SpiffGreen/cnn-text-classification.html

## cnn-text-classification.html
<html>
  <head>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
  </head>
  <body>
    <script>
      const urls = {
        model:
          "https://storage.googleapis.com/tfjs-models/tfjs/sentiment_cnn_v1/model.json",
        metadata:
          "https://storage.googleapis.com/tfjs-models/tfjs/sentiment_cnn_v1/metadata.json",
      };

      async function loadModel(url) {
        try {
          const model = await tf.loadLayersModel(url);
          return model;
        } catch (err) {
          console.log(err);
        }
      }

      async function loadMetadata(url) {
        try {
          const metadataJson = await fetch(url);
          const metadata = await metadataJson.json();
          return metadata;
        } catch (err) {
          console.log(err);
        }
      }

      const padSequences = (sequences, metadata) => {
        return sequences.map((seq) => {
          if (seq.length > metadata.max_len) {
            seq.splice(0, seq.length - metadata.max_len);
          }
          if (seq.length < metadata.max_len) {
            const pad = [];
            for (let i = 0; i < metadata.max_len - seq.length; ++i) {
              pad.push(0);
            }
            seq = pad.concat(seq);
          }
          return seq;
        });
      };

      function predict(text, model, metadata) {
        const inputText = text
          .trim()
          .toLowerCase()
          .replace(/(\.|\,|!)/g, "")
          .split(" ");
        const sequence = inputText.map((word) => {
          const wordIndex = metadata.word_index[word];
          if (typeof wordIndex === "undefined") {
            return 2; //oov_index
          }

          return wordIndex + metadata.index_from;
        });

        const paddedSequence = padSequences([sequence], metadata);
        const input = tf.tensor2d(paddedSequence, [1, metadata.max_len]);

        const predictOut = model.predict(input);
        const score = predictOut.dataSync()[0];
        predictOut.dispose();
        return score;
      }

      // const model = await loadModel();
      // const metadata = await getMetaData();

      Promise.all([loadModel(urls.model), loadMetadata(urls.metadata)]).then(([model, metadata]) => {
        const result = predict("you are a good person", model, metadata);
        console.log("Result: ", result);
      })

    </script>
  </body>
</html>
	<html>
	<head>
	<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
	</head>
	<body>
	<script>
	const urls = {
	model:
	"https://storage.googleapis.com/tfjs-models/tfjs/sentiment_cnn_v1/model.json",
	metadata:
	"https://storage.googleapis.com/tfjs-models/tfjs/sentiment_cnn_v1/metadata.json",
	};

	async function loadModel(url) {
	try {
	const model = await tf.loadLayersModel(url);
	return model;
	} catch (err) {
	console.log(err);
	}
	}

	async function loadMetadata(url) {
	try {
	const metadataJson = await fetch(url);
	const metadata = await metadataJson.json();
	return metadata;
	} catch (err) {
	console.log(err);
	}
	}

	const padSequences = (sequences, metadata) => {
	return sequences.map((seq) => {
	if (seq.length > metadata.max_len) {
	seq.splice(0, seq.length - metadata.max_len);
	}
	if (seq.length < metadata.max_len) {
	const pad = [];
	for (let i = 0; i < metadata.max_len - seq.length; ++i) {
	pad.push(0);
	}
	seq = pad.concat(seq);
	}
	return seq;
	});
	};

	function predict(text, model, metadata) {
	const inputText = text
	.trim()
	.toLowerCase()
	.replace(/(\.\|\,\|!)/g, "")
	.split(" ");
	const sequence = inputText.map((word) => {
	const wordIndex = metadata.word_index[word];
	if (typeof wordIndex === "undefined") {
	return 2; //oov_index
	}

	return wordIndex + metadata.index_from;
	});

	const paddedSequence = padSequences([sequence], metadata);
	const input = tf.tensor2d(paddedSequence, [1, metadata.max_len]);

	const predictOut = model.predict(input);
	const score = predictOut.dataSync()[0];
	predictOut.dispose();
	return score;
	}

	// const model = await loadModel();
	// const metadata = await getMetaData();

	Promise.all([loadModel(urls.model), loadMetadata(urls.metadata)]).then(([model, metadata]) => {
	const result = predict("you are a good person", model, metadata);
	console.log("Result: ", result);
	})

	</script>
	</body>
	</html>