src/lib/components/AutoTrainerScanner/AutoTrainerScanner.svelte

<script lang="ts">
  import type { GradioClient } from '$lib/types';
  import { 
    initializeTrainerScanProgress, 
    getNextPendingImage, 
    markImageProcessingCompleted,
    markImageProcessingFailed,
    getScanningStats
  } from '$lib/db/trainerScanning';
  import PicletGenerator from '$lib/components/PicletGenerator/PicletGenerator.svelte';
  
  interface Props {
    joyCaptionClient: GradioClient;
    fluxClient: GradioClient;
    commandClient: GradioClient;
    
    // Unused clients (kept for future use)
    // hunyuanClient: GradioClient;
    // zephyrClient: GradioClient;
    // qwenClient: GradioClient;
    // dotsClient: GradioClient;
  }
  
  let { joyCaptionClient, fluxClient, commandClient }: Props = $props();
  
  // Scanner state
  let scanState = $state({
    isScanning: false,
    currentImage: null as string | null,
    currentTrainer: null as string | null,
    progress: {
      total: 0,
      completed: 0,
      failed: 0,
      pending: 0
    },
    error: null as string | null
  });
  
  // Promise tracking for current image processing
  let currentImagePromise: {
    resolve: (value: any) => void;
    reject: (error: any) => void;
  } | null = null;
  
  let showDetails = $state(false);
  let isInitializing = $state(false);
  let shouldStop = $state(false);
  
  // Reference to PicletGenerator component
  let picletGenerator: any;
  
  // Initialize trainer paths on component mount
  $effect(() => {
    if (joyCaptionClient && fluxClient && commandClient) {
      loadInitialState();
    }
  });
  
  async function loadInitialState() {
    try {
      isInitializing = true;
      
      // Load trainer image paths and initialize database
      const response = await fetch('/trainer_image_paths.txt');
      if (!response.ok) {
        throw new Error(`Failed to fetch trainer_image_paths.txt: ${response.statusText}`);
      }
      
      const content = await response.text();
      const imagePaths = content.trim().split('\n')
        .map(path => typeof path === 'string' ? path.trim() : '')
        .filter(path => path.length > 0);
      
      console.log(`Loaded ${imagePaths.length} trainer image paths`);
      
      await initializeTrainerScanProgress(imagePaths);
      await updateProgress();
    } catch (error) {
      console.error('Failed to initialize scanner:', error);
      scanState.error = error instanceof Error ? error.message : 'Failed to initialize';
    } finally {
      isInitializing = false;
    }
  }
  
  async function updateProgress() {
    const stats = await getScanningStats();
    scanState.progress = {
      total: stats.total,
      completed: stats.completed,
      failed: stats.failed,
      pending: stats.pending
    };
  }
  
  async function startScanning() {
    if (scanState.isScanning) return;
    
    scanState.isScanning = true;
    scanState.error = null;
    shouldStop = false;
    
    try {
      await processTrainerImages();
    } catch (error) {
      console.error('Scanning error:', error);
      scanState.error = error instanceof Error ? error.message : 'Unknown error';
    } finally {
      scanState.isScanning = false;
      scanState.currentImage = null;
      scanState.currentTrainer = null;
    }
  }
  
  function stopScanning() {
    shouldStop = true;
  }
  
  async function processTrainerImages() {
    while (!shouldStop) {
      const nextImage = await getNextPendingImage();
      
      if (!nextImage) {
        // No more pending images
        break;
      }
      
      scanState.currentImage = nextImage.imagePath;
      scanState.currentTrainer = nextImage.trainerName;
      
      try {
        // Fetch remote image
        const imageFile = await fetchRemoteImage(nextImage.remoteUrl, nextImage.imagePath);
        
        // Create a Promise that will be resolved by the completion callback
        const imageProcessingPromise = new Promise<void>((resolve, reject) => {
          currentImagePromise = { resolve, reject };
        });
        
