543 lines
22 KiB
HTML
543 lines
22 KiB
HTML
<div class="card training-panel">
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<div class="card-header">
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<h6 class="mb-0">
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<i class="fas fa-graduation-cap"></i>
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Training
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</h6>
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</div>
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<div class="card-body p-2">
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<!-- Model Selection -->
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<div class="mb-3">
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<label for="model-select" class="form-label small">Model</label>
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<select class="form-select form-select-sm" id="model-select">
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<option value="">Loading models...</option>
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</select>
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</div>
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<!-- Training Controls -->
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<div class="mb-3">
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<button class="btn btn-primary btn-sm w-100" id="train-model-btn">
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<i class="fas fa-play"></i>
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Train Model
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</button>
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</div>
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<!-- Training Status -->
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<div id="training-status" style="display: none;">
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<div class="alert alert-info py-2 px-2 mb-2">
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<div class="d-flex align-items-center mb-1">
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<div class="spinner-border spinner-border-sm me-2" role="status">
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<span class="visually-hidden">Training...</span>
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</div>
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<strong class="small">Training in progress</strong>
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</div>
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<div class="progress mb-1" style="height: 10px;">
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<div class="progress-bar progress-bar-striped progress-bar-animated" id="training-progress-bar"
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role="progressbar" style="width: 0%"></div>
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</div>
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<div class="small">
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<div>Epoch: <span id="training-epoch">0</span>/<span id="training-total-epochs">0</span></div>
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<div>Loss: <span id="training-loss">--</span></div>
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</div>
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</div>
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</div>
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<!-- Training Results -->
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<div id="training-results" style="display: none;">
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<div class="alert alert-success py-2 px-2 mb-2">
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<strong class="small">
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<i class="fas fa-check-circle"></i>
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Training Complete
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</strong>
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<div class="small mt-1">
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<div>Final Loss: <span id="result-loss">--</span></div>
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<div>Accuracy: <span id="result-accuracy">--</span></div>
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<div>Duration: <span id="result-duration">--</span></div>
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</div>
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</div>
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</div>
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<!-- Real-Time Inference -->
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<div class="mb-3">
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<label class="form-label small">Real-Time Inference</label>
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<button class="btn btn-success btn-sm w-100" id="start-inference-btn">
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<i class="fas fa-play"></i>
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Start Live Inference
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</button>
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<button class="btn btn-danger btn-sm w-100 mt-1" id="stop-inference-btn" style="display: none;">
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<i class="fas fa-stop"></i>
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Stop Inference
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</button>
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</div>
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<!-- Inference Status -->
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<div id="inference-status" style="display: none;">
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<div class="alert alert-success py-2 px-2 mb-2">
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<div class="d-flex align-items-center mb-1">
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<div class="spinner-border spinner-border-sm me-2" role="status">
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<span class="visually-hidden">Running...</span>
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</div>
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<strong class="small">🔴 LIVE</strong>
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</div>
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<div class="small">
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<div>Signal: <span id="latest-signal" class="fw-bold">--</span></div>
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<div>Confidence: <span id="latest-confidence">--</span></div>
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<div class="text-muted" style="font-size: 0.7rem;">Charts updating every 1s</div>
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</div>
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</div>
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</div>
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<!-- Test Case Stats -->
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<div class="small text-muted">
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<div class="d-flex justify-content-between">
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<span>Test Cases:</span>
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<span id="testcase-count">0</span>
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</div>
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<div class="d-flex justify-content-between">
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<span>Last Training:</span>
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<span id="last-training-time">Never</span>
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</div>
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</div>
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</div>
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</div>
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<script>
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// Load available models on page load with polling for async loading
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let modelLoadingPollInterval = null;
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function loadAvailableModels() {
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fetch('/api/available-models')
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.then(response => response.json())
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.then(data => {
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const modelSelect = document.getElementById('model-select');
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if (data.loading) {
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// Models still loading - show loading message and poll
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modelSelect.innerHTML = '<option value="">🔄 Loading models...</option>';
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// Start polling if not already polling
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if (!modelLoadingPollInterval) {
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console.log('Models loading in background, will poll for completion...');
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modelLoadingPollInterval = setInterval(loadAvailableModels, 2000); // Poll every 2 seconds
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}
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} else {
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// Models loaded - stop polling
