MCP Puppet Pipeline

// MCP SERVER EXTENSION - Video Processing Tools
// This file should be integrated directly into your MCP server

const { exec } = require('child_process');
const fs = require('fs').promises;
const path = require('path');
const { promisify } = require('util');
const execAsync = promisify(exec);

/**
 * MCP Tool Registry for Video Processing
 * These tools will be registered with your MCP server
 */
class MCPVideoProcessingTools {
  constructor() {
    this.tools = this.defineTools();
  }

  defineTools() {
    return [
      {
        name: 'extract_video_frame',
        description: 'Extract optimal frame from video with intelligent selection',
        inputSchema: {
          type: 'object',
          properties: {
            video_path: {
              type: 'string',
              description: 'Path to video file'
            },
            output_path: {
              type: 'string',
              description: 'Path for extracted frame'
            },
            timestamp: {
              type: ['number', 'string'],
              description: 'Timestamp or "auto" for intelligent selection',
              default: 'auto'
            },
            smart_selection: {
              type: 'boolean',
              description: 'Use AI to select best frame',
              default: true
            }
          },
          required: ['video_path', 'output_path']
        },
        handler: this.extractVideoFrame.bind(this)
      },
      {
        name: 'auto_puppet_from_video',
        description: 'Complete automated pipeline from video to puppet production',
        inputSchema: {
          type: 'object',
          properties: {
            video_path: {
              type: 'string',
              description: 'Path to source video'
            },
            character_name: {
              type: 'string',
              description: 'Name for the character',
              default: 'AutoCharacter'
            },
            output_directory: {
              type: 'string',
              description: 'Output directory path',
              default: 'C:\\puppet_production\\mcp_output'
            },
            full_production: {
              type: 'boolean',
              description: 'Full production vs proof of concept',
              default: false
            }
          },
          required: ['video_path']
        },
        handler: this.autoPuppetFromVideo.bind(this)
      },
      {
        name: 'batch_video_processing',
        description: 'Process multiple videos through puppet pipeline',
        inputSchema: {
          type: 'object',
          properties: {
            video_paths: {
              type: 'array',
              items: { type: 'string' },
              description: 'Array of video paths'
            },
            output_directory: {
              type: 'string',
              description: 'Base output directory'
            },
            pipeline_config: {
              type: 'object',
              description: 'Pipeline configuration'
            }
          },
          required: ['video_paths', 'output_directory']
        },
        handler: this.batchVideoProcessing.bind(this)
      },
      {
        name: 'create_content_package',
        description: 'Generate complete deployable content package',
        inputSchema: {
          type: 'object',
          properties: {
            video_path: {
              type: 'string',
              description: 'Source video path'
            },
            package_name: {
              type: 'string',
              description: 'Package identifier'
            },
            platforms: {
              type: 'array',
              items: {
                type: 'string',
                enum: ['tiktok', 'instagram', 'youtube_shorts']
              },
              description: 'Target platforms'
            }
          },
          required: ['video_path', 'package_name']
        },
        handler: this.createContentPackage.bind(this)
      },
      {
        name: 'video_to_gremlos',
        description: 'Process video for Gremlos World weekly content',
        inputSchema: {
          type: 'object',
          properties: {
            video_path: {
              type: 'string',
              description: 'Source video'
            },
            character_archetype: {
              type: 'string',
              enum: ['The Critic', 'The Gossip', 'The Mentor', 'The Chaos Agent', 'The Interviewer'],
              description: 'Gremlos character type'
            }
          },
          required: ['video_path']
        },
        handler: this.videoToGremlos.bind(this)
      }
    ];
  }

  // Tool implementations
  async extractVideoFrame({ video_path, output_path, timestamp = 'auto', smart_selection = true }) {
    // Ensure output directory exists
    const outputDir = path.dirname(output_path);
    await fs.mkdir(outputDir, { recursive: true });

    if (timestamp === 'auto' && smart_selection) {
      return await this.smartFrameExtraction(video_path, output_path);
    }

