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Create preguntar-pdf.html
Browse files- preguntar-pdf.html +69 -0
preguntar-pdf.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Ask Questions to the Model</title>
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<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
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</head>
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<body>
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<h1>Ask Questions to the Model</h1>
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<input type="text" id="question" placeholder="Type your question here">
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<button id="askQuestion">Ask</button>
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<pre id="response"></pre>
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<script>
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async function loadModel() {
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try {
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const model = await tf.loadLayersModel('localstorage://pdf-trained-model');
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return model;
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} catch (err) {
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document.getElementById('response').textContent = 'Model not found. Train it first!';
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throw err;
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}
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}
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function tokenizeQuestion(question, tokenizer) {
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const tokens = question.split(/\s+/);
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return tokens.map(token => tokenizer[token] || 0);
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}
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document.getElementById('askQuestion').addEventListener('click', async () => {
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const question = document.getElementById('question').value;
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const responseElement = document.getElementById('response');
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if (!question) {
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responseElement.textContent = 'Please enter a question.';
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return;
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}
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responseElement.textContent = 'Loading model...';
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try {
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const model = await loadModel();
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// Tokenizer setup (replace with actual tokenizer logic from training)
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const tokenizer = { "example": 1, "question": 2 }; // Placeholder for actual tokenizer
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const input = tokenizeQuestion(question, tokenizer);
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if (input.length === 0) {
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responseElement.textContent = 'Error: Question could not be tokenized.';
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return;
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}
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const paddedInput = tf.pad(tf.tensor2d([input], [1, input.length]), [[0, 0], [0, Math.max(0, 10 - input.length)]], 'constant');
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const prediction = model.predict(paddedInput);
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const predictionArray = await prediction.array();
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responseElement.textContent = `Model response: ${predictionArray}`;
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} catch (err) {
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responseElement.textContent = 'Error: Could not load model or process question.';
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console.error(err);
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}
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});
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</script>
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</body>
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</html>
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