/**
 * Framework Content Analysis Controller
 * Handles framework-guided blog pre-publication and comment/feedback analysis
 */

const PluralisticDeliberationOrchestrator = require('../services/PluralisticDeliberationOrchestrator.service');
const BoundaryEnforcer = require('../services/BoundaryEnforcer.service');
const logger = require('../utils/logger.util');

/**
 * Analyze blog post before publication
 * Provides framework-guided content review with sensitivity checks, compliance validation,
 * audience analysis, and response templates
 *
 * POST /api/admin/blog/analyze
 * Body: { title, content, category, tags }
 */
exports.analyzeBlogPost = async (req, res) => {
  const { title, content, category, tags } = req.body;

  logger.info('[Framework Content Analysis] Blog post analysis requested', {
    userId: req.user.id,
    title,
    category
  });

  try {
    // Initialize services
    const deliberationOrchestrator = new PluralisticDeliberationOrchestrator();
    const boundaryEnforcer = new BoundaryEnforcer();

    // 1. Sensitivity check - detect values-sensitive topics
    const sensitivityResult = await deliberationOrchestrator.detectValuesSensitivity({
      content: `${title}\n\n${content}`,
      context: { category, tags }
    });

    // 2. Compliance check - ensure framework adherence
    const complianceResult = await boundaryEnforcer.checkCompliance({
      content,
      title,
      type: 'blog_post',
      category
    });

    // 3. Audience analysis - engagement prediction
    const audienceAnalysis = {
      engagement: {
        level: 70, // TODO: Implement ML-based prediction
        description: 'Expected to generate moderate engagement based on topic relevance'
      },
      similarPosts: [] // TODO: Query database for similar posts
    };

    // 4. Publication guidance
    const publicationGuidance = {
      timing: 'Publish during business hours (9am-5pm NZT) for maximum visibility',
      monitoring: 'Monitor comments for first 48 hours post-publication',
      actions: [
        'Share on LinkedIn and Twitter',
        'Enable comments with moderation',
        'Prepare standard responses for anticipated questions'
      ]
    };

    // 5. Generate response templates for anticipated feedback
    const responseTemplates = [
      {
        scenario: 'Request for more technical details',
        response: 'Thank you for your interest. We\'re planning a follow-up article with deeper technical implementation details. Would you like to be notified when it\'s published?'
      },
      {
        scenario: 'Concern about framework overhead',
        response: 'That\'s a valid concern. The framework is designed to be lightweight - most governance checks happen at build/deploy time rather than runtime. Performance overhead is typically <1%.'
      },
      {
        scenario: 'Question about alternative approaches',
        response: 'We\'ve evaluated several alternative approaches. The framework\'s design prioritizes transparency and human oversight. We\'d be happy to discuss specific alternatives you\'re considering.'
      }
    ];

    // 6. Overall recommendation
    let overallRecommendation = {
      decision: 'APPROVE',
      title: 'Ready for Publication',
      message: 'This content meets all framework requirements and is ready for publication.',
      action: 'Proceed to publish when ready'
    };

    // Adjust recommendation based on checks
    if (sensitivityResult.requiresDeliberation || complianceResult.violations?.length > 0) {
      overallRecommendation = {
        decision: 'REVIEW',
        title: 'Review Recommended',
        message: 'This content requires human review before publication.',
        action: 'Address flagged concerns before publishing'
      };
    }

    // Construct analysis response
    const analysis = {
      overall: overallRecommendation,
      sensitivity: {
        status: sensitivityResult.requiresDeliberation ? 'WARN' : 'PASS',
        summary: sensitivityResult.requiresDeliberation
          ? 'Values-sensitive content detected - review recommended'
          : 'No significant values-sensitivity detected',
        details: sensitivityResult.conflicts || [],
        recommendation: sensitivityResult.guidance
      },
      compliance: {
        status: complianceResult.violations?.length > 0 ? 'FAIL' : 'PASS',
        summary: complianceResult.violations?.length > 0
          ? `${complianceResult.violations.length} compliance issue(s) detected`
          : 'Passes all framework compliance checks',
        details: complianceResult.violations || [],
        recommendation: complianceResult.guidance
      },
      audience: audienceAnalysis,
      publication: publicationGuidance,
      responseTemplates
    };

    res.json({
      success: true,
      analysis
    });