        // Queue the image in PicletGenerator
        if (picletGenerator) {
          picletGenerator.queueTrainerImage(imageFile, nextImage.imagePath);
        }
        
        // Wait for this image to be processed before continuing
        console.log(`🔧 DEBUG: Waiting for completion of ${nextImage.imagePath}...`);
        await imageProcessingPromise;
        console.log(`✅ DEBUG: Completed processing of ${nextImage.imagePath}, moving to next image`);
        
      } catch (error) {
        console.error(`Failed to process ${nextImage.imagePath}:`, error);
        await markImageProcessingFailed(
          nextImage.imagePath, 
          error instanceof Error ? error.message : 'Unknown error'
        );
        
        // Reject the current promise if it exists
        if (currentImagePromise) {
          currentImagePromise.reject(error);
          currentImagePromise = null;
        }
      }
      
      await updateProgress();
    }
  }
  
  async function fetchRemoteImage(remoteUrl: string, originalPath: string): Promise<File> {
    const response = await fetch(remoteUrl);
    if (!response.ok) {
      throw new Error(`Failed to fetch ${remoteUrl}: ${response.statusText}`);
    }
    
    const blob = await response.blob();
    const fileName = originalPath.split('/').pop() || 'trainer_image.jpg';
    
    return new File([blob], fileName, { type: blob.type });
  }
  
  async function onTrainerImageCompleted(imagePath: string, picletId: number) {
    console.log(`✅ Trainer image completed: ${imagePath} -> Piclet ID: ${picletId}`);
    
    try {
      await markImageProcessingCompleted(imagePath, picletId);
    } catch (error) {
      console.error(`❌ Failed to mark ${imagePath} as completed:`, error);
    }
    
    await updateProgress();
    
    // Resolve the current image processing promise
    if (currentImagePromise) {
      currentImagePromise.resolve(undefined);
      currentImagePromise = null;
    }
  }
  
  async function onTrainerImageFailed(imagePath: string, error: string) {
    console.error(`❌ Trainer image failed: ${imagePath} -> ${error}`);
    await markImageProcessingFailed(imagePath, error);
    await updateProgress();
    
    // Resolve the current image processing promise (failed images should still allow progression)
    if (currentImagePromise) {
      currentImagePromise.resolve(undefined);
      currentImagePromise = null;
    }
  }
  
  function formatImageName(imagePath: string | null): string {
    if (!imagePath) return '';
    const parts = imagePath.split('/');
    return parts[parts.length - 1] || '';
  }
  
  function formatTrainerName(trainerName: string | null): string {
    if (!trainerName) return '';
    // Convert "001_Willow_Snap" to "Willow Snap"
    return trainerName.split('_').slice(1).join(' ');
  }
  
  function getProgressPercent(): number {
    const { total, completed } = scanState.progress;
    return total > 0 ? Math.round((completed / total) * 100) : 0;
  }
</script>

<div class="auto-trainer-scanner">
  <div class="scanner-header">
    <div class="title-section">
      <h3>🤖 Auto Trainer Scanner</h3>
      <button 
        class="details-toggle"
        onclick={() => showDetails = !showDetails}
      >
        {showDetails ? '▼' : '▶'} Details
      </button>
    </div>
    
    {#if scanState.progress.total > 0}
      <div class="progress-summary">
        <div class="progress-bar">
          <div 
            class="progress-fill" 
            style="width: {getProgressPercent()}%"
          ></div>
        </div>
        <span class="progress-text">
          {scanState.progress.completed} / {scanState.progress.total} ({getProgressPercent()}%)
        </span>
      </div>
    {/if}
  </div>

  {#if showDetails}
    <div class="scanner-details">
      {#if isInitializing}
        <div class="status-message">
          <div class="spinner"></div>
          <span>Initializing scanner...</span>
        </div>
      {:else if scanState.isScanning}
        <div class="scanning-status">
          <div class="current-processing">
            <div class="spinner"></div>
            <div class="processing-info">
              <div class="current-trainer">
                Processing: <strong>{formatTrainerName(scanState.currentTrainer)}</strong>
              </div>
              <div class="current-image">
                {formatImageName(scanState.currentImage)}
              </div>
            </div>
          </div>
          
