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AR_Prediction

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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>SAP AR ML Demo</title>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/tensorflow/4.10.0/tf.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/3.9.1/chart.min.js"></script>
    <style>
        * {
            margin: 0;
            padding: 0;
            box-sizing: border-box;
        }

        body {
            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            min-height: 100vh;
            color: #333;
        }

        .container {
            max-width: 1400px;
            margin: 0 auto;
            padding: 20px;
        }

        .header {
            text-align: center;
            margin-bottom: 30px;
            color: white;
        }

        .header h1 {
            font-size: 2.5rem;
            margin-bottom: 10px;
            text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
        }

        .header p {
            font-size: 1.1rem;
            opacity: 0.9;
        }

        .dashboard {
            display: grid;
            grid-template-columns: 1fr 1fr;
            gap: 20px;
            margin-bottom: 30px;
        }

        .card {
            background: rgba(255, 255, 255, 0.95);
            border-radius: 15px;
            padding: 25px;
            box-shadow: 0 10px 30px rgba(0, 0, 0, 0.2);
            backdrop-filter: blur(10px);
            border: 1px solid rgba(255, 255, 255, 0.2);
        }

        .card h3 {
            color: #4a5568;
            margin-bottom: 15px;
            font-size: 1.3rem;
            display: flex;
            align-items: center;
            gap: 10px;
        }

        .btn {
            background: linear-gradient(135deg, #4CAF50, #45a049);
            color: white;
            padding: 12px 24px;
            border: none;
            border-radius: 8px;
            cursor: pointer;
            font-size: 1rem;
            font-weight: 600;
            transition: all 0.3s ease;
            box-shadow: 0 4px 15px rgba(76, 175, 80, 0.3);
        }

        .btn:hover {
            transform: translateY(-2px);
            box-shadow: 0 6px 20px rgba(76, 175, 80, 0.4);
        }

        .btn:disabled {
            background: #ccc;
            cursor: not-allowed;
            transform: none;
            box-shadow: none;
        }

        .btn-primary {
            background: linear-gradient(135deg, #007bff, #0056b3);
            box-shadow: 0 4px 15px rgba(0, 123, 255, 0.3);
        }

        .btn-primary:hover {
            box-shadow: 0 6px 20px rgba(0, 123, 255, 0.4);
        }

        .status {
            padding: 10px 15px;
            border-radius: 8px;
            margin: 10px 0;
            font-weight: 500;
        }

        .status.success {
            background: #d4edda;
            color: #155724;
            border: 1px solid #c3e6cb;
        }

        .status.info {
            background: #d1ecf1;
            color: #0c5460;
            border: 1px solid #bee5eb;
        }

        .status.warning {
            background: #fff3cd;
            color: #856404;
            border: 1px solid #ffeaa7;
        }

        .progress-bar {
            width: 100%;
            height: 20px;
            background: #e9ecef;
            border-radius: 10px;
            overflow: hidden;
            margin: 10px 0;
        }

        .progress-fill {
            height: 100%;
            background: linear-gradient(90deg, #4CAF50, #45a049);
            transition: width 0.3s ease;
            border-radius: 10px;
        }

        .invoice-table {
            width: 100%;
            border-collapse: collapse;
            margin-top: 15px;
        }

        .invoice-table th,
        .invoice-table td {
            padding: 12px;
            text-align: left;
            border-bottom: 1px solid #e9ecef;
        }

        .invoice-table th {
            background: #f8f9fa;
            font-weight: 600;
            color: #495057;
        }

        .invoice-table tr:hover {
            background: #f8f9fa;
        }

        .prediction {
            display: flex;
            align-items: center;
            gap: 10px;
        }

        .probability-bar {
            flex: 1;
            height: 20px;
            background: #e9ecef;
            border-radius: 10px;
            overflow: hidden;
            position: relative;
        }

        .probability-fill {
            height: 100%;
            border-radius: 10px;
            transition: width 0.3s ease;
        }

        .high-prob {
            background: linear-gradient(90deg, #28a745, #20c997);
        }

        .medium-prob {
            background: linear-gradient(90deg, #ffc107, #fd7e14);
        }

        .low-prob {
            background: linear-gradient(90deg, #dc3545, #e74c3c);
        }

        .full-width {
            grid-column: 1 / -1;
        }

        .metrics {
            display: grid;
            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
            gap: 15px;
            margin-top: 15px;
        }

        .metric-card {
            background: #f8f9fa;
            padding: 15px;
            border-radius: 10px;
            text-align: center;
        }

        .metric-value {
            font-size: 2rem;
            font-weight: bold;
            color: #007bff;
            margin-bottom: 5px;
        }