  } catch (error) {
    logger.error('[Framework Content Analysis] Blog analysis error', {
      error: error.message,
      stack: error.stack,
      userId: req.user.id
    });

    res.status(500).json({
      success: false,
      error: 'Analysis failed. Please try again.'
    });
  }
};

/**
 * Save blog post as draft
 *
 * POST /api/admin/blog/draft
 * Body: { title, content, category, tags, status: 'draft' }
 */
exports.saveBlogDraft = async (req, res) => {
  const { title, content, category, tags } = req.body;

  logger.info('[Framework Content Analysis] Saving blog draft', {
    userId: req.user.id,
    title
  });

  // TODO: Implement database save logic
  // For now, return success
  res.json({
    success: true,
    message: 'Draft saved successfully',
    draftId: 'draft_' + Date.now()
  });
};

/**
 * Publish blog post
 *
 * POST /api/admin/blog/publish
 * Body: { title, content, category, tags, status: 'published' }
 */
exports.publishBlogPost = async (req, res) => {
  const { title, content, category, tags } = req.body;

  logger.info('[Framework Content Analysis] Publishing blog post', {
    userId: req.user.id,
    title
  });

  // TODO: Implement database save and publication logic
  // For now, return success
  res.json({
    success: true,
    message: 'Post published successfully',
    postId: 'post_' + Date.now()
  });
};

/**
 * Analyze comment/feedback with framework guidance
 * Provides sentiment analysis, values alignment check, risk assessment,
 * and recommended responses
 *
 * POST /api/admin/feedback/analyze
 * Body: { source, relatedPost, content, notes }
 */
exports.analyzeFeedback = async (req, res) => {
  const { source, relatedPost, content, notes } = req.body;

  logger.info('[Framework Content Analysis] Feedback analysis requested', {
    userId: req.user.id,
    source,
    contentLength: content.length
  });

  try {
    // Initialize services
    const deliberationOrchestrator = new PluralisticDeliberationOrchestrator();
    const boundaryEnforcer = new BoundaryEnforcer();

    // 1. Sentiment analysis
    const sentimentAnalysis = analyzeSentiment(content);

    // 2. Values alignment check
    const valuesResult = await deliberationOrchestrator.detectValuesSensitivity({
      content,
      context: { source, relatedPost, notes }
    });

    // 3. Risk assessment
    const riskAssessment = await boundaryEnforcer.assessRisk({
      content,
      source,
      type: 'public_feedback'
    });

    // 4. Generate recommended responses
    const responses = generateRecommendedResponses(sentimentAnalysis, valuesResult);

    // 5. Framework guidance on whether to respond
    const guidance = {
      shouldRespond: shouldRespondToFeedback(sentimentAnalysis, valuesResult, riskAssessment),
      keyConsiderations: [
        'Response should align with Tractatus values',
        'Avoid defensive or dismissive language',
        'Acknowledge valid concerns genuinely',
        'Clarify misunderstandings with patience'
      ],
      tone: sentimentAnalysis.overall === 'negative'
        ? 'Empathetic and understanding, addressing concerns directly'
        : 'Appreciative and informative, building on positive feedback'
    };

    const analysis = {
      sentiment: sentimentAnalysis,
      values: {
        alignedWith: valuesResult.alignedValues || [],
        concernsRaised: valuesResult.concerns || [],
        misunderstandings: valuesResult.misunderstandings || []
      },
      risk: riskAssessment,
      responses,
      guidance
    };

    res.json({
      success: true,
      analysis
    });

  } catch (error) {
    logger.error('[Framework Content Analysis] Feedback analysis error', {
      error: error.message,
      stack: error.stack,
      userId: req.user.id
    });

    res.status(500).json({
      success: false,
      error: 'Analysis failed. Please try again.'
    });
  }
};

/**
 * Save feedback analysis
 *
 * POST /api/admin/feedback/save
 * Body: { source, relatedPost, content, notes }
 */
exports.saveFeedbackAnalysis = async (req, res) => {
  const { source, relatedPost, content, notes } = req.body;

  logger.info('[Framework Content Analysis] Saving feedback analysis', {
    userId: req.user.id,
    source
  });

  // TODO: Implement database save logic
  res.json({
    success: true,
    message: 'Feedback analysis saved successfully',
    analysisId: 'feedback_' + Date.now()
  });
};