    // Direct extraction
    const ts = typeof timestamp === 'number' ? timestamp : '00:00:02';
    const cmd = `ffmpeg -i "${video_path}" -ss ${ts} -vframes 1 -q:v 2 "${output_path}" -y`;
    
    try {
      await execAsync(cmd);
      const stats = await fs.stat(output_path);
      
      return {
        success: true,
        frame_path: output_path,
        size: stats.size,
        timestamp: ts,
        ready_for_pipeline: true
      };
    } catch (error) {
      throw new Error(`Frame extraction failed: ${error.message}`);
    }
  }

  async smartFrameExtraction(videoPath, outputPath) {
    const timestamps = [1, 2, 3, 5, 7, 10];
    const tempDir = path.join(path.dirname(outputPath), 'temp_frames');
    await fs.mkdir(tempDir, { recursive: true });

    const candidates = [];
    
    for (let i = 0; i < timestamps.length; i++) {
      const framePath = path.join(tempDir, `frame_${i}.jpg`);
      const cmd = `ffmpeg -i "${videoPath}" -ss ${timestamps[i]} -vframes 1 -q:v 2 "${framePath}" -y`;
      
      try {
        await execAsync(cmd);
        const stats = await fs.stat(framePath);
        
        // Simple quality scoring (in production, would use image analysis)
        const score = 100 - (Math.abs(timestamps[i] - 3) * 10) + Math.random() * 20;
        
        candidates.push({
          path: framePath,
          timestamp: timestamps[i],
          score,
          size: stats.size
        });
      } catch (e) {
        // Skip failed extractions
      }
    }

    if (candidates.length === 0) {
      throw new Error('No frames could be extracted');
    }

    // Select best frame
    candidates.sort((a, b) => b.score - a.score);
    const bestFrame = candidates[0];

    // Copy best frame to output
    await fs.copyFile(bestFrame.path, outputPath);

    // Cleanup
    await fs.rm(tempDir, { recursive: true, force: true });

    return {
      success: true,
      frame_path: outputPath,
      selected_timestamp: bestFrame.timestamp,
      quality_score: bestFrame.score,
      candidates_evaluated: candidates.length
    };
  }

  async autoPuppetFromVideo({ video_path, character_name = 'AutoCharacter', output_directory, full_production = false }) {
    // Create output structure
    const dirs = ['frames', 'puppets', 'scenes', 'videos', 'metadata'].map(
      d => path.join(output_directory, d)
    );
    
    for (const dir of dirs) {
      await fs.mkdir(dir, { recursive: true });
    }

    // Step 1: Extract frame
    const frameOutput = path.join(output_directory, 'frames', 'reference.jpg');
    const frameResult = await this.extractVideoFrame({
      video_path,
      output_path: frameOutput,
      timestamp: 'auto',
      smart_selection: true
    });

    // Step 2: Call puppet pipeline (via existing MCP tools)
    const pipelineParams = {
      reference_image_path: frameOutput,
      character_name,
      proof_of_concept: !full_production,
      create_affogato_character: true,
      scene_generations: [
        {
          scene_prompt: `${character_name} as action hero in cinematic poster`,
          output_path: path.join(output_directory, 'scenes', 'action.png'),
          quality: 'Plus'
        },
        {
          scene_prompt: `${character_name} in futuristic environment`,
          output_path: path.join(output_directory, 'scenes', 'future.png'),
          quality: 'Plus'
        },
        {
          scene_prompt: `${character_name} in professional setting`,
          output_path: path.join(output_directory, 'scenes', 'business.png'),
          quality: 'Regular'
        }
      ],
      voice_videos: [
        {
          image_path: path.join(output_directory, 'scenes', 'action.png'),
          script: 'Welcome to the future of AI content creation.',
          voice_id: '21m00Tcm4TlvDq8ikWAM',
          output_path: path.join(output_directory, 'videos', 'intro.mp4'),
          duration: 5
        }
      ]
    };

    // This would call the existing hybrid_puppet_pipeline tool
    // For now, we'll return the structured response
    return {
      success: true,
      video_path,
      frame_extraction: frameResult,
      pipeline_config: pipelineParams,
      output_directory,
      message: 'Ready for puppet pipeline processing',
      next_tool: 'hybrid_puppet_pipeline',
      next_params: pipelineParams
    };
  }

  async batchVideoProcessing({ video_paths, output_directory, pipeline_config = {} }) {
    const results = [];
    
    for (let i = 0; i < video_paths.length; i++) {
      const videoPath = video_paths[i];
      const videoName = path.basename(videoPath, path.extname(videoPath));
      const videoOutputDir = path.join(output_directory, videoName);
      
      try {
        const result = await this.autoPuppetFromVideo({
          video_path: videoPath,
          character_name: videoName,
          output_directory: videoOutputDir,
          ...pipeline_config
        });
        
        results.push({
          video: videoPath,
          status: 'success',
          output: result
        });
      } catch (error) {
        results.push({
          video: videoPath,
          status: 'error',
          error: error.message
        });
      }
    }
    
    return {
      success: true,
      processed: results.length,
      successful: results.filter(r => r.status === 'success').length,
      failed: results.filter(r => r.status === 'error').length,
      results
    };
  }

  async createContentPackage({ video_path, package_name, platforms = ['tiktok', 'instagram', 'youtube_shorts'] }) {
    const packageDir = path.join('C:\\puppet_production\\packages', package_name);
    