          <button class="stop-button" onclick={stopScanning}>
            ⏹️ Stop Scanning
          </button>
        </div>
      {:else}
        <div class="scanner-controls">
          <button 
            class="start-button"
            onclick={startScanning}
            disabled={scanState.progress.pending === 0}
          >
            ▶️ Start Auto Scan
          </button>
        </div>
      {/if}
      
      {#if scanState.progress.total > 0}
        <div class="progress-details">
          <div class="progress-stats">
            <div class="stat">
              <span class="stat-label">Total:</span>
              <span class="stat-value">{scanState.progress.total}</span>
            </div>
            <div class="stat completed">
              <span class="stat-label">Completed:</span>
              <span class="stat-value">{scanState.progress.completed}</span>
            </div>
            <div class="stat pending">
              <span class="stat-label">Pending:</span>
              <span class="stat-value">{scanState.progress.pending}</span>
            </div>
            {#if scanState.progress.failed > 0}
              <div class="stat failed">
                <span class="stat-label">Failed:</span>
                <span class="stat-value">{scanState.progress.failed}</span>
              </div>
            {/if}
          </div>
        </div>
      {/if}
      
      {#if scanState.error}
        <div class="error-message">
          <strong>Error:</strong> {scanState.error}
        </div>
      {/if}
      
      <div class="scanner-info">
        <p>
          This will automatically process trainer images from the HuggingFace dataset,
          converting them into unique Piclets. The scanner will resume from where it left off
          if interrupted.
        </p>
      </div>
    </div>
  {/if}
</div>

<!-- Hidden PicletGenerator for trainer mode processing -->
<div style="display: none;">
  <PicletGenerator 
    bind:this={picletGenerator}
    {joyCaptionClient}
    {fluxClient}
    {commandClient}
    isTrainerMode={true}
    onTrainerImageCompleted={onTrainerImageCompleted}
    onTrainerImageFailed={onTrainerImageFailed}
  />
</div>

<style>
  .auto-trainer-scanner {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    border-radius: 12px;
    padding: 1rem;
    margin-bottom: 1rem;
    color: white;
    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
  }
  
  .scanner-header {
    display: flex;
    flex-direction: column;
    gap: 0.5rem;
  }
  
  .title-section {
    display: flex;
    align-items: center;
    justify-content: space-between;
  }
  
  .title-section h3 {
    margin: 0;
    font-size: 1.1rem;
  }
  
  .details-toggle {
    background: rgba(255, 255, 255, 0.2);
    border: none;
    color: white;
    padding: 0.3rem 0.6rem;
    border-radius: 6px;
    cursor: pointer;
    font-size: 0.9rem;
    transition: background-color 0.2s;
  }
  
  .details-toggle:hover {
    background: rgba(255, 255, 255, 0.3);
  }
  
  .progress-summary {
    display: flex;
    align-items: center;
    gap: 1rem;
  }
  
  .progress-bar {
    flex: 1;
    height: 8px;
    background: rgba(255, 255, 255, 0.2);
    border-radius: 4px;
    overflow: hidden;
  }
  
  .progress-fill {
    height: 100%;
    background: linear-gradient(90deg, #4facfe 0%, #00f2fe 100%);
    transition: width 0.3s ease;
  }
  
  .progress-text {
    font-size: 0.9rem;
    font-weight: 500;
    white-space: nowrap;
  }
  
  .scanner-details {
    margin-top: 1rem;
    padding-top: 1rem;
    border-top: 1px solid rgba(255, 255, 255, 0.2);
  }
  
