', error);
  }
}

// Example usage
classifyImage('https://example.com/path/to/image.jpg');
// Predicted class: sfw

/* Instructions:
* - Place this script in an HTML file using the <script type="module"> tag.
* - Ensure the HTML file is served over a local or remote server (e.g., using Python's http.server, Node.js server, or similar).
* - Replace 'https://example.com/path/to/image.jpg' in the classifyImage function call with the URL of the image you want to classify.
*
* Example of how to include this script in HTML:
* <script type="module" src="path/to/this_script.js"></script>
*
* This setup ensures that the script can use imports and perform network requests without CORS issues.
*/
import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';

// Since we will download the model from HuggingFace Hub, we can skip the local model check
env.allowLocalModels = false;

// Load the image classification model
const classifier = await pipeline('image-classification', 'AdamCodd/vit-base-nsfw-detector');

// Function to fetch and classify an image from a URL
async function classifyImage(url) {
  try {
    const response = await fetch(url);
    if (!response.ok) throw new Error('Failed to load image');

    const blob = await response.blob();
    const image = new Image();
    const imagePromise = new Promise((resolve, reject) => {
      image.onload = () => resolve(image);
      image.onerror = reject;
      image.src = URL.createObjectURL(blob);
    });

    const img = await imagePromise; // Ensure the image is loaded
    const classificationResults = await classifier([img.src]); // Classify the image
    console.log('Predicted class: ', classificationResults[0].label);
  } catch (error) {
    console.error('Error classifying image:', error);
  }
}

// Example usage
classifyImage('https://example.com/path/to/image.jpg');
// Predicted class: sfw