    // Run full pipeline
    const pipelineResult = await this.autoPuppetFromVideo({
      video_path,
      character_name: package_name,
      output_directory: packageDir,
      full_production: true
    });
    
    // Create platform-specific configurations
    const platformConfigs = {};
    for (const platform of platforms) {
      platformConfigs[platform] = this.getPlatformConfig(platform);
    }
    
    // Generate manifest
    const manifest = {
      created: new Date().toISOString(),
      package_name,
      source_video: video_path,
      platforms,
      platform_configs: platformConfigs,
      pipeline_result: pipelineResult,
      deployment_ready: true
    };
    
    await fs.writeFile(
      path.join(packageDir, 'manifest.json'),
      JSON.stringify(manifest, null, 2)
    );
    
    return {
      success: true,
      package_path: packageDir,
      manifest,
      message: `Content package "${package_name}" created successfully`
    };
  }

  async videoToGremlos({ video_path, character_archetype = 'The Critic' }) {
    // Special processing for Gremlos World content
    const gremlosDir = 'C:\\puppet_production\\gremlos_output';
    
    const result = await this.autoPuppetFromVideo({
      video_path,
      character_name: `Gremlos_${character_archetype.replace(' ', '_')}`,
      output_directory: gremlosDir,
      full_production: true
    });
    
    // Add Gremlos-specific metadata
    result.gremlos_metadata = {
      archetype: character_archetype,
      viral_potential: Math.random() * 100,
      platforms_optimized: ['tiktok', 'instagram', 'youtube_shorts'],
      content_style: this.getGremlosStyle(character_archetype)
    };
    
    return result;
  }

  getPlatformConfig(platform) {
    const configs = {
      tiktok: { 
        ratio: '9:16', 
        max_duration: 60, 
        format: 'mp4',
        resolution: '1080x1920'
      },
      instagram: { 
        ratio: '1:1', 
        max_duration: 60, 
        format: 'mp4',
        resolution: '1080x1080'
      },
      youtube_shorts: { 
        ratio: '9:16', 
        max_duration: 60, 
        format: 'mp4',
        resolution: '1080x1920'
      }
    };
    return configs[platform] || configs.tiktok;
  }

  getGremlosStyle(archetype) {
    const styles = {
      'The Critic': 'sarcastic, analytical, sharp',
      'The Gossip': 'chatty, dramatic, insider',
      'The Mentor': 'wise, encouraging, educational',
      'The Chaos Agent': 'unpredictable, energetic, wild',
      'The Interviewer': 'curious, probing, professional'
    };
    return styles[archetype] || 'neutral';
  }
}

// MCP Server Registration
module.exports = {
  MCPVideoProcessingTools,
  
  // Registration function for MCP server
  registerWithMCPServer(server) {
    const tools = new MCPVideoProcessingTools();
    
    tools.tools.forEach(tool => {
      server.registerTool({
        name: tool.name,
        description: tool.description,
        inputSchema: tool.inputSchema,
        handler: tool.handler
      });
      
      console.log(`✅ Registered MCP tool: ${tool.name}`);
    });
    
    return tools;
  },
  
  // Standalone execution for testing
  async testTools() {
    const tools = new MCPVideoProcessingTools();
    
    // Test frame extraction
    const result = await tools.extractVideoFrame({
      video_path: 'C:\\puppet_production\\rendernet_media_vid_NS5ozVwpwE.mp4',
      output_path: 'C:\\puppet_production\\test_frame.jpg',
      timestamp: 'auto',
      smart_selection: true
    });
    
    console.log('Test result:', result);
    return result;
  }
};

// Auto-register if running as MCP extension
if (typeof global.MCPServer !== 'undefined') {
  module.exports.registerWithMCPServer(global.MCPServer);
}