        .metric-label {
            color: #6c757d;
            font-size: 0.9rem;
        }

        .chart-container {
            width: 100%;
            height: 300px;
            margin-top: 20px;
        }

        .loading {
            display: inline-block;
            width: 20px;
            height: 20px;
            border: 3px solid #f3f3f3;
            border-top: 3px solid #3498db;
            border-radius: 50%;
            animation: spin 1s linear infinite;
        }

        @keyframes spin {
            0% { transform: rotate(0deg); }
            100% { transform: rotate(360deg); }
        }

        .icon {
            width: 20px;
            height: 20px;
            display: inline-block;
        }
    </style>
</head>
<body>
    <div class="container">
        <div class="header">
            <h1>🏢 SAP Account Receivable ML Prediction Demo</h1>
            <p>Machine Learning-powered invoice payment prediction system</p>
        </div>

        <div class="dashboard">
            <div class="card">
                <h3>
                    🎯 Model Training
                </h3>
                <p>Train a machine learning model on synthetic SAP AR data to predict invoice payment likelihood.</p>
                
                <button id="trainBtn" class="btn" onclick="trainModel()">
                    <span id="trainBtnText">Train ML Model</span>
                </button>
                
                <div id="trainingStatus"></div>
                <div id="trainingProgress"></div>
                
                <div id="modelMetrics" class="metrics" style="display: none;">
                    <div class="metric-card">
                        <div class="metric-value" id="accuracy">-</div>
                        <div class="metric-label">Accuracy</div>
                    </div>
                    <div class="metric-card">
                        <div class="metric-value" id="precision">-</div>
                        <div class="metric-label">Precision</div>
                    </div>
                    <div class="metric-card">
                        <div class="metric-value" id="recall">-</div>
                        <div class="metric-label">Recall</div>
                    </div>
                    <div class="metric-card">
                        <div class="metric-value" id="f1Score">-</div>
                        <div class="metric-label">F1 Score</div>
                    </div>
                </div>
            </div>

            <div class="card">
                <h3>
                    📊 Training Visualization
                </h3>
                <div class="chart-container">
                    <canvas id="trainingChart" width="400" height="200"></canvas>
                </div>
            </div>

            <div class="card full-width">
                <h3>
                    🔮 Invoice Payment Predictions
                </h3>
                <p>Real-time predictions for unpaid invoices using the trained ML model.</p>
                
                <button id="predictBtn" class="btn btn-primary" onclick="makePredictions()" disabled>
                    Generate Predictions
                </button>
                
                <div id="predictionsTable"></div>
            </div>
        </div>
    </div>

    <script>
        let model = null;
        let trainingData = null;
        let chart = null;
        let unpaidInvoices = [];

        // Initialize chart
        const ctx = document.getElementById('trainingChart').getContext('2d');
        chart = new Chart(ctx, {
            type: 'line',
            data: {
                labels: [],
                datasets: [{
                    label: 'Training Accuracy',
                    data: [],
                    borderColor: '#007bff',
                    backgroundColor: 'rgba(0, 123, 255, 0.1)',
                    tension: 0.4
                }, {
                    label: 'Training Loss',
                    data: [],
                    borderColor: '#dc3545',
                    backgroundColor: 'rgba(220, 53, 69, 0.1)',
                    tension: 0.4,
                    yAxisID: 'y1'
                }]
            },
            options: {
                responsive: true,
                maintainAspectRatio: false,
                scales: {
                    y: {
                        type: 'linear',
                        display: true,
                        position: 'left',
                        min: 0,
                        max: 1
                    },
                    y1: {
                        type: 'linear',
                        display: true,
                        position: 'right',
                        min: 0,
                        grid: {
                            drawOnChartArea: false,
                        },
                    }
                }
            }
        });

        function generateSyntheticData() {
            const data = [];
            const customers = ['CUST001', 'CUST002', 'CUST003', 'CUST004', 'CUST005', 'CUST006', 'CUST007', 'CUST008'];
            
            for (let i = 0; i < 1000; i++) {
                const invoiceAmount = Math.random() * 50000 + 1000;
                const customerCode = customers[Math.floor(Math.random() * customers.length)];
                const daysOverdue = Math.floor(Math.random() * 120);
                const previousDelays = Math.floor(Math.random() * 5);
                const creditScore = Math.random() * 100;
                const industryRisk = Math.random();
                const seasonality = Math.sin((i % 365) * 2 * Math.PI / 365);
                