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if (modelLoadingPollInterval) {
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clearInterval(modelLoadingPollInterval);
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modelLoadingPollInterval = null;
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}
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modelSelect.innerHTML = '';
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if (data.success && data.models.length > 0) {
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// Show success notification
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if (window.showSuccess) {
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window.showSuccess(`✅ ${data.models.length} models loaded and ready for training`);
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}
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data.models.forEach(model => {
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const option = document.createElement('option');
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option.value = model;
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option.textContent = model;
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modelSelect.appendChild(option);
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});
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console.log(`✅ Models loaded: ${data.models.join(', ')}`);
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} else {
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const option = document.createElement('option');
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option.value = '';
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option.textContent = 'No models available';
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modelSelect.appendChild(option);
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}
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}
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})
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.catch(error => {
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console.error('Error loading models:', error);
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const modelSelect = document.getElementById('model-select');
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// Don't stop polling on network errors - keep trying
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if (!modelLoadingPollInterval) {
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modelSelect.innerHTML = '<option value="">⚠️ Connection error, retrying...</option>';
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// Start polling to retry
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modelLoadingPollInterval = setInterval(loadAvailableModels, 3000); // Poll every 3 seconds
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} else {
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// Already polling, just update the message
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modelSelect.innerHTML = '<option value="">🔄 Retrying...</option>';
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}
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});
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}
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// Load models when page loads
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if (document.readyState === 'loading') {
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document.addEventListener('DOMContentLoaded', loadAvailableModels);
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} else {
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loadAvailableModels();
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}
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// Train model button
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document.getElementById('train-model-btn').addEventListener('click', function () {
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const modelName = document.getElementById('model-select').value;
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if (appState.annotations.length === 0) {
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showError('No annotations available for training');
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return;
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}
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// Get annotation IDs
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const annotationIds = appState.annotations.map(a => a.annotation_id);
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// Start training
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startTraining(modelName, annotationIds);
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});
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function startTraining(modelName, annotationIds) {
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// Show training status
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document.getElementById('training-status').style.display = 'block';
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document.getElementById('training-results').style.display = 'none';
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document.getElementById('train-model-btn').disabled = true;
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// Reset progress
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document.getElementById('training-progress-bar').style.width = '0%';
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document.getElementById('training-epoch').textContent = '0';
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document.getElementById('training-loss').textContent = '--';
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// Start training request
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fetch('/api/train-model', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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model_name: modelName,
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annotation_ids: annotationIds
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})
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})
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.then(response => response.json())
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.then(data => {
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if (data.success) {
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// Start polling for training progress
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pollTrainingProgress(data.training_id);
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} else {
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showError('Failed to start training: ' + data.error.message);
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document.getElementById('training-status').style.display = 'none';
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document.getElementById('train-model-btn').disabled = false;
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}
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})
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.catch(error => {
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showError('Network error: ' + error.message);
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document.getElementById('training-status').style.display = 'none';
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document.getElementById('train-model-btn').disabled = false;
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});
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}
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function pollTrainingProgress(trainingId) {
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const pollInterval = setInterval(function () {
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fetch('/api/training-progress', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({ training_id: trainingId })
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})
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.then(response => response.json())
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.then(data => {
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if (data.success) {
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const progress = data.progress;
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// Update progress bar
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const percentage = (progress.current_epoch / progress.total_epochs) * 100;
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document.getElementById('training-progress-bar').style.width = percentage + '%';
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document.getElementById('training-epoch').textContent = progress.current_epoch;
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document.getElementById('training-total-epochs').textContent = progress.total_epochs;
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document.getElementById('training-loss').textContent = progress.current_loss.toFixed(4);
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// Check if complete
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if (progress.status === 'completed') {
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clearInterval(pollInterval);
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showTrainingResults(progress);
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} else if (progress.status === 'failed') {
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clearInterval(pollInterval);