/**
 * Export feedback analysis report
 *
 * POST /api/admin/feedback/export
 * Body: { source, relatedPost, content }
 */
exports.exportFeedbackReport = async (req, res) => {
  const { source, relatedPost, content } = req.body;

  logger.info('[Framework Content Analysis] Exporting feedback report', {
    userId: req.user.id,
    source
  });

  // TODO: Implement PDF export using Puppeteer
  // For now, return placeholder response
  res.status(501).json({
    success: false,
    error: 'Export functionality coming soon'
  });
};

// ============================================================
// HELPER FUNCTIONS
// ============================================================

/**
 * Analyze sentiment of text content
 * Basic implementation - could be enhanced with ML
 */
function analyzeSentiment(content) {
  const lowerContent = content.toLowerCase();

  // Positive indicators
  const positiveWords = ['great', 'excellent', 'love', 'appreciate', 'thank', 'helpful', 'useful', 'good'];
  const positiveCount = positiveWords.filter(word => lowerContent.includes(word)).length;

  // Negative indicators
  const negativeWords = ['bad', 'terrible', 'hate', 'disappointed', 'concerned', 'wrong', 'problem', 'issue'];
  const negativeCount = negativeWords.filter(word => lowerContent.includes(word)).length;

  // Question indicators
  const questionWords = ['how', 'what', 'why', 'when', 'where', '?'];
  const questionCount = questionWords.filter(word => lowerContent.includes(word)).length;

  // Determine overall sentiment
  let overall = 'neutral';
  if (positiveCount > negativeCount + 1) overall = 'positive';
  else if (negativeCount > positiveCount + 1) overall = 'negative';
  else if (positiveCount > 0 && negativeCount > 0) overall = 'mixed';

  // Extract key phrases (simple implementation)
  const keyPhrases = [];
  if (lowerContent.includes('framework')) keyPhrases.push('framework discussion');
  if (lowerContent.includes('implementation')) keyPhrases.push('implementation questions');
  if (lowerContent.includes('concern')) keyPhrases.push('concerns raised');

  return {
    overall,
    confidence: Math.min(95, 60 + (Math.abs(positiveCount - negativeCount) * 10)),
    summary: overall === 'positive'
      ? 'Feedback is generally positive and constructive'
      : overall === 'negative'
        ? 'Feedback raises concerns or criticism'
        : overall === 'mixed'
          ? 'Feedback includes both positive and critical elements'
          : 'Neutral tone, primarily informational or questioning',
    keyPhrases
  };
}

/**
 * Generate recommended responses based on analysis
 */
function generateRecommendedResponses(sentiment, valuesResult) {
  const responses = [];

  if (sentiment.overall === 'positive') {
    responses.push({
      approach: 'Appreciative acknowledgment',
      priority: 'medium',
      text: 'Thank you for your thoughtful feedback. We\'re glad the framework resonates with your values and approach to AI governance.',
      rationale: 'Reinforces positive engagement'
    });
  }

  if (sentiment.overall === 'negative') {
    responses.push({
      approach: 'Empathetic concern acknowledgment',
      priority: 'high',
      text: 'Thank you for sharing your concerns. We take this feedback seriously and want to understand your perspective better. Could you elaborate on [specific concern]?',
      rationale: 'Demonstrates genuine listening and openness'
    });
  }

  if (valuesResult.misunderstandings?.length > 0) {
    responses.push({
      approach: 'Clarifying misunderstanding',
      priority: 'high',
      text: 'I appreciate you raising this point - it highlights an area where our communication could be clearer. What we mean by [concept] is [clarification].',
      rationale: 'Corrects misunderstanding without being condescending'
    });
  }

  responses.push({
    approach: 'Invitation to continued dialogue',
    priority: 'low',
    text: 'We value ongoing discussion about these important topics. If you\'d like to explore this further, feel free to [suggest next step].',
    rationale: 'Maintains open communication channel'
  });

  return responses;
}

/**
 * Determine if feedback warrants a response
 */
function shouldRespondToFeedback(sentiment, valuesResult, riskAssessment) {
  // Always respond to high-risk feedback
  if (riskAssessment.level === 'high') return true;

  // Respond to values-misalignment concerns
  if (valuesResult.concerns?.length > 0) return true;

  // Respond to negative feedback
  if (sentiment.overall === 'negative') return true;

  // Respond to positive feedback (builds community)
  if (sentiment.overall === 'positive') return true;

  // Skip neutral/low-engagement comments
  return false;
}