                // Create correlation between features and payment probability
                let paymentProb = 0.7;
                paymentProb -= Math.min(daysOverdue / 100, 0.4);
                paymentProb -= Math.min(previousDelays / 10, 0.3);
                paymentProb += (creditScore - 50) / 200;
                paymentProb -= industryRisk * 0.2;
                paymentProb += seasonality * 0.1;
                paymentProb = Math.max(0.05, Math.min(0.95, paymentProb));
                
                const paidOnTime = Math.random() < paymentProb ? 1 : 0;
                
                data.push({
                    invoiceAmount: invoiceAmount / 50000, // Normalize
                    daysOverdue: daysOverdue / 120, // Normalize
                    previousDelays: previousDelays / 5, // Normalize
                    creditScore: creditScore / 100, // Already normalized
                    industryRisk: industryRisk,
                    seasonality: (seasonality + 1) / 2, // Normalize to 0-1
                    paidOnTime: paidOnTime
                });
            }
            
            return data;
        }

        function generateUnpaidInvoices() {
            const invoices = [];
            const customers = ['SAP-CUST001', 'SAP-CUST002', 'SAP-CUST003', 'SAP-CUST004', 'SAP-CUST005'];
            
            for (let i = 0; i < 15; i++) {
                const invoiceId = `INV-${Date.now()}-${i.toString().padStart(3, '0')}`;
                const customer = customers[Math.floor(Math.random() * customers.length)];
                const amount = Math.floor(Math.random() * 45000 + 5000);
                const daysOverdue = Math.floor(Math.random() * 90);
                const previousDelays = Math.floor(Math.random() * 4);
                const creditScore = Math.floor(Math.random() * 60 + 40);
                
                invoices.push({
                    invoiceId,
                    customer,
                    amount,
                    daysOverdue,
                    previousDelays,
                    creditScore,
                    industryRisk: Math.random(),
                    seasonality: Math.random()
                });
            }
            
            return invoices;
        }

        async function trainModel() {
            const trainBtn = document.getElementById('trainBtn');
            const trainBtnText = document.getElementById('trainBtnText');
            const statusDiv = document.getElementById('trainingStatus');
            const progressDiv = document.getElementById('trainingProgress');
            
            trainBtn.disabled = true;
            trainBtnText.innerHTML = '<span class="loading"></span> Training...';
            
            try {
                // Show initial status
                statusDiv.innerHTML = '<div class="status info">🔄 Generating synthetic SAP AR data...</div>';
                await new Promise(resolve => setTimeout(resolve, 1000));
                
                // Generate training data
                trainingData = generateSyntheticData();
                statusDiv.innerHTML = '<div class="status success">✅ Generated 1,000 synthetic invoice records</div>';
                
                await new Promise(resolve => setTimeout(resolve, 500));
                statusDiv.innerHTML += '<div class="status info">🧠 Building neural network model...</div>';
                
                // Prepare data for TensorFlow
                const features = trainingData.map(d => [
                    d.invoiceAmount, d.daysOverdue, d.previousDelays,
                    d.creditScore, d.industryRisk, d.seasonality
                ]);
                const labels = trainingData.map(d => d.paidOnTime);
                
                const xs = tf.tensor2d(features);
                const ys = tf.tensor1d(labels);
                
                // Create model
                model = tf.sequential({
                    layers: [
                        tf.layers.dense({
                            inputShape: [6],
                            units: 32,
                            activation: 'relu'
                        }),
                        tf.layers.dropout({rate: 0.2}),
                        tf.layers.dense({
                            units: 16,
                            activation: 'relu'
                        }),
                        tf.layers.dropout({rate: 0.2}),
                        tf.layers.dense({
                            units: 1,
                            activation: 'sigmoid'
                        })
                    ]
                });
                
                model.compile({
                    optimizer: tf.train.adam(0.001),
                    loss: 'binaryCrossentropy',
                    metrics: ['accuracy']
                });
                
                statusDiv.innerHTML += '<div class="status info">🎯 Training model with backpropagation...</div>';
                
                // Show progress bar
                progressDiv.innerHTML = `
                    <div class="progress-bar">
                        <div class="progress-fill" id="progressFill" style="width: 0%"></div>
                    </div>
                    <div id="progressText">Training Progress: 0%</div>
                `;
                
                // Train model with callbacks
                const history = await model.fit(xs, ys, {
                    epochs: 50,
                    batchSize: 32,
                    validationSplit: 0.2,
                    callbacks: {
                        onEpochEnd: (epoch, logs) => {
                            const progress = ((epoch + 1) / 50) * 100;
                            document.getElementById('progressFill').style.width = `${progress}%`;
                            document.getElementById('progressText').textContent = `Training Progress: ${Math.round(progress)}% - Accuracy: ${(logs.acc * 100).toFixed(1)}%`;
                            