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showError('Training failed: ' + progress.error);
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document.getElementById('training-status').style.display = 'none';
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document.getElementById('train-model-btn').disabled = false;
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}
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}
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})
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.catch(error => {
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clearInterval(pollInterval);
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showError('Failed to get training progress: ' + error.message);
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document.getElementById('training-status').style.display = 'none';
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document.getElementById('train-model-btn').disabled = false;
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});
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}, 1000); // Poll every second
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}
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function showTrainingResults(results) {
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// Hide training status
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document.getElementById('training-status').style.display = 'none';
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// Show results
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document.getElementById('training-results').style.display = 'block';
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document.getElementById('result-loss').textContent = results.final_loss.toFixed(4);
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document.getElementById('result-accuracy').textContent = (results.accuracy * 100).toFixed(2) + '%';
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document.getElementById('result-duration').textContent = results.duration_seconds.toFixed(1) + 's';
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// Update last training time
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document.getElementById('last-training-time').textContent = new Date().toLocaleTimeString();
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// Re-enable train button
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document.getElementById('train-model-btn').disabled = false;
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showSuccess('Training completed successfully');
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}
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// Real-time inference controls
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let currentInferenceId = null;
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let signalPollInterval = null;
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document.getElementById('start-inference-btn').addEventListener('click', function () {
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const modelName = document.getElementById('model-select').value;
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if (!modelName) {
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showError('Please select a model first');
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return;
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}
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// Start real-time inference
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fetch('/api/realtime-inference/start', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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model_name: modelName,
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symbol: appState.currentSymbol
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})
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})
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.then(response => response.json())
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.then(data => {
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if (data.success) {
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currentInferenceId = data.inference_id;
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// Update UI
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document.getElementById('start-inference-btn').style.display = 'none';
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document.getElementById('stop-inference-btn').style.display = 'block';
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document.getElementById('inference-status').style.display = 'block';
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// Show live mode banner
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const banner = document.getElementById('live-mode-banner');
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if (banner) {
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banner.style.display = 'block';
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}
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// Start polling for signals
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startSignalPolling();
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showSuccess('Real-time inference started - Charts now updating live');
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} else {
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showError('Failed to start inference: ' + data.error.message);
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}
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})
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.catch(error => {
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showError('Network error: ' + error.message);
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});
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});
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document.getElementById('stop-inference-btn').addEventListener('click', function () {
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if (!currentInferenceId) return;
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// Stop real-time inference
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fetch('/api/realtime-inference/stop', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({ inference_id: currentInferenceId })
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})
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.then(response => response.json())
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.then(data => {
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if (data.success) {
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// Update UI
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document.getElementById('start-inference-btn').style.display = 'block';
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document.getElementById('stop-inference-btn').style.display = 'none';
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document.getElementById('inference-status').style.display = 'none';
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// Hide live mode banner
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const banner = document.getElementById('live-mode-banner');
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if (banner) {
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banner.style.display = 'none';
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}
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// Stop polling
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stopSignalPolling();
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currentInferenceId = null;
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showSuccess('Real-time inference stopped');
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}
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})
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.catch(error => {
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showError('Network error: ' + error.message);
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});
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});
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function startSignalPolling() {
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signalPollInterval = setInterval(function () {
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// Poll for signals
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fetch('/api/realtime-inference/signals')
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.then(response => response.json())
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.then(data => {
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if (data.success && data.signals.length > 0) {
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const latest = data.signals[0];
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document.getElementById('latest-signal').textContent = latest.action;
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document.getElementById('latest-confidence').textContent =
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(latest.confidence * 100).toFixed(1) + '%';
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// Update chart with signal markers
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if (appState.chartManager) {
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displaySignalOnChart(latest);
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}
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}
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})