  .status-message {
    display: flex;
    align-items: center;
    gap: 0.5rem;
    padding: 0.8rem;
    background: rgba(255, 255, 255, 0.1);
    border-radius: 8px;
    margin-bottom: 1rem;
  }
  
  .scanning-status {
    display: flex;
    flex-direction: column;
    gap: 1rem;
  }
  
  .current-processing {
    display: flex;
    align-items: center;
    gap: 1rem;
    padding: 1rem;
    background: rgba(255, 255, 255, 0.1);
    border-radius: 8px;
  }
  
  .processing-info {
    flex: 1;
  }
  
  .current-trainer {
    font-size: 1rem;
    margin-bottom: 0.3rem;
  }
  
  .current-image {
    font-size: 0.9rem;
    opacity: 0.8;
  }
  
  .scanner-controls {
    display: flex;
    gap: 0.8rem;
    margin-bottom: 1rem;
  }
  
  .start-button, .stop-button {
    padding: 0.8rem 1.2rem;
    border: none;
    border-radius: 8px;
    font-weight: 500;
    cursor: pointer;
    transition: all 0.2s;
  }
  
  .start-button {
    background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%);
    color: white;
  }
  
  .start-button:hover:not(:disabled) {
    transform: translateY(-1px);
    box-shadow: 0 4px 8px rgba(79, 172, 254, 0.3);
  }
  
  .start-button:disabled {
    background: rgba(255, 255, 255, 0.3);
    cursor: not-allowed;
    opacity: 0.6;
  }
  
  .stop-button {
    background: linear-gradient(135deg, #ff6b6b 0%, #ee5a24 100%);
    color: white;
  }
  
  .stop-button:hover {
    transform: translateY(-1px);
    box-shadow: 0 4px 8px rgba(255, 107, 107, 0.3);
  }
  
  
  .progress-details {
    margin-bottom: 1rem;
  }
  
  .progress-stats {
    display: grid;
    grid-template-columns: repeat(auto-fit, minmax(120px, 1fr));
    gap: 0.8rem;
  }
  
  .stat {
    display: flex;
    justify-content: space-between;
    align-items: center;
    padding: 0.6rem;
    background: rgba(255, 255, 255, 0.1);
    border-radius: 6px;
    border-left: 3px solid rgba(255, 255, 255, 0.5);
  }
  
  .stat.completed {
    border-left-color: #4caf50;
  }
  
  .stat.pending {
    border-left-color: #ff9800;
  }
  
  .stat.failed {
    border-left-color: #f44336;
  }
  
  .stat-label {
    font-size: 0.9rem;
    opacity: 0.9;
  }
  
  .stat-value {
    font-weight: 600;
    font-size: 1rem;
  }
  
  .error-message {
    background: rgba(244, 67, 54, 0.2);
    border: 1px solid rgba(244, 67, 54, 0.4);
    border-radius: 8px;
    padding: 0.8rem;
    margin-bottom: 1rem;
    font-size: 0.9rem;
  }
  
  .scanner-info {
    background: rgba(255, 255, 255, 0.1);
    border-radius: 8px;
    padding: 0.8rem;
    font-size: 0.9rem;
    line-height: 1.4;
  }
  
  .scanner-info p {
    margin: 0;
    opacity: 0.9;
  }
  
  .spinner {
    width: 20px;
    height: 20px;
    border: 2px solid rgba(255, 255, 255, 0.3);
    border-top: 2px solid white;
    border-radius: 50%;
    animation: spin 1s linear infinite;
  }
  
  @keyframes spin {
    to { transform: rotate(360deg); }
  }
  
  @media (max-width: 768px) {
    .progress-summary {
      flex-direction: column;
      align-items: stretch;
      gap: 0.5rem;
    }
    
    .current-processing {
      flex-direction: column;
      align-items: flex-start;
      text-align: left;
    }
    
    .scanner-controls {
      flex-direction: column;
    }
    
    .progress-stats {
      grid-template-columns: 1fr;
    }
  }
</style>