PyroPredictor-AI/pyropredictor.html at main · Sckibble/PyroPredictor-AI · GitHub

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<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Pyro Predictor</title>
    <link href="https://fonts.googleapis.com/css2?family=Playfair+Display:wght@400;700&display=swap" rel="stylesheet">
    <link rel="stylesheet" href="../styles.css"> <!-- Link to your external CSS file -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@4.22.0/dist/tf.min.js"></script>

</head>
<body>
<br>
<br>
<h1 id="pageTitle">
    Pyro Predictor
</h1>
<div class="container">
    <button id="checkDangerLevel" onclick="fetchAndPredictVisualCrossing()">Predict</button>
        <h2>7-Day Fire Danger Forecast</h2>
        <div id="forecast-results">
            <p id="result-day-1">Loading day 1...</p>
            <p id="result-day-2">Loading day 2...</p>
            <p id="result-day-3">Loading day 3...</p>
            <p id="result-day-4">Loading day 4...</p>
            <p id="result-day-5">Loading day 5...</p>
            <p id="result-day-6">Loading day 6...</p>
            <p id="result-day-7">Loading day 7...</p>
        </div>
</div>

        <script>
            let model;
            let scalerStats;
            const labels = ['Low', 'Moderate', 'High', 'Very High', 'Extreme'];

            async function loadEverything() {
                try {
                    console.log("Loading model :)");
                    model = await tf.loadGraphModel('model.json');
                    console.log("Model loaded, loading scaler");
                    const res = await fetch('scaler_stats.json');
                    scalerStats = await res.json();
                    console.log("Model and scaler loaded.");
                } catch (e) {
                    console.error("Failed to load model or scaler:", e);
                }
            }

            function normalizeInput(inputArray, mins, maxs) {
                return inputArray.map((val, i) => {
                    return (val - mins[i]) / (maxs[i] - mins[i]);
                });
            }

            async function predictFireDanger(input) {
                if (!model || !scalerStats) {
                    alert("Model or scaler not loaded yet!");
                    return;
                }

                const normInput = normalizeInput(input, scalerStats.mins, scalerStats.maxs);
                const inputTensor = tf.tensor2d([normInput]); // shape [1, 9]
                const prediction = model.predict(inputTensor);
                const result = prediction.argMax(-1); // get index of highest probability
                const predictedClass = (await result.data())[0]; // value 0-4
                return predictedClass;
            }

            async function fetchAndPredictVisualCrossing() {
                try {
                    const response = await fetch('https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/los%20angeles/next7days?unitGroup=us&include=days&key=3DAZML9PJ9STXC6VMGBYDMDGW&contentType=json');
                    const data = await response.json();

                    const days = data.days.slice(0, 7); // Get next 7 days

                    for (let i = 0; i < days.length; i++) {
                        const day = days[i];

                        const tempmax = day.tempmax || 0;
                        const dew = day.dew || 0;
                        const humidity = day.humidity || 0;
                        const precip = day.precip || 0;
                        const precipcover = day.precipcover || 0;
                        const windgust = day.windgust || 0;
                        const windspeed = day.windspeed || 0;
                        const solarradiation = day.solarradiation || 0;
                        const season = getSeasonFromMonth(new Date(day.datetime).getMonth() + 1);

                        const features = [tempmax, dew, humidity, precip, precipcover, windgust, windspeed, solarradiation, season];
                        console.log(`Day ${i + 1} features:`, features);

                        const predictedClass = await predictFireDanger(features);
                        const label = predictedClass !== undefined ? labels[predictedClass] : "Unavailable";

                        const resultDiv = document.getElementById(`result-day-${i + 1}`);
                        resultDiv.innerText = `${day.datetime}: ${label}`;
                    }

                } catch (err) {
                    console.error('Error fetching weather from Visual Crossing:', err);
                }
            }

            function getSeasonFromMonth(month) {
                // 1: Winter, 2: Spring, 3: Summer, 4: Fall (just like your Python logic)
                return Math.floor(((month % 12) + 3) / 3);
            }

            console.log("About to load model");
            window.addEventListener('load', loadEverything);
            </script>
        <script src="loadHeader.js"></script>
        <script src="animations.js"></script>
    </body>
</html>