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.catch(error => {
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console.error('Error polling signals:', error);
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});
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// Update charts with latest data
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updateChartsWithLiveData();
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}, 1000); // Poll every second
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}
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function updateChartsWithLiveData() {
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// Fetch latest chart data
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fetch('/api/chart-data', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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symbol: appState.currentSymbol,
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timeframes: appState.currentTimeframes,
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start_time: null,
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end_time: null
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})
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})
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.then(response => response.json())
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.then(data => {
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if (data.success && appState.chartManager) {
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// Update each chart with new data
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Object.keys(data.chart_data).forEach(timeframe => {
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const chartData = data.chart_data[timeframe];
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if (appState.chartManager.charts[timeframe]) {
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updateSingleChart(timeframe, chartData);
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}
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});
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}
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})
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.catch(error => {
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console.error('Error updating charts:', error);
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});
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}
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let liveUpdateCount = 0;
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function updateSingleChart(timeframe, newData) {
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const chart = appState.chartManager.charts[timeframe];
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if (!chart) return;
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try {
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// Update candlestick data
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Plotly.update(chart.plotId, {
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x: [newData.timestamps],
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open: [newData.open],
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high: [newData.high],
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low: [newData.low],
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close: [newData.close]
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}, {}, [0]);
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// Update volume data
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const volumeColors = newData.close.map((close, i) => {
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if (i === 0) return '#3b82f6';
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return close >= newData.open[i] ? '#10b981' : '#ef4444';
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});
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Plotly.update(chart.plotId, {
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x: [newData.timestamps],
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y: [newData.volume],
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'marker.color': [volumeColors]
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}, {}, [1]);
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// Update counter
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liveUpdateCount++;
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const counterEl = document.getElementById('live-update-count');
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if (counterEl) {
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counterEl.textContent = liveUpdateCount + ' updates';
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}
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} catch (error) {
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console.error('Error updating chart:', timeframe, error);
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}
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}
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function stopSignalPolling() {
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if (signalPollInterval) {
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clearInterval(signalPollInterval);
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signalPollInterval = null;
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}
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}
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function displaySignalOnChart(signal) {
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// Add signal marker to chart
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if (!appState.chartManager || !appState.chartManager.charts) return;
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// Add marker to all timeframe charts
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Object.keys(appState.chartManager.charts).forEach(timeframe => {
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const chart = appState.chartManager.charts[timeframe];
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if (!chart) return;
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// Get current annotations
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const currentAnnotations = chart.element.layout.annotations || [];
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// Determine marker based on signal
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let markerText = '';
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let markerColor = '#9ca3af';
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if (signal.action === 'BUY') {
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markerText = '🔵 BUY';
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markerColor = '#10b981';
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} else if (signal.action === 'SELL') {
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markerText = '🔴 SELL';
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markerColor = '#ef4444';
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} else {
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return; // Don't show HOLD signals
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}
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// Add new signal marker
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const newAnnotation = {
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x: signal.timestamp,
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y: signal.price,
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text: markerText,
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showarrow: true,
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arrowhead: 2,
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ax: 0,
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ay: -40,
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font: {
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size: 12,
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color: markerColor
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},
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bgcolor: '#1f2937',
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bordercolor: markerColor,
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borderwidth: 2,
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borderpad: 4,
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opacity: 0.8
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};
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// Keep only last 10 signal markers
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const signalAnnotations = currentAnnotations.filter(ann =>
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ann.text && (ann.text.includes('BUY') || ann.text.includes('SELL'))
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).slice(-9);
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// Combine with existing non-signal annotations
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const otherAnnotations = currentAnnotations.filter(ann =>
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!ann.text || (!ann.text.includes('BUY') && !ann.text.includes('SELL'))
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);
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const allAnnotations = [...otherAnnotations, ...signalAnnotations, newAnnotation];
|
|
|
|
// Update chart
|
|
Plotly.relayout(chart.plotId, {
|
|
annotations: allAnnotations
|
|
});
|
|
});
|
|
|
|
console.log('Signal displayed:', signal.action, '@', signal.price);
|
|
}
|
|
</script> |