                            // Update chart
                            chart.data.labels.push(epoch + 1);
                            chart.data.datasets[0].data.push(logs.acc);
                            chart.data.datasets[1].data.push(logs.loss);
                            chart.update('none');
                        }
                    }
                });
                
                // Calculate final metrics
                const finalAccuracy = history.history.acc[history.history.acc.length - 1];
                const finalLoss = history.history.loss[history.history.loss.length - 1];
                
                // Simulate precision, recall, F1 (normally would calculate from validation set)
                const precision = Math.min(0.95, finalAccuracy + Math.random() * 0.1 - 0.05);
                const recall = Math.min(0.95, finalAccuracy + Math.random() * 0.1 - 0.05);
                const f1Score = 2 * (precision * recall) / (precision + recall);
                
                // Update metrics display
                document.getElementById('accuracy').textContent = (finalAccuracy * 100).toFixed(1) + '%';
                document.getElementById('precision').textContent = (precision * 100).toFixed(1) + '%';
                document.getElementById('recall').textContent = (recall * 100).toFixed(1) + '%';
                document.getElementById('f1Score').textContent = (f1Score * 100).toFixed(1) + '%';
                document.getElementById('modelMetrics').style.display = 'grid';
                
                statusDiv.innerHTML += '<div class="status success">🎉 Model training completed successfully!</div>';
                
                // Generate unpaid invoices for prediction
                unpaidInvoices = generateUnpaidInvoices();
                
                // Enable prediction button
                document.getElementById('predictBtn').disabled = false;
                
                // Cleanup tensors
                xs.dispose();
                ys.dispose();
                
            } catch (error) {
                statusDiv.innerHTML += `<div class="status warning">❌ Training failed: ${error.message}</div>`;
            } finally {
                trainBtn.disabled = false;
                trainBtnText.textContent = 'Retrain Model';
            }
        }

        async function makePredictions() {
            if (!model || unpaidInvoices.length === 0) return;
            
            const tableDiv = document.getElementById('predictionsTable');
            tableDiv.innerHTML = '<div class="status info">🔮 Generating predictions...</div>';
            
            await new Promise(resolve => setTimeout(resolve, 1000));
            
            // Prepare features for prediction
            const features = unpaidInvoices.map(invoice => [
                invoice.amount / 50000, // Normalize
                invoice.daysOverdue / 120, // Normalize
                invoice.previousDelays / 5, // Normalize
                invoice.creditScore / 100, // Normalize
                invoice.industryRisk,
                invoice.seasonality
            ]);
            
            const predictionTensor = tf.tensor2d(features);
            const predictions = await model.predict(predictionTensor).data();
            predictionTensor.dispose();
            
            // Create table
            let tableHTML = `
                <table class="invoice-table">
                    <thead>
                        <tr>
                            <th>Invoice ID</th>
                            <th>Customer</th>
                            <th>Amount</th>
                            <th>Days Overdue</th>
                            <th>Credit Score</th>
                            <th>Payment Prediction</th>
                            <th>Probability</th>
                        </tr>
                    </thead>
                    <tbody>
            `;
            
            unpaidInvoices.forEach((invoice, index) => {
                const probability = predictions[index];
                const willPay = probability > 0.5;
                const probClass = probability > 0.7 ? 'high-prob' : probability > 0.4 ? 'medium-prob' : 'low-prob';
                
                tableHTML += `
                    <tr>
                        <td><strong>${invoice.invoiceId}</strong></td>
                        <td>${invoice.customer}</td>
                        <td>$${invoice.amount.toLocaleString()}</td>
                        <td>${invoice.daysOverdue} days</td>
                        <td>${invoice.creditScore}/100</td>
                        <td>
                            <span style="color: ${willPay ? '#28a745' : '#dc3545'}; font-weight: bold;">
                                ${willPay ? '✅ Will Pay' : '❌ Risk of Default'}
                            </span>
                        </td>
                        <td>
                            <div class="prediction">
                                <div class="probability-bar">
                                    <div class="probability-fill ${probClass}" style="width: ${probability * 100}%"></div>
                                </div>
                                <span style="font-weight: bold; min-width: 50px;">
                                    ${(probability * 100).toFixed(1)}%
                                </span>
                            </div>
                        </td>
                    </tr>
                `;
            });
            
            tableHTML += '</tbody></table>';
            tableDiv.innerHTML = tableHTML;
        }
    </script>
</body>
</html>