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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>Multi-Client Logistic Regression with TensorFlow.js</title>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
    <script type="text/javascript" src="https://www.gstatic.com/charts/loader.js"></script>
    <style>
        body {
            font-family: Arial, sans-serif;
            max-width: 1800px;
            margin: 0 auto;
            padding: 20px;
        }

        button,
        input {
            margin: 20px 0;
            border: 1px solid #ccc;
        }

        #progressBar {
            width: 100%;
            background-color: #f0f0f0;
        }

        #progressBar div {
            height: 30px;
            background-color: #4CAF50;
            text-align: center;
            line-height: 30px;
            color: white;
        }

        #chart_div {
            margin-top: 20px;
            border: 1px solid #ccc;
            padding: 10px;
        }
    </style>
</head>

<body>
    <h1>Multi-Client Logistic Regression</h1>
    <input type="file" id="csvFile" accept=".csv">
    <button onclick="importCSV()">Process Data</button>
    <div id="progressBar">
        <div id="progress"></div>
    </div>
    <div id="chart_div" style="width: 100%; height: 500px;"></div>
    <script>
        let clientData = [];
        let dates = [];
        let numClients = 0;
        google.charts.load('current', { 'packages': ['corechart'] });

        function importCSV() {
            const fileInput = document.getElementById('csvFile');
            const file = fileInput.files[0];
            const reader = new FileReader();

            reader.onload = function (e) {
                const content = e.target.result;
                processCSV(content);
            };
            reader.onerror = function (e) {
                handleError("Error reading file: " + e.target.error);
            };

            reader.readAsText(file);
        }
        function processCSV(content) {
            try {
                const rows = content.split('\n');
                const header = rows[0].split(',');
                numClients = header.length - 1;

                clientData = Array(numClients).fill().map(() => []);

                for (let i = 1; i < rows.length; i++) {
                    const row = rows[i].trim().split(',');
                    if (row.length > 1) {
                        dates.push(new Date(row[0]));
                        for (let j = 0; j < numClients; j++) {
                            clientData[j].push(parseInt(row[j + 1]));
                        }
                    }
                }
                trainModels();
            } catch (error) {
                handleError("Error processing CSV: " + error.message);
            }
        }
        async function trainModels() {
            const progress = document.getElementById('progress');
            const predictions = [];

            for (let i = 0; i < numClients; i++) {
                try {
                    const model = tf.sequential();
                    model.add(tf.layers.dense({ units: 1, inputShape: [1], activation: 'sigmoid' }));
                    model.compile({ optimizer: 'adam', loss: 'binaryCrossentropy', metrics: ['accuracy'] });
                    const xs = tf.tensor2d(dates.map((_, index) => [index]), [dates.length, 1]);
                    const ys = tf.tensor2d(clientData[i], [clientData[i].length, 1]);
                    await model.fit(xs, ys, {
                        epochs: 100,
                        callbacks: {
                            onEpochEnd: (epoch, logs) => {
                                if (epoch % 10 === 0) {
                                    progress.style.width = `${((i * 100 + epoch) / (numClients * 100)) * 100}%`;
                                    progress.textContent = `${Math.round(((i * 100 + epoch) / (numClients * 100)) * 100)}%`;
                                }
                            }
                        }
                    });
                    const nextDay = tf.tensor2d([[dates.length]]);
                    const prediction = model.predict(nextDay);
                    predictions.push(prediction.dataSync()[0]);
                } catch (error) {
                    handleError(`Error training model for Client ${i + 1}: ${error.message}`);
                }
            }
            drawChart(predictions);
        }
        function drawChart(predictions) {
            const data = new google.visualization.DataTable();
            data.addColumn('number', 'Client');
            data.addColumn('number', 'Prediction');

            predictions.forEach((pred, index) => {
                data.addRow([index + 1, pred]);
            });

            const options = {
                title: 'Client Predictions',
                hAxis: { title: 'Clients', minValue: 0, maxValue: numClients },
                vAxis: { title: 'Predictions', minValue: 0, maxValue: 1 },
                legend: 'none'
            };

            const chart = new google.visualization.ScatterChart(document.getElementById('chart_div'));
            chart.draw(data, options);
        }

        function handleError(message) {
            console.error(message);
            alert("An error occurred. Please check the console for details.");
        }
    </script>
</body>

</html>